Remote Minehunting

Office of Naval Research

Three/1997

Vol XLIX

Three/1997 1

Naval Research Reviews publishes articles about research conducted by the laboratories and contractors of the

Office of Naval Research and describes important naval experimental activities. Manuscripts submitted for

publication, correspondence concerning prospective articles, and changes of address, should be directed to Code

00PA, Office Naval Research, Arlington, VA 22217-5660. Requests for subscriptions should be directed to the

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Reviews is published from appropriated funds by authority of the Office of Naval Research in accordance with

Navy Publications and Printing Regulations. NAVSOP-35.

CHIEF OF NAVAL RESEARCH

RADM Paul G. Gaffney, II, USN

DEPUTY CHIEF OF NAVAL RESEARCH

TECHNICAL DIRECTOR

Dr. Fred Saalfeld

CHIEF WRITER/EDITOR

William J. Lescure

SCIENTIFIC EDITOR

Douglas Todoroff

MANAGING EDITOR

Norma Gerbozy

ART DIRECTION

Cynthia Nishikawa

Jorge Scientific Corporation

Office of Naval Research

Three/1997

Vol XLIX

46

Profiles in Science

About the cover

The Dolphin Towed Advanced Sensor was used to detect, classify, and identify mine threats from

deep to shallow water. Camera good-luck caught a real dolphin near its mechanical namesake. The

sensor is a semi-autonomous vehicle, towed by a remotely controlled semi-submersible. It is a

prototype and this experiment was proof of the concept only, not of the hardware.

Articles

21

Underwater ElectroOptical System for Mine

Identification

Michael P. Strand

29

Advances in the

Magnetic Detection and

Classification

of Sea Mines and

Unexploded Ordnance

Ted R. Clem

Departments

2

Foreword

Douglas Todoroff, Guest Editor

3

High Frequency/Low

Frequency Synthetic

Aperture Sonar

Gary S. Sammelmann, Jose E.

Fernandez, James T. Christoff, Leonid

Vaizer, John D. Lathrop, Robert W. Sheriff

& Thomas C. Montgomery

9

Automated Detection/

Classification of Sea

Mines in Sonar Imagery

Gerald J. Dobeck, John C. Hyland

& Le’Derick Smedley

47

Research Notes
2 Naval Research Reviews

physical aperture to provide high resolution imagery. In this

article, a description of a novel motion compensation technique is presented which is critical to the application of synthetic aperture sonar technology for minehunting.

In the second article, Dobeck, et.al. describe an approach to automated detection and classification of sea mines

in sonar imagery which has performance similar to that of

an expert sonar operator. Their approach utilizes an improved

detection density algorithm, a feature extraction technique

that uses a stepwise feature selection strategy, and two independent classifiers. In this article, the authors demonstrate

significant improvement in overall classification by

“ANDING” classifiers which use complementary statistical,

mathematical, and geometrical constructs to describe class

boundaries in feature space.

The third article address the identification of proud,

partially buried, and moored mines in shallow and very shallow water. Mine identification is a critical phase of

minehunting in which mines are discriminated from mine

like contacts prior to neutralization. In this article, Mike

Strand describes a comparison of laser range-gated and laser line scan technologies for mine identification.

The final article describes recent advances in the magnetic detection and classification of sea mines and of

unexploded ordnance. In this article, Ted Clem describes

the development of a 5-channel tensor magnetic gradiometer that provides significant improvements in classification and localization over that offered by the commonly used

single-channel total field magnetometer. While the current

configuration of this sensor features bulk niobium low critical temperature superconducting components, recent advances in material research will allow development of a high

temperature superconducting gradiometer concept using liquid nitrogen refrigerant to reduce package size and cryogenic

support requirements.

The technologies described in this issue of Naval Research Reviews represent significant advances in the detection, classification, and identification of sea mines. The synthetic aperture sonar, automated processing, and electro-optic identification technologies will be demonstrated to the

operational user as part of the Joint Countermine Advanced

Concept Technology Demonstration (ACTD) in fiscal year

1998.

Foreword

Douglas Todoroff, Guest Editor

Office of Naval Research

In the post-Cold War environment, naval warfare priorities have shifted from open/blue-water-basin operations

to regional, shallow water Third World conflicts as the major technology challenges facing naval operations in the 21st

century. This increased importance of responding to littoral

conflicts has highlighted the demanding requirements for

developing effective sea-mine countermeasures in shallow

and very shallow water. Thus, it is appropriate at this time

that the Naval Research Reviews should devote a theme issue to the critical problem of countering the mine threat. In

this issue, we focus on recent technology developments in

the area of mine detection, classification and identification.

The recent interest in mine countermeasures is a result

of the proliferation of mines and the threat they pose to naval operations. Mines can be easily obtained and used by

any nation. Sophisticated mines can now be purchased commercially, and crude copies of sophisticated mines can be

manufactured in-house, even by those nations with limited

industrial capabilities. As a result, mines have become a

weapon of choice for Third World countries. Mines have

been used repeatedly in the last quarter century to threaten

commercial shipping, to cover aggressive actions, and to attack naval vessels. In recent years, USS Roberts, USS Tripoli, and USS Princeton have all been victims of naval mining.

The diversity of mines available to regional powers

makes shallow water mine countermeasures perhaps the most

technology challenging of all naval mission requirements.

In deep water, tethered mines constitute the main threat to

naval operations. In shallow water , however, the mine threat

consists of bottom, volume, and floating mines. Here shallow water refers to depths less than 100 meters. A very shallow water regime has been defined as the coastal region with

water depths from 11 to 3 meters. In this regime, the threat

consists primarily of bottom-influence sea mines, smaller

anti-invasion mines, moored mines, and floating mines.

Additionally, the likelihood of mine burial increases as the

water depth decreases.

As a result, there is no single, simple solution to the

mine detection problem. In particular, it is generally recognized that pure acoustic approaches to the detection of underwater sea mines cannot be as effective as multidisiplinary

techniques. In this issue, we include recent mine detection

technology advances in both acoustics and non-acoustics.

The first article is by researchers at Coastal Systems

Station (NSWC) and Northrup Grumman regarding recent

developments in synthetic aperture sonar for mine detection

and classification. Synthetic aperture sonar, like synthetic

aperture radar, involves the processing of data from a small
Three/1997 3

1. Introduction

The HF/LF SAS ( high frequency / low frequency synthetic aperture sonar ) is a high resolution synthetic aperture

sonar developed by COASTSYSTA and Northrop Grumman

to detect, classify, and identify mine-like targets in the shallow water (SW) and very shallow water (VSW) regimes.

The LFSAS is expected to be able to detect and classify volume, proud, and buried mines with its 3”x3” resolution. The

HFSAS with its 1”x1” resolution is expected to have an identification capability approaching that of some optical systems.

The HF/LF SAS was delivered to COASTSYSTA in

July of 1996, and it is currently undergoing field testing. This

article describes the design and the type of performance exAbstract

The HF/LF SAS (high frequency/low frequency SAS)

is a high resolution SAS developed by COASTSYSTA and

Northrop Grumman for the shallow water (SW) and very

shallow water (VSW) regimes. This sonar suite has recently

been delivered to COASTSYSTA and it is currently undergoing field testing. This article describes this sonar and the

type of resolution and acoustical images which are expected

from this sonar. The application of this sonar to the SW and

VSW regimes required the development of a novel method

of motion compensation. A description of this method and

the type of accuracy’s expected from this technique are presented. Finally, a look at future broad band systems and their

predicted performance is presented.

High Frequency/Low

Frequency Synthetic

Aperture Sonar

Gary S. Sammelmann, Jose E. Fernandez, James T. Christoff, Leonid Vaizer, & John D. Lathrop

Coastal Systems Station, Dahlgren Division, Naval Surface Warfare Center, Panama City FL

Robert W. Sheriff & Thomas C. Montgomery

Northrop Grumman, Oceanic Division, Annapolis MD
4 Naval Research Reviews

pected from the HF/LF SAS. This article also describes the

novel motion compensation technique developed by

COASTSYSTA and Northrop Grumman for this sensor.

Section 2 of this article describes the design of the HF/

LF SAS, and Section 3 describes the motion compensation

algorithms. Section 4 describes the current performance of

the HF/LF SAS.

2. Hardware Design

The main design objective of HF/LF SAS was to design a small, light weight, high resolution acoustic sensor

for operation in the SW and VSW regimes which was compatible with operation in a UUV (unmanned underwater vehicle). Compatibility requirements with MK 48 based UUVs

constrained the dimensions of this sensor to a cylindrical

body 21 inches in diameter in a section no longer than 36

inches. The requirement that the sensor be able to detect,

classify, and identify mine-like targets and fit within the above

section can only be satisfied by a SAS. The requirement that

it be able to detect and classify buried targets, and identify

volume and proud targets suggested a dual frequency approach consisting of a low frequency SAS similar to

MADOM to detect and classify buried targets, and a high

frequency SAS for identification of volume and proud targets. The horizontal and vertical beam widths of the sensor

and the depression angle of the sensor have been optimized

to produce the greatest signal-to-noise ratio (SNR) and coverage in the shallow and very shallow water environment

using PC SWAT. The length of the receiver was chosen to

attain the desired maximum range at a given platform velocity.

Past experience with MADOM showed that a LFSAS

with a central frequency of 20 kHz and band width of 10

kHz was capable of detecting and classifying buried targets.

As a proof of concept, the original MADOM configuration

was modified to have a central frequency of 20 kHz with a

10 kHz band width and 6 inch elements. This configuration

gave MADOM the capability of achieving 3”x3” resolution.

In December 1992, a trial sea test of this configuration demonstrated the concept. The concept was further demonstrated

in September 1995 at the MUDSS (Mobile Underwater Survey System) Feasibility Demonstration, where this modified

version of MADOM successfully detected and classified targets ranging in size from a 60 millimeter howitzer shell to a

2000 lb. bomb. During this demonstration, the HPSS ( High

Performance Sidescan Sonar ) was employed in a SAS configuration as a HFSAS. However, due to problems with the

filters, this sonar was only able to achieve 5 cm x 15 cm

resolution.

The major obstacle inhibiting performance of the LF/

HF SAS was the motion of the sensor away from a straight

line trajectory. The method of overcoming this problem was

to develop an adaptive motion compensation technique which

determines the ping to ping motion of the vehicle based on

Figure 1

Photograph of the completed system.
Three/1997 5

DAS Heat Sink

AOA MR/H

 Vehicle

Vehicle 

 Shell

Preamplifiers

 LF/HF

Hydrophones

LF Elements

HF Elements

EMI Shield

 HF

Projector

 LF

Projector

Heat Sink

Power Conversion

 Assembly

• +5V Supply

• 15V Supplies

• EMI Filter

Power Transmit

 Assembly

• 200 Volt

• Transmitters

• Status

• Matching Networks

 Data Acquisition

System VME Chassis

• System Computer

• Timing and Control

• 1553

• Lower E/O

• Data Mux

• Analog/Digital Converter

• Receivers

+_

the correlation between elements of neighboring pings. The

method chosen for this motion compensation was a technique whereby the last 2 phase centers of the array overlap

with the first 2 phase centers of the next ping. The time delay estimates between these overlapping pairs of phase centers allows one to determine the translation, pitch, and yaw

motion of the array between adjacent pings. For this purpose, the array was designed with an extra pair of elements

in order to facilitate the necessary 2 phase center overlap

between adjacent pings and reduce the magnitude of the grating lobes of the array.

To facilitate the motion compensation estimates of the

array, a Motion Measurement Package (MMP) consisting of

a Doppler sonar, and an Inertial Navigation System (INS)

was included. The INS allows one to determine an approximate value of the motion of the sensor. However, the accuracy of INS is insufficient to achieve the desired resolution

of the sensor. Hence, the need for an adaptive motion compensation technique, which has the potential of estimating

the motion of the vehicle to within a thousandth of a wavelength, is necessary. However, the INS provides the operator

with a baseline to compare the estimated motion with the

motion measured by the INS. The Doppler sonar provides

accurate velocity measurements. By slaving the ping repetition rate of the projector to the Doppler unit, one can reduce

the effects of time varying velocity of the array along its

trajectory, by requiring that the projector fire after the array

has moved a predefined distance along track.

The projector of the LFSAS is a tonpilz design. The

vertical height of the projector can be configured as either a

one or two wavelength high projector by changing the jumper

leads on the matching network assembly. The projector can

produce a high fidelity 2 cycle CW pulse with a 10 kHz band

width centered at 20 kHz.

The projector of the HFSAS is a standard half wavelength ceramic bar design. The projector consists of 10 elements in a linear array, which allows one to vary the center

of the projector at will. The projector can produce a 6 cycle

CW pulse with a 30 kHz bandwidth centered at 180 kHz.

The receive elements of the LFSAS and HFSAS have

been designed using a PiezoRubber (PZR) as the active material. The LFSAS contains 14 elements with an element

spacing of 1.5 inches. Only 12 of these 14 elements are used

to form the synthetic beam. The remaining 2 are used to

achieve the desired phase center overlap between adjacent

pings. The HFSAS contains 11 elements with an element

spacing of 2 inches. Only 9 of these 11 elements is used to

form the synthetic beam. The remaining 2 are used to obtain

the desired phase center overlap.

The number of elements in the LFSAS and HFSAS is

determined by the maximum range (40 meters) of the sensor, and the maximum platform velocity (8 knots). The distance traveled between pings is 0.2286 meters or 9 inches.

The LFSAS forms 3 beams with a spacing of 3 inches per

ping, and the HFSAS forms 9 beams with a spacing of 1

inch per ping.

Figure 2

Exploded view of the 21 inch section.
6 Naval Research Reviews

Point Scatterer Response

No Motion

Pixel Intensity (db) - No Compensation

Pixel Intensity (db) - With MC

Pixel Intensity (db) - With MC and Image Rotatio

Element

Element

Element

A study of the predicted signal-to-noise ratio of the

LFSAS and HFSAS as a function of depression angle showed

that the signal to noise ratio for a proud target was maximized and the blind spot under the vehicle was minimized

for a depression angle of 10 degrees.

Figure 1 shows a photograph of the completed system. Figure 2 shows an exploded view of the 21 inch section.

3. Motion Compensation

The method of motion compensation developed by

Northrop Grumman and COASTSYSTA is an adaptive approach which utilizes the cross-correlation between elements

of adjacent pings to estimate the time delay. This approach

requires that the phase centers of the last 2 elements of a

ping and the first 2 elements of the next ping overlap. Two

overlapping elements are required to determine the ping-toping time delay and its rate of change.

The concept of using overlapped displaced phase centers (DPC) was first disclosed by R.S. Raven [1]. Subsequent work at Northrop Grumman and COASTSYSTA [2, 3]

has demonstrated the ability of this technique to compensate

for ping-to-ping translations and rotations.

The central idea to this motion compensation technique

is the assumption that the signal from a pair of overlapping

phase centers should be nearly identical, except for small

deviations due to the motion of the platform. This assumption is based upon the notion that both phase centers are illuminating approximately the same footprint on the bottom,

and have approximately the same travel time to any point on

the bottom. However, contamination of the signal due to

multipathing, and surface reverberation in particular, at the

larger ranges tends to decrease the correlation between the

two signals, since multipathing adds a time varying signal to

the signal at the phase centers. Thus, one must compensate

for the effect of multipathing at the larger ranges.

The second key assumption to this motion compensation technique is that the signals from the overlapping phase

Figure 3

Point response function of the HFSAS with no motion in the

three cases: no motion compensation; one element overlap;

and two element overlap.

Element

Element

Element

Point Scatterer Response

10 Deg Crab

Pixel Intensity (db) - No Compensation

Pixel Intensity (db) - With MC

Pixel Intensity (db) - With MC and Image Rotatio

Figure 4

Point response function of the HFSAS with with a constant

10 degree crab in the three cases: no motion compensation;

one element overlap; and two element overlap.
Three/1997 7

Figure 5 shows the point response function in the case

of a sinusoidal yaw. The case with no motion compensation

is greatly distorted by the yaw. The case with a one element

overlap is somewhat distorted due to the time varying nature

of the yaw.

The case with a two element overlap is similar to the

case with no motion indicating that the motion compensation technique with a two element overlap has correctly removed the distortion to the image due to the time varying

yaw.

4. Performance

Figure 6 depicts an image of the target field during the

sea tests of the HFSAS in the shallow water field.

The picture on the inside front cover of this issue depicts an image of the target field using the HFSAS. The

image of 2 spheres and the resolution panel are clearly in

evidence in this figure. The image of each sphere consists

of 2 returns. The specular return, and a second return which

may be an elastic return due to penetration of the fluid filled

spheres. The legs of the resolution panel are clearly in evidence in this image, as are the doughnut shaped protrusions

lying on the resolution panel.

5. Conclusions

The recent completion of the HFSAS and LFSAS and

the September 1996 sea test of this system offers new capabilities in mine detection, classification, and identification

in shallow and very shallow water. The motion compensation technique described in this article holds the potential of

predicting the time varying motion of the HFSAS and LFSAS

Point Scatterer Response

10 deg p-p Sinusoidal Yaw

Pixel Intensity (db) - No Compensation

Pixel Intensity (db) - With MC

Pixel Intensity (db) - With MC and Image Rotatio

Element

Element

Element

Figure 5

Point response function in the case of a sinusoidal yaw.

centers have a high correlation, and one can estimate the

time delay between the two signals due to the motion of the

platform by estimating the time delay in the cross correlation between these two signals. By using two overlapping

phase centers, one can estimate the time delay and the rate

of change of the time delay between two adjacent pings due

to the motion of the platform.

Figure 3 shows the point response function of the

HFSAS with no motion in the three cases: no motion compensation; one element overlap; and two element overlap. In

all three cases, the point response function is close to its theoretical value.

Figure 4 shows the point response function with a constant 10 degree crab in the three cases: no motion compensation; one element overlap; and two element overlap. The

case with no motion compensation is greatly distorted by

the motion, whereas the two cases with motion compensation are similar to those without motion, indicating the motion compensation technique has properly removed the distortion of the synthetic beam pattern caused by the constant

crab. This case shows that the case of one element overlap is

sufficient to remove static time delays due to motion.

Figure 6

Image of the target field using the HFSAS.

Resolution Panel

20cm Sphere

15cm Sphere
8 Naval Research Reviews

to a high degree of precision ( a thousandth of a wavelength).

During the next few months further refinements of this motion compensation technique will be worked out to refine

the process and improve the quality of the HFSAS and

LFSAS images.

Acknowledgments

The authors would like to acknowledge the support of

Code 321 of the Office of Naval Research, with a special

thanks to Dr. R. Jacobson, and Dr. W. Ching.

Biographies

Since 1984 Gary S. Sammelmann has been engaged

in both theoretical and applied acoustics at the Coastal Systems Station. He is the coauthor of an exact formulation of

scattering from large aspect ratio targets in a waveguide, and

he has written a number of fundamental articles on scattering from shells and large aspect ratio targets. He is also the

sole developer of the computer simulations PC SWAT and

Imaging SWAT.

Jose E. Fernandez has been working at the Coastal

Systems Station since 1984, where he has been involved in

the design, testing and data analysis of several sonar systems. He is currently working in the area of synthetic aperture sonar. His research interests include self-cohering of

arrays, adaptive array systems, synthetic aperture and modern beamforming and spectral estimation techniques and their

application to sonar systems.

Leonid Vaizer became an electronics engineer at the

Coastal Systems Station in 1984. There he had worked in

the areas of parameter estimation, design and analysis of the

navigation algorithms, target motion analysis, target localization algorithms from the magnetic data. Recently Mr.

Vaizer has been working on the motion compensation and

beamforming algorithms for the Synthetic Aperture Sonar.

John D. Lathrop is currently senior scientist at Coastal

Systems Station, where his expertise in the fields of acoustics, optics, and magnetics are frequently required. He headed

the original MADOM project, which successfully demonstrated a low frequency synthetic aperture sonar. He is currently helping coordinate the Joint Counter Mine ACTD.

Mr. Robert W. Sheriff has twenty years of experience

in acoustic sensor technology development. His activities

over the past six years have been focused towards high resolution synthetic aperture side-look sonars. In particular, he

has had a key role in the design of the synthetic aperture

used in the U.S. Navy’s shallow water sensor suite (High

Frequency/Low Frequency Synthetic Aperture Sonar). He

is presently the lead analyst for the synthetic aperture sonars

under development at Northrup/Grumman.

Mr. James T. Christoff has been involved in the development of high resolution synthetic aperture sonar (SAS)

for mine hunting applications for the past twenty years. He

is currently working in the area of self-cohering SAS, adaptive beamforming of conventional and synthetic arrays, and

the adaption of modern signal processing techniques to high

resolution imaging sonar systems. Most recently he has

served as the project engineer for the High Frequency/Low

Frequency Synthetic Aperture Sonar.

Mr. Thomas C. Montgomery has been employed at

Northrop Grumman Oceanic Systems since 1981. He is currently an engineer in the SONAR systems group. Mr. Montgomery has been working in the area of high resolution Synthetic Aperture SONAR for proud and buried detection for 5

years.

REFERENCES

1. R.S. Raven, “Electronics stabilization for displaced

phase center systems”, United States Patent

4,244,0336, Jan 1981.

2. R.W. Sheriff, “Synthetic aperture beamforming with

automatic phase compensation for high frequency

sonars”, Proceedings of the 1992 Symposium On

Autonomous Underwater Vehicle Technology, pp.

236-245, July 1992.

3. L. Vaizer, J.T. Christoff, J.D. Lathrop, and G.S.

Sammelmann “Motion Compensation for High

Frequency Sonars”, presented at Nov. 1994 ADPA

conference.
Three/1997 9

Automated Detection/

Classification of Sea

Mines in Sonar

Imagery

Gerald J. Dobeck, John C. Hyland and Le’Derick Smedley

Naval Surface Warfare Center, Dahlgren Division, Coastal Systems Station, Panama City, Florida

Abstract

An advanced capability for automated detection and

classification of sea mines in sonar imagery has been developed. The Advanced Mine Detection and Classification

(AMDAC) algorithm consists of an improved detection density algorithm, a classification feature extractor that uses a

stepwise feature selection strategy, a k-nearest neighbor

attractor-based neural network (KNN) classifier, and an optimal discriminatory filter classifier (ODFC). The detection

stage uses a nonlinear matched filter to identify mine-size

regions in the sonar image that closely match a mine’s signature. For each detected mine-like region, the feature extractor calculates a large set of candidate classification features. A stepwise feature selection process then determines

the subset features that optimizes probability of detection

and probability of classification (PdPc) for each of the classifiers while minimizing false alarms.

The AMDAC has been tested using 335 sonar images

from three different sonar systems: a synthetic aperture sonar (SAS with 255 images), a side-scan sonar (SSS1 with 60

images) and another side-scan sonar (SSS2 with 60 images).

The AMDAC’s performance is: 90% PdPc with 0.42 false

alarms per image for the SSS1; 92% PdPc with 0.64 false

alarms per image for the SAS; and 91% PdPc with 0.12

false alarms per image for the SSS2. For these data bases

the algorithm’s performance is as good or slightly better than

that of an expert sonar operator.

This work was funded by the Office of Naval Research,

ONR 321TS, through the 6.2 Mine Countermeasures program element.

1. Introduction

This paper builds on our earlier work [1] and describes

a new algorithm for the automated detection and classification (D/C) of sea mines in sonar imagery. Three sonar image data bases are used to evaluate the new D/C algorithm.

The algorithm is referred to as the Advanced Mine Detection and Classification (AMDAC) algorithm. It is comprised

of four stages: (1) Image Enhancement, (2) Detection, (3)

Feature Extraction and Optimal Feature Selection, and (4)

Classification (see Figure 1).

For the last seven years CSS has used three side-looking sonar data bases to evaluate mine detection and classification (D/C) algorithms developed in house, by industry and

by academia [2], [3], [4], [5]. The data bases consist of a

255 image set from a synthetic aperture sonar (SAS), a 60

image set from a side-scan sonar (SSS1), and a 60 image set

from another side-scan sonar (SSS2).

These data bases were selected because they demonstrate a variety of problematic issues and signal processing

challenges. Mine threats in these data bases are bottom

mines; they provide a significantly greater challenge to detect and classify than volume mines. Results for probability

of detection and classification (PdPc) and false alarm rates

referred to in this report are for single pass (one look) sonar

operation. Single pass operation is of interest because of the

implied higher search rate. Performance of an expert sonar

operator was available for two of these databases, thus providing a benchmark for automated algorithm performance.

Because these three data bases are somewhat small and do

not represent all interesting scenarios, one must be cautious
10 Naval Research Reviews

SONAR IMAGE AND

detection

compute

candidate

classification

features

Select best

features

for

KNN

Attractorbased

Optimal

Discrimination

Select best

features

for

ODFC

FINAL

CLASS

DETECTION

NONLINEAR

MATCHED

FILTERS

ANOMALY

SCREEN

BACKGROUND

IMAGE

ENHANCEMENT

NORMALIZATION

set of

OPTIMAL

STEPWISE

FEATURE

SELECTION

OPTIMAL

STEPWISE

FEATURE

SELECTION

K-nearest

Neighbor

Neural Net

(KNN)

Filter

Classifier

(ODFC)

OPTIMAL FEATURE SELECTION

At each

CLASSIFICATION

Figure 1

Overview of AMDAC Algorithm.

in generalizing any conclusions. However, the prohibitive

cost of collecting and processing large data bases makes

utilization of small data bases the typical practice when evaluating and comparing automated D/C algorithms. Real sonar image data is preferred over simulated sonar data because sonar simulations are expensive and do not capture all

the critical dynamics associated with actual sonar images.

The following sections, 2.1 through 2.6, describe the

four stages of the AMDAC.

2. AMDAC Algorithm

2.1 Image Enhancement

The purpose of the image enhancement stage is to precondition the image so that the subsequent detection and classification stages are robust to variations in background level.

This is accomplished by normalizing the background

throughout the image to a constant level so that highlight

and shadow levels are consistent and clearly stand out.

The principal reasons for variations of bottom brightness are (1) inadequate range-varying gains that are applied

to the sonar return to compensate for the range-dependent

reduction in signal strength cause by spherical spreading of

acoustic energy, (2) the strong sonar returns from highly reflective bottom regions (e.g., gravel), and (3) the weak sonar

returns from highly absorbent regions (e.g., mud). Thus, preconditioning reduces some of the background invariance and

reinstates some degree of robustness to bottom variation.

For the three sonar data bases studied in this article

the inadequate range-varying gain problem, as described

above, was the dominate factor; so a simple range normalization was found to be adequate. This was done as follows.

I

r

(i,j) = raw 8-bit sonar image (0 ≤ Ir(i,j) ≤ 255 )

 i = range index ( 1≤ i ≤ n )

 j = cross-range index ( 1 ≤ j ≤ m )

In(i,j) = normalized image (0 ≤ In(i,j) ≤ 4 )

 = MIN ( 4, Ir(i,j)/b(i) )

where

b(i) = mean level of Ir at range i

= 1

m

I i, j n

j 1

m

( ) =

∑

Note the following facts about In:

(1) The mean background pixel intensity is essentially unity.

(2) The dynamic range for highlight pixel intensity is from 1

to 4.
Three/1997 11

Figure 2

Target Signature Mask.

{

Pre-Target Highlight Dead

Zone

Shadow/

Post-Target

CrossRange

Range

(3) The dynamic range for shadow pixel intensity is from 1

to 0.

Experience of expert side-looking sonar operators has

shown that the above scaling procedure is a good way to

distribute the dynamic range between highlight and shadow.

In subsequent discussions we will make use of these

three facts in setting parameter values. As an aside, it has

been found that images from other environments may contain many large irregular regions, some of which are highly

reflective while others are highly absorbent. In these cases

the simple range normalization does not achieve the goal of

making the background level uniformly near unity. Other

computationally more intensive methods are needed that

normalize the image based on local statistics within the image. As an example, low-pass median filtering has performed

quite well for us.

2.2 Detection Stage

The purpose of the detection stage is to scan the entire

image and identify candidate mine-like regions that will be

more thoroughly analyzed by the subsequent classification

stages. This is the most computationally intensive stage because a mine-size region surrounding each image pixel must

be evaluated. Therefore, the goal is to keep the computations involved with each mine-size region small. The purpose of this computation is primarily not to determine minelikeness but rather to screen out the non mine-like regions in

the vast majority of the image and thus reduce the amount of

data that must be processed by the computationally more

intense classification stages. If the detection stage can reduce a typical image to about 10 detections (10 being very

generous), the computational requirements of the classification stages are insignificant when compared to the detection

screening.

The detection stage of the AMDAC is an improvement

on the one reported in our earlier paper [1]. The detection

stage operates on the normalized image. The algorithm divides the image into regions along range to account for the

variability of the background and mine signature as a function of range. For this study the image was divided into

three regions. Next, the image is convolved with a nonlinear matched filter, a different matched filter for each of the

range regions. The procedure sets negative values from the

match filter to zero. For each range region the resulting

matched-filtered image is normalized by removing the

region’s mean and dividing by the region’s standard deviation. Next, the procedure scans a target-size window over

the normalized matched-filter image. A detection is declared

by counting the total number of pixels within the window

that exceed a specified “amplitude-detection threshold”; a

pixel count above the specified “detection-count threshold”

is declared a target.

2.3 Nonlinear Matched Filter

The nonlinear matched filter is the work horse of the

detection stage [6]. The matched-filter mask contains four

distinct regions: pre-target, highlight, dead zone and shadow/

post-target ; see Figure 2. It is defined as,

I i, j g h k, l , I i k, j l m n

l M

M

k N

N

1

2

1

2

( ) = ( ) ( ) ( ) + + =− =−

∑ ∑

where

h(k,l) (I-1) for h(k,l) corresponding to

shadow, highlight, and dead zone regions

g (h (k,l), I) =

h(k,l) |I-1| for h(k,l) corresponding to pretarget region and post-target regions

Note that the nominal background level of unity is subtracted

from I.

In each of the four regions, the matched filter coefficients are constant and defined by,

shadow region or post-target region: h(k,l) = 1/(Sa(S0 - 1))

highlight region: = 1/(Ha(H0 - 1))

dead zone region: = 0

pre-target and post-target regions: = -1/(Ta|T0 - 1|)

where

Sa = area of shadow region in square pixels (see Tables

1, 2 and 3)

S0 = reference shadow level = 0.75 for this study

Ha = area of highlight region in square pixels (see Tables

1, 2 and 3)
12 Naval Research Reviews

{

H0 = reference highlight level = 1.5 for this study

Ta = area of pre-target region in square pixels (see

Tables 1, 2 and 3)

T0 = reference anomalous background level

The following two anomalous levels used in this study

result in the same h(k,l) value.

2.0, anomalous high backgound level

T0=

0.0, an anomalous low background level

In this study the reference center h(0,0) corresponds

to the center pixel of the highlight region. The following is a

brief discussion of this matched filter.

The nonlinearity is associated only with the pre-target

and post-target regions. The intent of this part of the matched

filter was to prevent the detection of objects with highlights

or shadows that were greater than mine size. The filter coefficient for this region is negative; thus, the output of the filter is decreased if the pre-target or post-target regions are

dominated by low or high valued pixels. This region could

have been selected to encircle the typical mine-size area.

However, the large variations in the mine’s cross-range size

and shadow length, together with considerations of the extra

computational burden, led us to use only the area in front of

the highlight region. Since the mines in our SAS image data

base have no shadows, a post-target region was used in place

of the shadow region.

The selection of the coefficients, defined by the reference levels and the region’s area, was done to make the contribution from each region of the matched filter equal for

typical shadow, highlight, and anomalous background levels. Specifically, the coefficients are constructed such that

the contribution for each region is unity when evaluated at

the reference level of that region.

The optimal detection-amplitude and detection-count

threshold pair is selected from the optimal receiver operator

characteristics (ROC) table [6]. The ROC table contains

optimal threshold pair that gives the least false alarm rate

for a given Pd. A computationally fast method was developed to generate the ROC table that requires only a single

pass through the data base.

The AMDAC uses a different filter mask for each mine

type and each range region. For example, the SAS sonar has

one mask for detecting mine type “A” in the first region,

another mask for detecting mine type “A” in the second region, and another mask for detecting mine type “A” in the

third region. To detect a different mine type, three different

masks would be used. Reference [1] discusses target mask

design in detail. Tables 1 through 3 show the masks’ sizes

for the three sonars and the three range regions.

2.4 Feature Extraction

In the feature extraction stage, a large set of classification features is computed from a target-size classification

window centered at each detection location in both the normalized raw sonar image and the normalized matched-filtered

image. The procedure calculates forty-five candidate features

based on the size, shape and strength of the highlight and

shadow, and histogram information of pixel intensity within

the classification window. A complete list of features follows:

1. W_pix: number of normalized image pixels in the

window that exceed a threshold*

2. Mfw_pix: number of matched-filter pixels in the window that exceed a threshold

3. M_wpix: maximum normalized image pixel intensity

in the window

4. Mmf_wpix: maximum matched-filter pixel intensity in

the window

5. T_str: target strength computed from the normalized image image

6. Mft_str: target strength computed from the matchedfilter image

7. Mx_eig: length of major axis of an ellipse fit to highlight region of the normalized image

8. Mn_eig: width of minor axis of an ellipse fit to highlight region of the normalized image

9. Mfmx_eig: length of major axis of an ellipse fit to bright

region of the matched-filter image
Three/1997 13

10. Mfmn_eig: width of minor axis of an ellipse fit to bright

region of the matched-filter image

11. S_len: shadow length

12. S_str: shadow strength

13. Mmf_pclu: maximum matched-filter intensity over the

pixels in the detection cluster

14. C_pix: number of pixels in the detection cluster

15. C_im: number of detected clusters in the image

16 - 25. Hist_org(i): umber of normalized pixels above

threshold(i) in the window, i from 1 to 10

26 - 35. Hist_mf(i): number of matched-filter pixels above

threshold(i) in the window

36 - 45. Hist_diff(i): number of matched-filter pixels above

threshold(i) in the classification window

minus the number of matched-filter pixels

above threshold(i) in the region that locally

surrounds the classification window

* All thresholds are selected by the designer.

2.5 Optimal Feature Selection

When training classifiers with finite databases, Bellman

discovered that the robustness of a classifier will collapse

when the number of features becomes too large [7]. This is

known as “the curse of dimensionality.” Therefore, an important part of the overall classifier design is to select a small

and robust set of classification features from the larger candidate set. Evaluating all possible combinations of 45 features is computationally not feasible (there are 245 - 1 combinations, a number greater than 1012). Therefore, a stepwise

optimal selection process is used (select the best single feature, next select best of the remaining 44 features to add to

the first, next select to best of the remaining 43 features to

add to the previous two, etc.) [8]. For a 45 feature set this

involves evaluating 1035 feature combinations.

Computationally efficient algorithms have been developed

for this selection process.

The two classifiers used in this algorithm were the knearest neighbor neural network (KNN) and the optimal discriminatory filter classifier (ODFC). Since two classifiers

are used in this detection/classification algorithm, there are

two selection algorithms; each one is tuned to the respective

classifier. This is required because a set of features that is

optimal for one classifier will, in general, be far from optimal for the other classifier. This usually results when each

classifier uses a different mathematical/geometrical structure to partition feature space into class regions. A set of

features amenable to one partitioning scheme may not separate well for a different partitioning scheme.

Briefly, two feature selection algorithms were developed for the KNN and ODFC, respectively. By optimal is

met the following. Bellman’s curse of dimensionality implies that, for a finite training set, the detection and classification performance (as judged by the validation/test set) does

not continually improve as more features are added (as dimensionality is increased) to the classification process. With

a finite data set it is possible only to determine a subset of

features that will give robust performance. Optimal selection in this context means finding the subset of features from

a much larger set that gives the best performance for both

the finite training and validation (test) sets for a specific classifier (in our case, either KNN or ODFC). Specifically, the

best subset is the subset of features which when fed into the

classifier minimizes a risk metric. The risk metric that is used

is defined as the maximum (between the training and validation data sets) of the weighted sum of the number of missed

mines and number of false calls. As stated above, for a typical candidate feature set of size 45, there would be 245 - 1

combinations of subsets to evaluate in order to determine a

global optimum. Because this is too large of a number to

evaluate, a Forward Stepwise Optimal Selection Process

(FSOSP) is used instead of a Globally Optimum Selection

process. The FSOSP selects as the first feature the one that

gives the best classifier performance of any single feature.

The second feature selected is the one that performs best

when used with the first. As it name implies, the Forward

Optimal Stepwise Selection Process continues at each stage

by adding a new feature to the subset determined in the previous stage. This stepwise process is obviously not globally

optimal, but has worked extremely well in this optimization

application. Our research suggests that it is important that

the classifier itself be used in the selection process. Other

selection techniques (e.g., principal component analysis or

multivariate normal models) are often used because they are

computationally tractable. But they are usually not based

on the same mathematical, statistical, or geometrical structure as the classifier and, therefore, do not select the best

features for that classifier. Keeping in mind Bellman’s curse

of dimensionality, our research suggests that classifiers that

train fast, for which the best subset of features can be optimally selected, will perform significantly better than more

sophisticated classifiers for which the best subsets cannot be

found because training is too computationally expensive.

The selection process has been further improved by

adding a Backward Stepwise Optimal Selection Process

(BSOSP) analogous to model order reduction used in statistics [9]. Because the candidate features are not orthogonal,

adding a new feature to the current optimal feature subset

can in fact decrease the performance metric; this is consistent with Bellman’s curse of dimensionality. The augmented

procedure is to first use the FSOSP to determine the initial

feature set as previously described. Then, the BSOSP determines which features to remove. Application of the FSOSP

and BSOSP can be repeated until performance improvement

stops. As an example, using the SSS2 data base, the number of false alarms per image decreased from 0.28 (using

only FSOSP) to 0.12 (using FSOSP followed by BSOSP)

for the same PdPc of 91%.
14 Naval Research Reviews

2.6 Classification

The KNN and ODFC classifiers were chosen because

they process the data using significantly different mathematical, statistical, and geometrical paradigms; “ANDING” the

two classifiers produces highly effective results. In general,

many classifiers could be “ANDED” but for the sonar data

presented here, two have worked very well. Also the process of classifier selection relied strongly on the fact that the

optimal feature selection process employed would be “tuned”

to each classifier. With this in mind the two classifiers were

chosen because: (a) they train very fast - this fact makes the

algorithm used in the stepwise optimal selection process feasible, (b) they each divide feature space into different geometrical models — the KNN into hyperspheres and the

ODFC into hyperplanes — this tends to make the classifiers

complementary,(c) the classifiers use different “noise” tolerant paradigms. The KNN uses sample Bayesian conditional probabilities as determined from the training data. The

ODFC uses noise rejection and signal enhancement schemes

based on concepts of matched-filter design.

The KNN is a probabilistic-based neural network that

employs radial-basis neurons [10]. The neural network has

two layers: the attractor layer and the confidence layer. The

attractor layer is constructed during training in the following way. Feature space is partitioned by a set of attractors.

A feature vector, f, is said to belong to attractor i if

N(f, fi

) < R i where N(f, f i) is some distance measure between

vectors f and f

i

 (for this study an L2 norm was used), f

i

 is the

center of attractor i and Ri is the radius of attractor i.

For a given N(.,.), Ri, and f

i

 the probability that attractor

i contains a given class can be estimated from sample probabilities computed from the training set. Each attractor is

represented as a radial-basis neuron that has the feature vector as an input and multiple outputs (one output for each

class). The i-th neuron fires when the input feature vector

belongs to attractor i; when it fires, the level of each output,

associated with a given class, equals the estimated probability that the attractor contains that class.

During training, the number of attractors, their center

locations (fi), radii (Ri), and degree of overlap are determined

in an optimal fashion to encompass the entire training set.

The radii are selected using the well-known k-nearest neighbor scheme to achieve accurate probability estimates and

adequate class boundary resolution for good class discrimination. The more training samples in an attractor, the more

accurate the sample probability estimates will be. In a relative sense, an attractor with a large radius will encompass a

large number of training samples. However, the size of the

radii is balanced against the resolution into which feature

space is partitioned; the smaller the radii, the better the discriminatory resolution at complex class boundaries. Finally,

attractor overlap is used to regulate the smoothness of probability estimates across class boundaries.

Training of this neural network also takes into account

that the training data is usually statistically unfairly sampled;

i.e., the number of training samples in a class (or subclass)

does not reflect the true probability of occurrence of that

class but instead reflects the artificial conditions over which

the data was collected. For example, one might have more

of one class than another, or more data collected under one

condition than another. In reality, fairly sampled data is seldom available because the cost to collect an adequate data

base is unacceptably high or simply because one does not

have adequate access to another country’s targets or environment in which to collect representative data. Under such

uncertainty, it was determined that the best statistical property to reinstate into the training set is that of equally likely

classes (or subclasses). This is accomplished by a specially

devised counting method that is used to determine the sample

probabilities. Thus, the training of this neural network is not

biased to favor those classes that were most plentiful in the

training and test sets and to ignore those classes that were

least abundant. Other neural network training algorithms

that optimize the number of correct classifications are typically biased by such data.

The output of the attractor layer is then fed to the confidence layer which determines the confidence that the input

feature vector belongs to each class. First all the attractors

that contain the input feature vector (or are sufficiently near

it) are identified. The confidence that the feature vector belongs to a given class is then determined by interpolating the

individual probability estimates that the feature vector belongs to a class over this set of attractors. In the interpolation, the contribution of each attractor’s probability estimate

is weighed according to how close the feature vector is to

the attractor’s center.

This neural network has the desirable characteristic

that it can determine if a feature vector cannot be reliably

classified. Specifically, if there are no attractors sufficiently

near, then the feature vector is considered statistically different than the training vectors and is said to belong to an

unknown class. Another way to view this is to say that during training, the neural network never experienced anything

that was representative of this new vector (i.e., the new vector is not within the neural network’s experience base). Under this circumstance, there is no basis to select among the

known classes and all outputs of the neural network are set

to zero. Other noteworthy characteristics of this network

are that the training algorithm is non-iterative, fast and the

network sizes are reasonable thereby permitting very quick

execution so specialized hardware is not needed.

The ODFC is a classifier with its basis in linear discrimination theory [10]. Two banks of linear filters are determined from the training set: one bank is sensitive to mine

characteristics and the other is sensitive to clutter characteristics. The following is a brief description on how the discrimination process works.

Let f be a feature vector. Let Fm(f,i) be the energy
Three/1997 15

Figure 3

Detections for SSS2 Image 0.

Figure 5

ODFC Classifications for SSS2 Image 0.

Figure 4

KNN Classifications for SSS2 Image 0.

output for input f of the i-th filter in the mine-filter bank, and

Fc(f,j) be the energy output for input f of the j-th filter in the

clutter-filter bank. Fm and Fc are linear filters; the outputs of

these filters are linear combinations of the input features plus

a DC offset and a warping term made up of a linear combination of nonlinear functions of the features. The filter’s coefficients, DC offset, and warping coefficients are determined

such that, on the average, the energy output of the mine-filter

bank is greater than the energy output of the clutter-filter

bank when the input feature vector corresponds to a mine.

And vice versa for a feature vector that corresponds to clutter. That is, on the average,

max Fm(f(mine),i) > max Fc(f(mine),j)

 i j

max Fm(f(clutter),i) < max Fc

(f(clutter),j)

 i j

The solution involves the solving of a generalized eigenvalue problem. In the ODCF linear combinations of features

permit boundaries between classes to be described by sets of

hyperplanes. By adding a linear combination of nonlinear

functions of the features one changes the hyperplane surface

to one that is curved (warped). These nonlinear terms permit the partitioning surfaces in feature space to more efficiently fit irregular class boundaries.

Classification is determined by “ANDING” the output of both classifiers. Both the KNN and ODFC have two

outputs corresponding to the confidence that the input feature vector is associated with a mine or clutter. Let Cknn(mine)

and Cknn(clutter) designate these two confident levels for the

KNN; and Codfc(mine) and Codfc(clutter) for the ODFC. If

 Tknn Cknn(mine) > Cknn(clutter)

 AND

 Todfc Codfc(mine) > Codfc(clutter)

a mine classification is declared. The classification gain

thresholds, Tknn and Todfc, are adjusted to select the desired

balance between PdPc and false alarms.

3. Results

3.1 SSS2 Example 1

In Figures 3 through 10, Image 0 and Image 51 from

the 60 image SSS2 data base are used to illustrate the D/C

algorithm. Figure 3 shows the normalized image and the

results of the detection stage. The gray scale indicator on

the extreme right of the image indicates pixel intensity. White

indicates a pixel intensity of 4.0 and represents the maximum pixel intensity value displayed. Black indicates a pixel

intensity of 0.0 and represents the minimum pixel intensity

value displayed. To give a size perspective, each sonar image has a two-meter by two-meter square highlight in the

lower left-hand corner of the image. In all images, range

from the sonar increases from left to right implying target

shadows are to the right of the highlights. The detected minelike targets typically resemble the target-signature mask—a

highlight region followed by a shadow and preceded by a

uniform background. With a strong highlight and pronounced

shadow, Detection B is easily visible. Although Detection C
16 Naval Research Reviews

Figure 6

KNN and ODFC Classifications for SSS2 Image 0.

Figure 7

Detections for SSS2 Image 51.

Figure 8

KNN Classifications for SSS2 Image 51.

Figure 9

ODFC Classifications for SSS2 Image 51.

Figure 10

KNN and ODFC Classifications for SSS2 Image 51.

has a noticeable highlight followed by a shadow, the shadow’s

proximity to a trough made by a fisherman’s drag net obscures the shadow region. False Detection A is triggered by

a subtle highlight and shadow.

Figures 4 and 5 show that both the KNN and the ODFC

classify Detections B and C as mine-like; they both reject

Detection A. “ANDING” the two classifiers results in Final

Classifications B and C as illustrated in Figure 6. The squares

in Figure 6 indicate actual mines; there one sees that both

mines are detected and classified and one false target is detected but not classified as mine-like by either classifier.

3.2 SSS2 Example 2

Figure 7 shows another SSS2 example. This particular image illustrates some of the difficulties associated with

normalizing the background in the sonar image. Note that

the background level of the image is not uniform. And there

is a large band of bright pixels that extends from the upper

left to the lower right of the image caused by a highly reflective bottom type. Despite the lack of uniformity in the background intensity, the AMDAC detects four candidate mines.

In Figure 8, the KNN classifies only Detections D and

E as mines and rejects Detections C and F. Figure 9 shows

that the ODFC classifies Detections E and F as mines and

rejects Detections C and D. Results from “ANDING” the

two classifiers are shown in Figure 10. As the ground truth

square indicates, the final mine classification is correct and

all false alarms have been eliminated. This example clearly

illustrates the power of combining two different classification approaches; separately, each approach would have had

one correct classification and one false alarm. “ANDING”

the two classifiers eliminated the false alarms.
Three/1997 17

Figure 11

False Alarms and Missed Detections for SSS2.

Figure 12

Detections for SAS Image 116.

3.3 SSS2 False Alarms, Missed

Detections and Overall

Performance

Overall, the AMDAC detected and classified 91% of

the mines in the SSS2 data base with an average false alarm

rate of 0.12 false alarms per image. Figure 11 shows the

seven false alarms detected and three missed mines in the

entire SSS2 data base. The false targets are quite mine-like.

The top and bottom missed mines in Figure 11 have weak

highlights and very poor shadows. The middle missed mine

is embedded in very bright surface reverberation which made

the mine signature blend in with the background.

3.4 SAS Example

In Figures 12 through 15, Image 116 of the 215 image

SAS image data base is used to further illustrate the D/C

algorithm. The sonar image contains data from both the starboard and port sides of the sonar. Returns from the water

column in the middle of the sonar image were blanked out

prior to processing. Sonar operators ignore this region because it consists of returns from the water volume prior to

the first bottom return. Note that there are no shadows on

the targets; because the data was collected during shallow

water operations, surface reverberation came into play and

filled in the shadows. The lack of a target shadow makes the

detection and classification process quite difficult. When a

target exhibits only a highlight, the detection thresholds are

typically set to accept a higher false detection rate. The classification phase is then relied upon to reject the extra false

targets that are detected.

Figure 12 shows that the AMDAC has detected eight

mine-like regions in the SAS image. All eight candidate

regions do exhibit the desired mine-size highlight surrounded

by a uniform background. Figure 13 shows that the KNN

rejects two of the detections and Figure 14 shows the ODFC

rejects five of the detections. “ANDING” the classifiers

results in the classification of the two mines and one false

target as shown in Figure 15.

3.5 Results Summary

Table 4 summarizes the AMDAC’s performance for

the all three sonars, the SSS1, the SAS and the SSS2. Table
18 Naval Research Reviews

Figure 13

KNN Classifications for SAS Image 116.

Figure 14

ODFC Classifications for SAS Image 116.

4 also lists expert sonar operator’s performance for the SSS1

and SAS data bases. Operator performance for the SSS2 is

unavailable. Because of the similarities between the SSS1

and the SSS2, the operator performance for the SSS2 should

be comparable to that of the SSS1.

For the SSS1, the AMDAC detects and classifies 90%

of the mines with 0.42 false alarms per image while the expert operator detected 80% with 0.72 false alarms per image. For the SAS, the AMDAC performs similarly to the

expert sonar operator at 68% PdPc. However, the AMDAC

can also be adjusted to 92% PdPc with 0.64 false alarms per

image. And for the SSS2, the AMDAC shows an impressive 91% PdPc with only 0.12 false alarms per image.

4. Conclusions and

Future Research

Five fundamental principles have come out of this research on automated detection and classification.

(1) The detection stage is by far the most

computationally intensive stage. Because the entire image

must be scanned, the basic design focus is on keeping the

number of computations per pixel small. The main goal is

not to detect mine-like objects, but rather to eliminate the

majority of the image that is non-mine-like. The resulting

detections will be processed in depth by the classification

stages which require many more computations per pixel in

order to extract robust and discriminatory mine-like features.

If the detector produces 10 mine-like detections per image

on the average (a very generous number), we have found

that the computational requirements of our classification

stages are negligible compared to those of the detector. It

then follows that this classifier’s main goal is to eliminate

the false targets while preserving mine detections.

(2) The classification stage uses two classifiers: the
Three/1997 19

Figure 15

KNN and ODFC Classifications for SAS Image 116.

KNN and ODFC. The stepwise optimal selection procedure, described herein, dramatically improves the performance of each classifier by permitting a computationally

efficient means to select the “best” subset of classification

features from a much larger set of candidate features. As

such it mitigates problems associated with Bellman’s curse

of dimensionality.

(3) Significant improvement in classifier performance

results if the feature selection process is highly “tuned” to

the respective classifier. Classifiers use statistical, mathematical, and geometrical constructs to partition feature space into

class regions. In this research we found that a subset of features optimal for one classifier’s partitioning scheme will be

far from optimal for another classifier which uses a different

partitioning approach. Therefore, we us the same partitioning approach when determining the best feature subset for a

particular classifier.

(4) Because training procedure for both the KNN and

ODFC is very fast, it was possible to develop a stepwise

optimal selection procedure capable of efficiently evaluating thousands of feature combinations. Without such an automated procedure it was impossible for us to determine the

“best” features. Being able to use the “best” features dramatically improved the performance of our classifiers. This

strongly suggests the following. Rapid-trainable classifiers,

whose “best” features can be determined by stepwise optimal selection, may significantly outperform more complex

classifiers that have computationally intense training procedures and must rely on selection methods that are either heuristic or not well “tuned” to the classifier.

(5) There is a significant improvement in overall classification performance that results from “ANDING” classifiers which use complementary statistical, mathematical, and

geometrical constructs to describe class boundaries in feature space. “ANDING” the KNN and ODFC proved very

adept at reducing false alarms while maintaining a high probability of mine detection and classification. This concept

can be illustrated using the paradigm of “getting a second

doctor’s opinion”. Two doctors look at data from a patient

to arrive at their diagnoses. Even though they look at the

same data, each doctor emphasizes or de-emphasizes different aspects based on their training and experience. If they

conclude the same diagnosis, there is little doubt of its validity. This also suggests that “ANDING” two or more simple

classifiers may lead to improved performance over a single

more complex classifier. This leads the authors to believe

that extremely high performance in automated mine D/C can

be achieved by “ANDING” (fusing) the diverse D/C algorithms currently being developed through ONR under their

Mine Countermeasures research programs.

Overall, the new detection and classification algorithm

developed has been shown to perform significantly better

than the detection and classification algorithm previously

reported in SPIE 95 [1]. The new algorithm used in this

study performs as well or slightly better than an expert sonar

operator on the three sonar image data bases.

The algorithm’s robustness will be further evaluated

on new advanced synthetic aperture and side-looking sonars

that are currently undergoing their initial sea testing. We will

investigate the benefits of using three classifiers rather than

just two. Using more than three classifiers produce some

combinatorial design issues. Also, algorithms that adapt

detection and classification thresholds will be developed in

order to increase robustness to varying environments and

bottom types.

5. Acknowledgments

This work was funded by the Office of Naval Research,

ONR 321TS as part of the 6.2 Mine Countermeasures program element The technical agent for this work is the NSWC

Coastal Systems Station, Dahlgren Division. The point of

contact is Dr. Gerald J. Dobeck, Code 10T2, Panama City,

Florida 32407-7001, Phone: 904-234-4222.
20 Naval Research Reviews

6. Biographies

Dr. Gerald J. Dobeck received the B.S. degree in physics at the University of Massachusetts in 1970 and the M.S.

and Ph.D. degrees in electrical engineering at the University

of South Florida in 1973 and 1976, respectively. Since 1976

he has been employed at the Coastal Systems Station, Naval

Surface Warfare Center, Dahlgren Division, Panama City,

Florida, where he received the 1981 and 1996 Commanding

Officer/Technical Director Science and Technology Award.

He is a reviewer for IEEE, ASME, and the Journal of Underwater Acoustics and has been session chair at past IEEE and

SPIE conferences. His current research interests include

automatic detection and classification of underwater targets

in cluttered environments from synthetic and real aperture

sonar imagery, the echo structure of acoustic sonar returns,

underwater electro-optic imagery, and gradiometer/magnetometer signals.

Dr. John C. Hyland received the B.E. degree in electrical engineering and mathematics from Vanderbilt University in 1984 and the M.E. and Ph.D. degrees in electrical

engineering from the University of Florida in 1987 and 1993.

From 1987 to present he has been employed at the Coastal

Systems Station, NSWC Dahlgren Division, Panama City,

Florida. He has received two commendations from the Chief

of Naval Research for his research into mine avoidance. He

has been a reviewer of the IEEE Ocean Engineering Society

Journal and has been a session co-chair at the Society for

Computer Simulation Conference (SCSC). His technical

experience and research interests include underwater vehicles, simulation, sonar modeling, statistical analysis of

minehunting systems, and image processing.

Le’Derick Smedley received a B.S. degree in electrical engineering from Tuskegee University in 1990. Since,

he has been employed at the Coastal Systems Station, Naval

Surface Warfare Center, Dahlgren Division, Panama City,

Florida. He has worked primarily in image processing: detection and classification, beamforming, neural network. His

current research efforts are the application of neural networks

to the problems of automated mine detection and classification in compressed sonar images as well as sensor fusion

data.

7. REFERENCES

[1] G. J. Dobeck and J. C. Hyland, “Sea Mine Detection

and Classification Using Side-Looking Sonars,”

Proceedings of the SPIE Annual International

Symposium on Aerospace/Defense Sensing, Simulation and Control, Vol. 2496: Detection Technologies

for Mines and Mine-like Targets, 17-21 April 1995,

Orlando, Florida.

[2] M. G. Bello, “Hierarchical Multilayer Perceptron

Network-Based Fusion Algorithms for Detection/

Classification of Mines Using Multiple Acoustic

Images and Magnetic Data,” Proceedings of SPIE

’96, Vol. 2765, 9-12 April, 1996, Orlando, Florida.

[3] T. Aridgides et al,, “Adaptive-Filter/Feature Orthogonalization Processing String for Optimal LLRT Mine

Classification in Side-Scan Sonar Imagery,” Proceedings of SPIE ’96, Vol. 2765, 9-12 April, 1996,

Orlando, Florida.

[4] D. Kil and F. Shin, “The Impact of Lossy Image

Compression on Automated Target Recognition

Performance,” Proceedings of IEEE Oceans ’96,

September, 1996, Fort Lauderdale, Florida .

[5] S. Nelson and S. Tuovila, “Automated Recognition

of Acoustic Image Clutter,” Proceedings of SPIE ’96,

Vol. 2765, 9-12 April, 1996, Orlando, Florida.

[6] H. Van Trees, Detection, Estimation and Modulation

Theory, Part I, John Wiley and Sons, 1968.

[7] R. Duda and P. Hart, Pattern Recognition and Scene

Analysis, John Wiley and Sons, 1973.

[8] K. Enslein, A. Ralston and H. Wolf, Statistical

Methods for Digital Computers, Volume II, John

Wiley and Sons, 1977.

[9] L. Ott, An Introduction to Statisitcal Methods and

Data Analysis, Third Edition, PWS-KENT Publishing Company, Boston, Massachusetts, 1988.

[10] B. D. Rilpley, Pattern Recognition and Neural

Networks, Cambridge University Press, 1996.
Three/1997 21

Underwater ElectroOptical System for

Mine Identification

Michael P. Strand

Coastal Systems Station, Naval Surface Warfare Center, Dahlgren Division, Panama City, Florida

l. Introduction

The Electro-Optic Identification (EOID) Sensors

project is developing a Laser Visual Identification Sensor

(LVIS) for identification of proud, partially buried, and

moored mines in shallow water/very shallow water (SW/

VSW). LVIS will be deployed in small diameter underwater

vehicles, including unmanned underwater vehicles (UUVs).

Since the mission is mine identification, LVIS must: (a) deliver high quality images in turbid coastal waters, while (b)

being compatible with the size and power constraints imposed by the intended deployment platforms. This project is

sponsored by the Office of Naval Research, as a part of the

AOA Mine Reconnaissance/Hunter program.

High quality images which retain target detail and contrast are required for mine identification. LVIS will be designed to produce images of minelike contacts (MLC) of

sufficient quality to allow identification while operating in

turbid coastal waters from a small diameter UUV. Technology goals for the first generation LVIS are: (a) identification

range up to 40 feet for proud, partially buried, and moored

MLCs under coastal water conditions; (b) day/night operation from a UUV operating at speeds up to 4 knots; (c) power

consumption less than 500 watts, with 275 watts being typical; and (d) packaged within a 32-inch long portion of a 21-

inch diameter vehicle section.

2. Role of EO in Mine

Countermeasures

Analysis of Fleet missions, including establishment of

a Fleet Operating Area (FOA) and establishment of an Amphibious Objective Area (AOA) provide a framework to determine the benefit of performing identification of MLCs

during reconnaissance. Identification plays a key role in reconnaissance to establish an AOA. Reconnaissance operations are conducted prior to the assault to help define the

threat and to determine where mine sweeping or mine hunting must be performed. Through reconnaissance, assessments

are made of the extent of the mined areas, and mine types

and mine densities are determined. Mine identification is

required to make these assessments. Without identification,

only densities of MLCs may be obtained, and mine sweeping or mine hunting will be required over all portions of the

AOA which have MLCs. With identification, mine hunting

or mine sweeping may be restricted to the areas of the AOA

which are mined. In typical scenarios, this can lead to a drastic

reduction in mine sweeping/hunting assets required to complete the mission within the allotted time.

Identification also plays a key role in reconnaissance

prior to establishment of an FOA. Prior to moving a battle

group or task force into an FOA, the officer in tactical com-
22 Naval Research Reviews

mand (OTC) must be provided an assessment of the potential mine threat in the area. The tactical mine warfare officer

uses data gathered by all available intelligence sources to

estimate the likelihood and level of mining. The operational

concept for a reconnaissance system is to use the system to

verify the presence of a minefield in a specific area by locating individual mines. The system is used to sample the area

of interest, with the level of sampling effort based upon a

balancing of the threat estimate and tactical time constraints.

If no mines are found, the OTC either decides to accept the

risk and continue operations or he continues sampling to increase the confidence that no mines are present. If one or

more mines are located and identified, the battle group or

task force will modify their intended movement for the purpose of self-protection. This will entail continuing reconnaissance to provide the OTC with enough information to

decide the best course of action; i.e., either (a) map all objects for avoidance, (b) avoid the entire minefield, (c) request and wait for clearance by mine warfare forces, or (d)

reconnoiter an alternative operating area.

If reconnaissance is performed without identification,

every MLC in the FOA must be avoided. The percent of

operating area denied is a non-linear function of the density

of MLCs and the radius of the avoidance circle around each

MLC. Even quite modest densities of MLCs lead to denied

areas which unacceptably restrict Fleet operating options.

In summary, mine identification is an important part

of mine reconnaissance. MLCs limit the performance of mine

reconnaissance systems. Without identification, MLCs must

be avoided by Fleet assets (leading to denied areas) or neutralized. Denied areas can dramatically and unacceptably

restrict Fleet operations options, while neutralization requires

time and appropriate Fleet assets. With LVIS providing a

mine identification capability to a mine reconnaissance system, only the contacts identified as mines need to be avoided

or neutralized.

3. Environmental

Impact on EO System

Performance

Environmentally derived noise sources are principal

factors limiting the performance of underwater EO imaging

systems. In deep, clear ocean water, an EO imaging sensor

may have a range in excess of 100 feet, while in turbid littoral waters the same sensor’s range may be reduced to 5

feet or less. For underwater imaging sensors, the environmental limitations may be classified as: (a) backscatter noise,

(b) blur/glow/forward scatter noise, and (c) attenuation. Backscatter noise refers to the photons which are scattered back

into the receiver before reaching the target plane. Blur, glow,

or forward scatter noise refers to the forward scattering of

photons which have been reflected from the target plane.

Because they are scattered, they image as though they were

reflected from the wrong position on the target, leading to a

loss of image resolution and contrast. Finally, attenuation

refers to the loss of photon signal due to photon absorption

or scattering out of the field of view of the receiver. Attenuation limits signal strength at the receiver.

A major goal of EO system designers is to optimize

the system design to minimize the detrimental effects of the

environment. In addition to the system architecture and parameters, the performance of a given underwater EO mine

countermeasures (MCM) system in a given location is determined by: (1) the Inherent Optical Properties (IOPs) of

the water, and (2) the IOPs of the bottom and target.

The IOPs of the water which affect the performance

of EO MCM systems currently under development include:

(1) the beam attenuation coefficient c(λ)[m-1], (2) the beam

absorption coefficient a(λ)[m-1], and (3) the volume scattering function β(λ,Θ)[m-1sr-1], and the scattering coefficient

b(λ)[m-1].

The beam attenuation coefficient c(λ)=a(λ)+b(λ) measures the exponential decay of a narrow laser beam due to

either absorption or scattering. The single scattering albedo

Λ(λ)=b(λ)/c(λ) measures the portion of the beam attenuation which is due to scattering. The volume scattering function, β(λ,Θ) measures the angular distribution of the photon

scattering.

The beam attenuation coefficient c is the primary indicator of the distance at which an EO imaging system will

be able to deliver acceptable imagery. Frequently, the water

clarity is characterized according to the range R=l/c at which

cR=1. Accordingly, when l/c=2m, for example, the water is

said to be “2-meter water,” and when l/c=4m, the water is

said to be “4-meter water.” Predictably, an imaging system

will deliver acceptable imagery at a longer range — approximately twice as far — in “4-meter water” than it will in “2-

meter water.” The range R of an imaging system is frequently

specified in terms of dimensionless beam attenuation lengths,

where the range in beam attenuation lengths is given by the

product cR. Thus, when an image is taken at a range where

cR=5, the image is said to be taken at 5 (beam) attenuation

lengths (AL).

The single scattering albedo Λ also plays a key role in

the performance of EO imaging systems. In typical coastal

waters Λ is 70-80 percent, although it can range from less

than 50 percent to greater than 90 percent. When the albedo

is low, attenuation by absorption is more predominant, leading to lower power levels. In this case, the imaging system is

more likely to become power limited. In addition, blur/glow/

forward scatter is less important when the albedo is low, so

the image contrast is higher. On the other hand, when the

albedo is high, blur/glow/forward scatter is more important,

leading to a reduction in image resolution and contrast. In

addition, attenuation due to absorption is less important, so

the system performance is less likely to be power limited.
Three/1997 23

The volume scattering function (VSF) characterizes

the angular dependence of the scattering, ranging from 0

degrees (forward) to 180 degrees (backward). The VSF affects all three of the primary mechanisms of image degradation. Backscatter depends on the magnitude of β(λ,Θ) near

Θ=π. Blur/glow/forward scatter depends on the magnitude

of β(λ,Θ) near Θ=0. Scattering at other angles contributes to

attenuation.

Conventional underwater imaging systems, consisting

of a video camera and floodlights, are limited to ranges of 1-

1.5-beam attenuation lengths (the beam attenuation length

in coastal waters is typically 1-2.5 meters) by backscatter

noise. They lack the range and resolution required to facilitate rapid target identification.

The EOID Sensors project has evaluated laser-based

imaging approaches capable of reducing the impact of environmental noise sources in order to facilitate rapid identification of MLCs in turbid littoral waters at useful ranges. Two

approaches, laser line scan (LLS) and laser range-gated

(LRG), have been evaluated in detail with the objective of

extending the mine identification range to five or more beam

attenuation lengths. LLS123 systems reduce the effects of

backscatter noise and blur/glow/forward scatter noise by

synchronously scanning a narrow laser beam and a narrow

field-of-view receiver across the sea bottom. Performance

of LLS systems is dramatically superior to the performance

of conventional imaging systems. LRG4 imaging systems

(i.e., imaging LIDARs) perform significantly better than

conventional systems because they reduce the impact of backscatter by temporarily “gating-out” much of the backscatter.

4. Testing of LaserBased Underwater EO

Imaging Systems

A test program was established in order to assess the

relative merits of the LRG and LLS imaging approaches for

SW/VSW mine identification. The cornerstone of this program was a sea test which provided simultaneous imagery

with state-of-the-art LLS and LRG systems. This test provided the first direct, head-to-head, comparison of the performance of these two competing technologies. This test

endeavored to remove all questions of unequal test conditions, since the two systems were imaging the same targets

from the same platform through the same water at the same

location at the same time.

The LRG system tested was Sparta Laser Systems

Laboratory SEE-RAY LRG system. The SEE-RAY system

employed a gated (approximately 8 nsec gate width), intensified CCD camera which generates images at standard video

rates. F/.85 receiver optics with a 29.4 mm aperture were

employed which gave the system an instantaneous field-ofview of 20 by 15 degrees. A tilt gimbal allowed movement

of the system through an arc of 130 degrees across track,

increasing the system field of regard. Laser illumination was

provided by a 30 Hz, Q-switched, frequency doubled

Nd:YAG laser operating at 532 nm with output power of

approximately 100 mJ per 6 ns pulse. The laser output power

could not be adjusted by the operator. However, zoom-optics on the laser allowed the output energy to be either distributed over the receiver field-of-view or concentrated near

the center of the image. The gating range, camera gain, laser

zoom, and gimbal tilt angle were controlled from an operator console. The raw LRG image data was displayed on a

television monitor and simultaneously recorded on a 10

MHZ, broadcast quality, U-Matic video recorder. After the

test, digital snapshot imagery was generated from the video

tape using an 8-bit digital frame grabbing board. Typically,

bursts of approximately 20 sequential images were grabbed

and visually inspected. The image deemed best was saved to

hard disk for analysis. SEE-RAY is 39 inches long, 13 inches

in diameter, and requires 500 watts of power.

The LLS system tested was the ART/SAIC LS-2048.

The LS-2048 employed a CW argon ion laser with simultaneous output at 488 and 514.5 nm. Laser output power could

be continuously adjusted from a maximum of 2 watts down

to 65 mW in all laser lines. A rotating, four-faceted mirror

and output optics assembly scanned the laser beam over a

70-degree section of the sea bottom while a synchronously

rotating four-faceted mirror and input optics assembly focused the reflected light onto a photomultiplier tube receiver.

The receiver circuitry incorporated a gated integrator which

divided each 70-degree scan line into 2048 or 1024 pixels

with an effective dynamic range of 12 bits. Raw, unprocessed, 12-bit data snapshot images of 1024 x 2048 pixels or

2048 x 1024 pixels were stored on hard disk for later analysis. Simultaneously, the scanned lines were processed as acquired and presented in a gray-scale waterfall display on the

operator console. The continuous waterfall display was also

recorded on S-VHS video tape. The LS-2048 is 80 inches

long, 11 inches in diameter, and requires 5,000 watts of power.

In order to operate in SW during daylight hours, the

LS-2048 was modified through the insertion of 3-nm pass

band optical filters, centered at 51 4.5 nm, into the receiver

optics. This improvement resulted in the first successful SW

daylight imagery using an LLS. In addition, the filter rejected the LRG system's 532-nm laser light, allowing the

LRG and LLS systems to simultaneously image the same

targets. Of course, the 514.5-nm filter also rejected reflected

light from the 488 line of the laser, resulting in an effective

output laser power range (in the 514.5 line) from 800 mW

down to 12 mW. The LLS laser powers reported in this document are the powers transmitted in the 514.5 line only.

The test platform chosen for this test was Applied Remote Technology's XP-21 autonomous undersea vehicle

(AUV) operating in a tethered mode. A payload section and

tether were fabricated which allowed the LRG and LLS sys-
24 Naval Research Reviews

tems to be simultaneously installed on the XP-21. The tether

provided power to the imaging systems; command and control, and trickle power to the XP-21; and command and control and image pathways to and from the imaging systems.

Use of the XP-21 allowed precise positioning of the imaging systems, repeatable test tracks, and very efficient data

collection. The LRG systems performance was optimized

through allowing its operating parameters to be adjusted to

give the best image while the XP-21 hovered above each

target. Data runs yielding simultaneous LRG and LLS imagery were typically executed only after the LRG parameters were optimized by hovering over each target.

A shallow, turbid water environment was selected for

the test. Periodic measurements of inherent and apparent

optical properties of the water column were made throughout each test day. Parameters measured included the beam

attenuation coefficient c, the absorption coefficient a, the

diffuse attenuation coefficient k, and downwelling and upwelling irradiance.

Targets deployed included positive and negative contrast Air Force Resolution Targets. These targets were painted

on 36-inch square aluminum panels using flat white and black

paints with nominal reflectivities of 87 and 2 percent, respectively. The target designated as the “positive” target consisted of a white Air Force resolution pattern painted on a

black background, while that designated as the “negative”

target consisted of a black Air Force resolution pattern painted

on a white background. This positive/negative target combination was selected to differentiate the effect of blur/glow/

forward scatter from those of backscatter and attenuation by

means of the following observations. First, notice that the

inherent contrasts CIN=(White-Black)÷White of the positive

and negative targets are identical, since they were prepared

using the same paints on the same substrate material.5 Second, since attenuation reduces the observed signal from the

positive (white) and negative (black) areas by the same factor A, it is seen that the observed contrast COBS=((A∗White)-

(A∗Black))÷(A∗White)=CIN cannot be reduced from the inherent contrast by attenuation.6

 Third, since the same backscatter (BS) intensity is added to the white and the black

areas, the observed contrasts COBS= [(A∗White+BS) -

(A∗Black+BS)] ÷ (A∗White+BS) = (White-Black) ÷

(White+BS÷A)<CIN are decreased from the inherent contrasts

by backscatter. However, the contrast reduction due to backscatter (and attenuation) is identical for the positive and negative targets. Fourth, white areas lose intensity to black areas

due to forward scattering, resulting in contrast reduction.

Since negative targets have larger white areas, they are more

susceptible to blur/glow/forward scatter noise, and therefore

suffer greater contrast reductions. Accordingly, greater contrast reductions in negative targets are an indicator of the

significance and magnitude of blur/glow/forward scatter effects on image quality.

Figures 1 and 2 present typical LLS and LRG images

of the positive and negative Air Force Resolution Targets,

respectively. The positive (white pattern on a black background) images in Figure 1 compare LLS pictures in the top

row7 and with LRG images in the bottom row for comparable ranges as measured in beam attenuation lengths (ALs).

The negative (black pattern on white background) images

are similarly arrayed in Figure 2. Compare from top to bottom for LLS versus LRG contrast. These images show: (1)

the LLS imagery is markedly superior to the LRG imagery

for both the positive and negative targets; and (2) images of

the bright background, negative targets are significantly

poorer than images for the positive targets for both systems,

but especially for the LRG system.

The superiority of the LLS imagery is not due to any

purported power advantage. Laser power parity for the two

systems, in terms of the same number of photons per pixel,

Figure 1

Positive (white) ranges for LLS versus LRG. LLS laser power

was 190 mW.

LLS Range = 5.6 AL LRG Range = 6.8 AL

LRG Range = 4.7 AL LRG Range = 5.6 AL LRG Range = 6.4 AL

Figure 2

Negative (black) ranges for LLS versus LRG. LLS laser power

was 190 mW.

LLS Range = 4.6 AL LLS Range = 5.6 AL LLS Range = 6.8 AL

LRG Range = 4.7 AL LRG Range = 5.6 AL LRG Range = 6.4 AL
Three/1997 25

occurred when the LLS power was approximately 200 mW.

The LLS imagery in Figures 1 and 2 was taken at 190 mW

laser power in the 514.5 nrn laser line, yielding approximate

energy parity with the LRG system . Figure 3 shows comparable LLS imagery taken at 5.6 ALs using 95, 35, and 12

mW laser power in the 514.5 laser line -- far below the energy density employed by the LRG system. Quantitative

analysis reveals that the LLS contrasts are maintained at these

low power levels, even though shot noise increases.7

 Conversely, when the LRG system laser was "zoomed down" on

the targets, resulting in an approximate four-fold increase in

energy density on the target, there was no observable improvement in LRG image quality.

Contrast values were extracted from various three-bar

patterns on these targets as a quantitative assessment of the

relative quality of the images. Figure 4 presents these contrasts as extracted from the vertical three-bar pattern above

the large square (e.g., the bar number 2 of series 0). The

widths of these bars are 2.24 cm, while the sides of the large

square are 10 cm. The sea test data has been supplemented

with data from an earlier LRG tank test. Figure 4 shows the

contrast points for the LRG and LLS systems are quite distinct and well separated. At any given range, the observed

contrast of the LLS system is typically two to three times the

contrast of the LRG system. Moreover, in order to match

LLS contrasts, the LRG system frequently needed to be two

or three times closer to the target.

The imagery and the contrast graph clearly show that

the contrasts of the negative targets are significantly lower

than the contrasts of the corresponding positive targets. As

indicated above, this disparity between the positive and negative target contrasts cannot be explained by means of attenuation and backscatter. Rather, it is a direct indicator of the

dominant role of blur/glow/ forward scatter noise in the degradation of the image quality. This dominant effect is particularly evident in the LRG system, where the negative target imagery is far inferior to the corresponding positive target imagery. For the LLS system, the negative target contrasts are smaller than, but still reasonably close to, the corresponding positive target contrasts, indicating success for

the LLS system in partially rejecting blur/glow/forward scatter noise.

Even more direct evidence of the dominant role of blur/

glow/forward scatter noise in the degradation of LRG imagery exists. Prior to the sea test, a test of a breadboard LRG

system had been completed using a 4- x 4- x 90-foot test

tank. The breadboard LRG was a dry unit; a large periscope

was used to insert the laser pulse into the tank and to extract

the return image pulse from the tank. This arrangement allowed data collection in a novel, "back-illuminated" configuration in addition to the normal "front-illuminated" configuration. in the back-illuminated configuration, backscatter noise was completely non-existent, allowing a very

straightforward evaluation of the role of blur/glow/forward

scatter noise in LRG systems. Figure 5 is a diagram of the

tank test configuration showing the two laser paths. This test

was conducted using fresh water, with Maalox used to add

controlled amounts of turbidity to the water. Tests were conducted under simulated turbid, littoral water conditions, with

beam attenuation lengths of 3, 2, 1, and 0.5 meters.

In the front-illuminated configuration (path 1), the laser beam is inserted into the water using a periscope and

propagates through the water on its way to the target. The

image pulse is reflected from the target, propagates through

the water on its way back to the periscope, where it is extracted from the water and recorded on the gated camera. In

this configuration, the image is degraded by all of the environmental noise sources; i.e., attenuation, backscatter and

Figure 3

LLS imagery at 5.6 ALs at reduced laser powers.

Figure 4

Contrast versus range plot for LLS and LRG images.

35 mW Laser Power, 

Positive

12 mW Laser Power, 

Positive

95 mW Laser Power, 

Negative

35 mW Laser Power, 

Negative

12 mW Laser Power, 

Negative

95 mW Laser Power, 

Positive
26 Naval Research Reviews

power-dependence of the image. Targets used in the backilluminated configuration mode included positive and negative Air Force resolution patterns silk-screened on transparent plexiglass sheets, and a "point source" target. The pointsource target consisted of six holes (diameters 8.33, 6.4, 4.0,

2.8, 2.0, and 1.0 mm) drilled in two rows (the two largest

and the four smallest) in a sheet metal plate. The rows were

separated by 232 mm, while the holes were separated by 51

mm.

Figures 6 and 7 show typical data sets from this tank

test. Figure 6 illustrates LRG tank test data at a range of 3-

beam attenuation lengths with front and rear illumination of

the positive and negative targets. The outline of the 30-inch

diameter "back-illumination spot" is clearly evident in the

back-illuminated positive and negative target images. The

lower right hand panel shows imagery of the back-illuminated “point-source" target. Figure 7 provides additional LRG

tank test images at 5.6 ALs. The two top panels are standard

front-illuminated, positive and negative targets. The panels

in the middle line are back-illuminated, positive negative,

and point targets, all at normal laser power. "Normal laser

power" means that the back-illuminating laser power was

attenuated to approximately match the laser power incident

on the front-illuminated targets. (These powers were measured with a radiometer). The Figure 7 bottom three panels

blur/glow/forward scatter noise. Contrast data from this configuration has been included in Figure 4.

In the back-illuminated configuration (path 2), the laser pulse propagates over the tank to the back side of the

target, is inserted into the water by a second periscope, passes

through a diffusion apparatus before back-illuminating transparent targets. In this configuration, backscatter noise is completely non-existent. In addition, the laser beam does not

suffer attenuation due to scattering and absorption by water

on its way to the target, so the intensity of the image leaving

the target plane can be considerably higher than the corresponding image in the front-illuminated configuration. Accordingly, much of the back-illuminated imagery was collected in two modes. In the first mode, the laser pulse was

attenuated so that the laser power leaving the target was approximately the same as the corresponding laser power in

the front-illuminated configuration. In the second mode, the

laser pulse was not attenuated, allowing an evaluation of

Figure 5

Test tank facility.

Figure 6

LRG tank test images, 3-beam ALs.

Figure 7

LRG tank test images 5.6-beam ALs, normal and high power.

Front Illuminated, 

Positive

Front Illuminated, 

Negative

Back Illuminated, 

Positive

Back Illuminated, 

Point Target

Back Illuminated, 

Negative

Back Illuminated, 

Positive, Normal Power

Front Illuminated, 

Negative

Front Illuminated, 

Positive

Back Illuminated, 

Point, Normal Power

Back Illuminated, 

Negative, Normal Power

Back Illuminated, 

Positive,

Normal Power x 25

Back Illuminated, 

Negative,

Normal Power x 25

Back Illuminated, 

Point,

Normal Power x 25
Three/1997 27

minated, enhanced power, LRG tank test imagery and contrasts.

Shortly after the completion of the sea test, a second,

very brief, tank test was conducted which compared the performance of the LRG system in the sea test configuration

with a prototype, enhanced receiver, LRG system. The prototype receiver has a higher quantum efficiency photocathode (approximately 45 percent versus approximately 15 percent) and a lower excess noise factor (approximately 1.1

versus approximately 3). However, these system improvements did not lead to significantly improved imagery under

turbid littoral water conditions. In fact, under most conditions, the sea-test configuration performed at a par with, or

superior to, the prototype configuration. Because this was a

prototype receiver, special caution must be exercised when

drawing conclusions. However, because the system was

tested under turbid water conditions, the system was not

power limited. Rather, its performance was limited by blur/

glow/forward scatter noise, which was not addressed by these

receiver enhancements.

The positive and negative Air Force Resolution Targets provide examples of imagery of high inherent contrast

targets. Because of their high contrast, they are very cooperative targets. Caution must be exercised when extrapolating these results to imaging less cooperative, low inherent

contrast targets.

5. Conclusions

This completed the first direct, head-to-head, performance comparison of LLS and LRG underwater imaging

technologies. The results were an unambiguous appraisal of

their relative merits for the mine identification mission in

turbid, littoral waters. The following conclusions were

reached:

The LRG system demonstrated: (a) successful detection (but not identification) of high contrast targets at relatively long range, (b) insensitivity to ambient light, and (c)

insensitivity to platform motion. However, it had extreme

difficulty identifying or even detecting low contrast targets

even at relatively short ranges. It was found that the contrast

and resolution required for the mine identification mission

was severely limited by blur/glow/forward scatter noise.

The LLS system demonstrated the contrast and resolution required for mine identification under a robust set of

conditions, including: (a) successful identification of high

and low contrast targets, (b) successful imagery at high and

low laser power, (c) successful imagery of tethered targets

in the volume, and (d) successful imagery during day and

night conditions. The system demonstrated effective reduction of blur/glow/forward scatter noise, as well as backscatter noise. Under certain situations, the LLS system became

power limited, suggesting improvements in detector quanare the same as the middle row, except that the back-illuminating laser power was not attenuated, yielding a laser power

leaving the target significantly higher than the "normal laser

power" leaving the front-illuminated, reflective targets. In

this case the laser power was approximately 25 times higher

than normal laser power.

Figure 8 presents contrasts extracted from this data for

the front-illuminated, positive and negative Air Force Resolution Targets and corresponding back-illuminated positive

and negative resolution targets for the back-illuminated targets. The contrasts of the back-illuminated targets are, as

expected, higher than that of the corresponding front-illuminated targets. However, the contrast of the front-illuminated

positive targets is significantly higher than the corresponding contrasts of the back-illuminated negative targets. This

data places significant bounds on the magnitude of the contrast reduction due to backscatter; if the contrast reduction

of the LRG system was mainly due to backscatter, the contrasts of the back-illuminated negative (and positive) targets

should be significantly higher than the contrasts of the frontilluminated positive (and negative) targets. Moreover, the

contrasts of the back-illuminated positive and negative targets should be close to identical. Neither of these is the case.

The data from the "point source" target clearly displays the fact that the LRG system was not power limited

under the conditions of this test. The LRG system had enough

sensitivity and gain to image the point source target at 5.6

ALs; it is blur/glow/forward scatter which limits the system

performance, particularly for the negative targets. In fact,

the system had enough sensitivity and gain to resolve the

8.33- and 6.4-mm point sources at 9-beam attenuation

lengths. Under these conditions blur/glow/ forward scatter

noise rendered even the positive back-illuminated image featureless.

It should also be noted that the LLS imagery and contrasts from the sea test is far superior to even the back-illuFigure 8

Contrast versus range for LRG tank test.
28 Naval Research Reviews

tum efficiency and increased laser power would be beneficial.

Based on these test results, requirements analysis, and

sensor performance modeling, the decision has been made

to base the LVIS sensor on LLS technology. The LVIS sensor has been designed, and is currently being fabricated by

Applied Remote Technology. It is scheduled for initial sea

testing in July/August 1995.

Biography

Michael Strand received a B. Chem. degree from the

University of Minnesota, and a Ph.D. in Chemical Physics

from the University of Chicago. He did postdoctoral research

at the University of Colorado's Joint Institute for Laboratory

Astrophysics from 1977 to 1980. He then taught in the physics and mathematics departments of Texas A&M University,

before joining the staff of the Naval Surface Warfare Center,

Coastal Systems Station in 1987. He is currently specializing in underwater laser-based imaging systems.

REFERENCES

1. J. Leatham and B. W. Coles, “Use of Laser Sensors

for Search and Survey,” Underwater Intervention ’93

Conference Proceedings, 1993.

2. A. Gordon, “Underwater Laser Line Scan Technology,” Underwater Intervention ’93 Conference

Proceedings, 1993.

3. T. J. Kulp, D. Garvis, and R. Kennedy, “Current

Status of the NAVSEA Synchronous Scanning Laserlmaging System,” SPE Proceedings, Vol. 980, 1988.

4. B. A. Swartz, “Diver and ROV Deployable Laser

Range Gated Underwater Imaging Systems,”

Underwater Intervention ’93 Conference Proceedings, 1993.

5. Other definitions of contrast are possible, and may be

more useful for certain purposes. This definition was

used because it supports the separation of the effects

of attenuation and backscatter from those of blur/

glow/forward scatter.

6. Of course, if the signals are sufficiently attenuated,

the image will be lost in the shot noise. Sufficient

power and gain is required. However, shot noise is

not the primary issue for the images under consideration.

7. The LLS images exhibit image distortions caused by

non-uniform platform motions, especially roll

induced by propeller torques when the platform is

accelerating.

8. These images were taken during early evening, when

ambient light levels were low. Comparable images

taken near noon, when ambient light levels were

high, led to significant contrast reductions at the

lowest laser power levels due to ambient light effects.
Three/1997 29

Abstract

Magnetic sensors offer a complementary approach to

active acoustics for shallow water mine reconnaissance and

hunting. The U.S. Navy has developed an approach using a

5-channel tensor magnetic gradiometer to provide enhanced

classification and location in mobile operations beyond the

capability offered by the commonly used single channel total field magnetometer. Buried mine detection and low false

alarm rates were demonstrated using such a 5-channel gradiometer in fusion with acoustic sensors. This sensor featured bulk and wire niobium low critical temperature (low

Tc

) superconducting components cryocooled by liquid helium. Advances in material research and new concepts are

being pursued to enhance opportunities with this 5-channel

gradiometer concept: an advanced low Tc

 superconducting

Advances in the

Magnetic Detection

and Classification

of Sea Mines and

Unexploded Ordnance

Ted R. Clem, Coastal Systems Station, Naval Surface Warfare Center, Dahlgren Division, Panama City, Florida

prototype incorporating all thin film niobium superconducting components to demonstrate increased detection range in

the Joint Countermine Advanced Concept Technology Demonstration, a high Tc

 superconduting concept using liquid

nitrogen refrigeration to reduce package size and cryogenic

support requirements, and a room temperature fluxgate prototype for man-portable applications where shorter detection ranges are useful. Recent experimental results using

this technology have been obtained to demonstrate an enhanced capability for the detection of unexploded ordnance

for environmental cleanup. In this paper, these recent advances in sensor development and the new testing results

will be reviewed.
30 Naval Research Reviews

I. Introduction

Magnetic sensors have proven merit for mobile area

surveys and search operations conducted from air, land or

sea including application for the detection, classification, and

localization of sea mines, unexploded ordnance (UXO), and

chemical, biological and nuclear waste [1]-[4]. In fact, in an

assessment conducted by the Jet Propulsion Laboratory for

the Army Corps of Engineers, magnetic sensors in general

were identified among the most useful sensors for UXO detection and localization, and superconducting gradiometers

were specifically identified as the most useful tool in a class

by themselves [5]. For applications in sea mine countermeasures, we can envision operational scenarios in which longrange detection is required for reconnaissance and hunting

in preparation for an amphibious assault or shorter-range

detection is required for diver mine detection and avoidance

(Fig. 1).

In Sections I.A. and I.B., we shall describe two types

of sensors which detect magnetic anomalies: sensors which

detect changes in the local magnetic field, magnetometers;

and sensors which measure the spatial derivatives of magnetic field, (first order) gradiometers.

A. Magnetometers

The performance of a magnetic sensor is measured by

its detection range, which is a function of its configuration

and sensitivity and the magnetic moments of the targets of

interest. In the far field, a target can be well approximated as

a magnetic dipole. In this approximation, relatively simple,

analytic expressions can be written to relate sensitivity requirements in terms of nominal values for target magnetic

moment and range.

For the special case in which a circular, connected conducting loop with area A is carrying an electrical current I,

we can define the magnetic moment of the loop as the vector

m IAn = ˆ where nˆ is the normal to the loop in the direction

defined by the right-hand rule for positive current. The International System (SI) unit for magnetic moment is amperemeter-squared (A-m2

). The magnetic induction variations in

the Earth’s field, without the use of very sophisticated for a

magnetic dipole can be written as

B m rr

r

m

r = μ ⋅ − 



 



 0

5 3 4

3

π

( ) (1)

The concept of magnetic moment m can be generalized for an arbitrary magnetic body and the magnetic induction of the body will approach the result of (1) in the far field

In air, the magnetic induction B is related to the magnetic

field H via B=μ0

H. Hereafter, B will be used exclusively

and will be referred to as the magnetic field. The following

units for magnetic field will frequently be used to represent

sensor sensitivities: nanotesla (1 nT=10-9T), picotesla

(1 pT=10-12T), and femtotesla (1 fT=l0-15T).

A number of sensors, notably fluxgate magnetometers

and superconducting magnetometers, measure the individual

vector components of field. A three-axis vector magnetometer, likely using fluxgate or superconducting sensors, is very

useful for localization (providing three channels of information). For stationary applications in geophysics and barrier

defense, such sensors are effective. However, to date, these

sensor types have not proven effective for mobile applications, since means to compensate the anomalous signals arisFigure 1

Operationals scenarios for the magnetic detection and classification in sea mine countermeasures: (a) operation onboard

an unmanned underwater towed vehicle towed behind a semi-submersible remotely-operated vehicle for reconnaissance

and hunting and (b) diver-portable operation for diver mine detection and mine avoidance.

(a) (b)
Three/1997 31

ing from rotations in the Earth’s magnetic field have not been

devised.

Other magnetometers, notably those based on nuclear

or atomic resonance processes, measure the magnitude of

the total magnetic field and are known as total-field magnetometers. Let B0

 denote the magnetic field of the Earth, and

let b denote the induction generated by an anomaly. If

|b| << |Bo|, then the signal observed by a total field sensor

(referenced to the baseline Earth field) is

b B bB

B m ≡ + − = + •+ − ≈ • B bB Bb B b 0 00

2

0

2

0

0

0

2 (2)

As a result of the right-hand approximation above, a totalfield magnetometer does not simply measure the magnitude

of the magnetic-field anomaly, but measures instead the projection of that anomalous signal onto the earth’s field

Total-field magnetometers have provided the generally accepted method for magnetic anomaly detection. In

particular. the AN/ASQ-81 and its successor the AN/ASQ208 are total-field magnetometers utilized by the U.S. Navy

for submarine detection from airborne platforms. A major

advantage of this type of sensor is its insensitivity to rotation in the Earth’s background field of 50,000 nT (since total

field is a rotational invariant).

Measurements by total field magnetometers are difficult to interpret because these sensors effectively measure

the projection of the anomalous magnetic field vector onto

the Earth’s magnetic field instead of the total field. Interpretation often requires an experienced operator, and precise

anomaly locations ate difficult to obtain. Since total field

magnetometers provide only one channel of information, they

lack valuable target vector information. In particular, they

provide very limited localization and little capability for

anomaly classification through moment determination. Moreover, these sensors are limited in field operation to sensitivities at levels approaching 0.1 nT as a result of geomagnetic

noise, i.e., temporal variations in the Earth’s field, without

the use of very sophisticated compensation schemes.

B. Gradiometers

The gradient of the magnetic field (in standard MKS

units of T/m) is a second-order tensor with components given

in Ref. 6 by

 G B

x ij

i

i

≡ ∂

∂

=− • − { } ( ) − + ( ) 3

4 7

2 2

r m r rr r r rm rm c

i j ij i j j i

μ

τ

δ (3)

As a result of Maxwell’s equations in free space, only 5 of

these 9 tensor elements are independent. For this reason, (first

order) gradiometers are typically designed with 5 independent gradient channels, using the minimum number which

permits characterization of the local tensor gradient field.

It is feasible to determine the bearing vector and the

magnetic-moment vector direction of the dipole by inversion of the gradient equations at a single point only [6]. More

recently it has been shown that the addition of gradient rate

information at a single point leads to a unique solution for

dipole position and moment vector [7].

The contradiction of the gradient tensor defined by

Figure 2

Gradiometer sensor concepts including (a) a single SQUID-based gradiometer channel and (b) a conceptually simple 5-

channel gradiometer configuration capable of magnetic dipole localization and moment classification (with 3 orthogonal

magnetometers for motion compensation).

(a) (b)
32 Naval Research Reviews

G GGij ij

i j

≡ = =

∑∑1

3

1

3

(4)

is a rotational invariant associated with the gradient tensor

analogous to the magnitude of the field vector. This quantity

may prove very useful for applications in which a gradiometer is subjected to large rotations during the period of measurement, e.g., hand-held operation (in contrast to straight

runs onboard stabilized platforms) [6].

An example of one configuration to measure a singlegradient tensor component and a simple configuration to

measure 5 independent gradient components are displayed

in Figure 2. Each gradient tensor component is measured by

a spatially separated loop pair connected in a common-mode

rejection configuration. 3 vector magnetometers are included

in the 5-channel gradiometer displayed in Figure 2(b) to compensate for the residual magnetometer signals in the gradiometer channels that arise as a result of manufacturing imperfections in the gradiometer loops.

Gradiometers offer the potential to remove many of

the limitations associated with magnetometers because the

output of a gradiometer is typically produced by twin magnetometers operating in differential mode. In particular, this

configuration provides common-mode rejection of the nominal 0.1-nT temporal variations in the Earth’s field and of the

nominal 1000-nT field changes arising from typical 1 degree sensor rotations while in towed motion.

Gradiometers may be fabricated using many available

magnetometer technologies. Available fluxgate and total-field

magnetometers can perform at levels approaching 1-10 pT,

while superconducting magnetometers utilizing Superconducting Quantum Interference Devices (SQUIDs) can perform at levels on the order of 1-10 fT. The extreme performance available from superconducting magnetometers provides a capability to fabricate gradiometers with high sensitivity having short baselines. The short baseline provides a

compact package with extreme coherence between the magnetometers. The coherence is required to maintain high performance when in motion, and the compact package is amenable to implementation aboard one vehicle. In addition, with

the compact sizes, a large number of gradient channels can

be integrated into a small package to obtain complete position and moment determination of a magnetic dipole target

at a single point in space.

C. The Superconducting

Gradiometer/Magnetometer

Sensor

Almost all of the efforts with SQUID sensor technology have dealt with sensors inside a very controlled laboratory environment, and to a more limited extent outside the

laboratory at stationary locations, notably for geophysical

measurements. During the 1 980’s, the Coastal Systems Station (CSS) developed the Superconducting Gradiometer/

Magnetometer Sensor (SGMS) specifically for mobile operations outside the laboratory environment (Fig. 3). The

sensor employs largely niobium bulk and wire superconducting technology (with thin-film Josephson Junctions), features

dc SQUIDs housed inside superconducting shields, and is

convectively cooled to 4 degrees Kelvin by helium gas evaporating from a liquid helium reservoir. Under some field conditions, the SGMS has attained sensitivities on the order of

1pT/m-Hz1/2 at 0.1 Hz. In comparison total-field gradiometers (with a 0.3-meter baseline) specifically designed for

operation onboard a moving platform have achieved sensi-

(a) (b)

Figure 3

Major subassemblies in the SGMS package: (a) dewar and (b) sensor probe unit.
Three/1997 33

MAGNETIC MOMENT (A-m2)

10 dB SIGNAL-TO-NOISE RATIO

MAGNETOMETER (nT)

GRADIOMETER (nT/m)

RANGE

(m)

tivities on the order of 30 pT/m-Hz1/2 at 0.1 Hz and a fluxgate

gradiometer described in Section VII.A has achieved a sensitivity on the order of 300 pT/m-Hz1/2 at 0.1 Hz [1], [2].

The CSS initiated the Magnetic and Acoustic Detection of Mines (MADOM) Project starting in 1985. This

project successfully demonstrated the value of magnetic and

acoustic sensor fusion for mine reconnaissance and hunting.

The SGMS demonstrated, for the first time, high sensitivity

and rugged, robust, and reliable performance of a superconducting gradiometer operating outside the laboratory environment onboard a towed underwater vehicle with sea testing conducted for a period of 7 years. Gradiometer operation was automated (with the exception of semi-automated

initial tuning) and fully-automated, real-time magnetic detection and classification signal processing was demonstrated

to provide effective and accurate moment determination and

localization for single and multi-target cases [1], [2].

D. Quantitative Comparison of

Magnetometers and

Gradiometers

The signal strength of a magnetic dipole decreases as

the third power of the range for magnetic fields and as the

fourth power of the range for magnetic field gradients. The

approximate ranges of magnetometers and gradiometers are

displayed in Figure 4 as functions of dipole strength and sensor sensitivity. It can be shown that the sensitivity requirements for a magnetometer and a gradiometer, respectively,

to have the same detection range r against a given dipole

target, is given by the approximate relation Ng/Nm ~ 3/r. For

example, the detection of a mortar shell with a magnetic

moment of 0.1 A-m2 at a range of 3 m, requires a magnetometer with sensitivity of 0.36 nT or a gradiometer with sensitivity of 0.36 nT/m (given a 10-dB signal-to-noise ratio for

both cases).

It should be noted that the high rate of signal reduction with the fourth power of distance in the case of a gradiometer represents an apparent shortcoming for a gradiometer configuration. We believe that the ability to develop gradiometers with sensitivity greater than 1x10-3 nT/m for mobile operations and the extreme difficulty in utilizing magnetometers with sensitivity greater than 0.1 nT in mobile

operations significantly outweighs this shortcoming. Moreover, the fourth power reduction of detection range with

moment for a gradiometer has merit for the detection of targets with relatively small moments. For example, a 3 pT/m

gradiometer can detect an individual 500-pound bomb (with

a moment of 30 A-m2) at a range of 33 m and a 60-mm mortar shell (with a moment of 0.1 A-m2

) at a range of 8 m.

Hence there is only a factor of approximately 4 reduction in

detection range for a 60-mm mortar shell compared to the

500-pound bomb although there is a factor of 300 in reduction of magnetic moment.

An example of the ease of interpretation for 5-channel

gradiometer data compared to single-channel total-field

magnetometer data is displayed in Figure 5. Magnetic profiles have been generated for a 60-mm mortar shell buried 1

Figure 4

Approximate ranges (in meters) of magnetometers and

gradiometers as a function of target strength in terms of

magnetic moment (in units of A-m2). Curves are given for

different sensor sensitivities (in units of nT for magnetometers

and nT/m for gradiometers assuming a 10 dB signal-to-noise

ratio in both cases).

NORTH-SOUTH (FEET) NORTH-SOUTH (FEET)

10 dB SNR gr=1nT/m 10 dB SNR gr=1nT/m

tf=0.79 nT

tf=0.79 nT

az=45 deg

pol=45 deg

M=0.1 A-m2

az=45 deg

pol=45 deg

M=0.1 A-m2

EAST-WEST (FEET)

EAST-WEST (FEET)

Figure 5

A comparison of magnetometer and gradiometer capabilities

for target localization. The profiles measure tf = total field

(Equation (2)) and gr = gradient magnitude (Equation (4)).

Profiles are given for a magnetic dipole centered at origin

with (a) a general orientation and (b) orientation in an eastwest direction. Observe that the gradiometer profiles are

approximately circularly symmetric about the dipole’s location

so that “gradient” searches normal to the gradient magnitude

profiles are meaningful for the gradiometer. In contrast, the

magnetometer profiles are not amenable to such

straightforward interpretation.
34 Naval Research Reviews

meter under ground for two different orientations with respect to the Earth’s background field. In this example, the

magnetometer profiles and gradiometer profiles are given

by the anomalous total field, Eq. (2), and the corresponding

changes in magnitude of the gradient tensor, Eq. (4), respectively. The complex total field profiles require precision data

and critical interpretation to localize dipole sources. The symmetric gradiometer profile leads to straightforward interpretation convenient for gradient searches for dipole localization.

II. Scenarios for

Gradiometer Mobile

Operation

We can envision three general types of operational

scenarios: relatively long-range rapid surveys for target clusters, more moderate-range searches against individual targets, and detailed close-in surveys. The selection of magnetic sensor type will largely depend on these operational

requirements, determined primarily by the desired detection

range, which is, in turn, a function of the magnetic moment

of the targets and of sensor sensitivity. The selection also

depends on such factors as financial budgets, logistical support and technical expertise of the operators. To date, magnetic sensor approaches have provided limited localization

and mapping capabilities. To gain widespread acceptance,

approaches must be introduced which provide accurate localization and target classification, and which lend themselves

to straightforward interpretation and minimal training. Performance must not be limited by magnetic noise from the

host platform and other subsystems. For land-based operations, the system must be capable of operating over rough,

overgrown terrain. The sensor and associated signal processing also must deal effectively with environmental noise.

A. General Considerations

Long-range rapid surveys conducted from an aircraft

have been proposed for initial surveys to locate clusters of

UXO targets [4]. The mine reconnaissance/hunting demonstration of MADOM represents an example of a moderaterange search. Land-based manual surveys or diver operations for mine detection or avoidance provide examples of

close-in surveys. High sensitivity will be critical for the longrange and moderate-range scenarios, but such sensitivity will

likely be sacrificed for the close-in surveys at ground level.

In fact, local geologic noise limits a gradiometer’s noise floor

to levels on the order of 0.05 up to 10 nT/m-Hz1/2 which may

limit the use of high-sensitivity gradiometers such as the

SGMS for some land-based operations.

A perspective on the role of higher sensitivity gradiFigure 6

Two modes of operation for 5-gradient channel sensors: (a) wide-area surveys using high sensitivity sensors and (b) shortrange searches for single targets using less sensitive sensors.

(a) (b)
Three/1997 35

ometers used for wide-area searches and lower sensitivity

gradiometers for close-in surveys can be obtained from the

following example (Fig. 6). A 3 pT/m gradiometer can detect a grouping of twenty 500-pound bombs (clustered in a

circle several meters in diameter) at a range of 46 meters. An

area search rate of 1 km2

/hr can be obtained when the sensor’s

altitude is 15 meters moving at a forward speed of 15 km/hr.

When deployed from the ground, a less sensitive 300 pT/m

gradiometer would provide detection ranges of 10 meters

and 3 meters for the detection of a 500-pound bomb and a

60-mm mortar shell, respectively.

Two fundamental approaches for operation of a 5-channel tensor gradiometer stand out. First it will often be convenient to conduct straight-line searches at constant velocity

for airborne and underwater vehicle operation and also for

some land-based surveys if the terrain admits straight-line

trajectories. For other cases, including many man-portable

land-based and diver operations, it will be unreasonable to

expect controlled operator motions along straight-line trajectories at constant velocities. In either case, we can implement signal processing using point-by-point inversion of the

motion-compensated gradiometer signals. For the special

case of straight-line searches, we can also utilize least-squares

fit to the time series in order to improve target detection.

One approach using point-by-point inversion is described in

Section II.B and one approach using a least-squares fit is

described in Section II.C below.

B. Localization using Point-byPoint Signal Inversion

As mentioned above, the five independent gradient tensor components at a single point can be used to construct the

bearing vector to a dipole source, and a scaled moment vector with the same direction as the dipole moment vector and

a magnitude given by m/r4 [6]. The difficulty with this inversion is that there are multiple solutions: two nontrivially related solutions in a given half space, and two additional solutions obtained by reflection of the first two through an origin centered on the gradiometer. This multiplicity of solutions has limited the practical application of this algorithm.

More recently, Wynn [7] has investigated the use of the rate

of change of the gradient tensor components. and their role

in resolving the scaling and uniqueness issues associated with

the gradient tensor inversion.

For a sensor with specified translational velocity v, the

time rate of change of the gradient tensor components has

the form

G r ij = 3

4

0

9

μ

π

{35(m•r)(v•r)rirj - 5r 2[(m•v)rirj

+(m•r)(virj+vjri) + (v•r)(mirj+mjri)

+(m•r)(v•r)δij] + r 4[mivj + mjvi + (m•v)δij]} (5)

These equations have been inverted to give multiple

solutions for the bearing vector and a scaled moment vector

in the direction of the dipole moment vector with a magnitude given by m/r5

. The solution for bearing vector and moment vector direction common to the two inversions is

unique, and the different scaling for the scaled moments in

the two inversions yields the range to the dipole, resulting in

a unique solution for m and r on a point-by-point basis. In

practice, it is not necessary to specify a sensor velocity. All

that is needed is knowledge of the position of the gradiometer relative to the Earth’s reference frame. Work is ongoing

to apply these algorithms to the practical interactive localization of buried UXO by means of a man-portable tensor

gradiometer.

C. Localization using a TimeSeries Least-Squares Fit

A mathematical model for detection, classification, and

localization (D/C/L) of multiple stationary magnetic dipole

targets using a gradiometer (with 5 independent tensor gradiometer channels appropriately selected and 3 orthogonal

magnetometer channels) moving in a straight line trajectory

past the targets at a constant speed has been developed and

validated in the MADOM project. The 5 output signals Si(t)

(i=1,2,....5) from the 5 independent gradiometer channels and

the 3 magnetic field components Bl(t) (l=1,2,3) are measured

as a function of time as the sensor moves past the targets.

The time derivatives of field dBl(t)/dt are calculated from

the B1(t) for eddy-current compensation. The objective of

this model is to extract the dipole signals Gij

(k)(t)(k=1,2,...,n)

for the unknown number n of dipole targets and then to determine the magnetic moments and the positions of the n

targets.

The model describes the signals Si in the 5 gradiometer channels by the equations

St c G t Bt Bt vt i ij ij

k

k

n

i il l

j l

il l

l

i ( ) = ( ) 











+ + ( ) + ( ) + ( ) ( )

= = = =

∑ ∑ ∑ ∑ 1 1

3

1

5

1

3

αβ γ

(6)

where c is the pre-determined calibration matrix for

the gradiometer, νi(t) and αi are uncompensated noise (setting the noise floor of the channel compensation parameters

per channel) and channel biases, respectively, and βil, and γil

are the balance and eddy-current vectors for channel i. An

iterative analysis first estimates the α’s, β’s, γ’s, (a total of

35 parameters) and then executes a gradient search for the

location of the single target that best fits the residual signal.

The α’s, β’s, γ’s, and target location and moment are then

optimized, and the procedure is repeated for a second target.

Targets continue to be added until finally no target can be

found whose signal contributes substantially to a reduction
36 Naval Research Reviews

in total signal power, at which point the algorithm terminates.

Fig. 7 displays the motion-compensated signals obtained from the gradiometer and the information extracted

from the algorithm in detecting a 1000-lb Mk83 bomb. The

five gradiometer signals are displayed for a 45-m section of

data with the bomb located 15 m to port and 5 m below the

gradiometer at the closest point of approach. The measured

and predicted target location, moment, and orientation are

also displayed to indicate the capability of the algorithm to

provide good fits to the data.

III. Mobile Underwater

Debris Survey System

A project has been initiated to develop and evaluate a

Mobile Underwater Debris Survey System (MUDSS) capable

of finding and accurately mapping the locations of UXO ranging from small shells to large bombs in water depths of from

4 to approximately 100 feet in coastal regions at formally

used defense sites [3]. The effort involves a collaboration

between CSS and the Jet Propulsion Laboratory [8]. In addition to the application of UXO detection at coastal sites utilizing underwater towed sensor suites as described in this

section, the use of a superconducting gradiometer in a system concept for rapid airborne reconnaissance and survey of

UXO sites has been proposed [4].

A. General Project Description

The MUDSS project is divided into two phases. Phase

I, which ran for one year and culminated in an at-sea feasibility demonstration of a multi-sensor MUDSS prototype

against UXO in a drill target field The feasibility demonstration was successfully executed in August and September

of 1995 in St. Andrews Bay (near Panama City, FL) with the

SGMS utilized for magnetic detection. Phase II will culminate in 1997 in a technology demonstration consisting of a

UXO survey at a yet-to-be-determined formally used defense

site.

Figure 8 depicts the first version of the MUDSS system which was fielded in 1995 for the feasibility demonstration. The surface craft is a custom designed magnetically

and acoustically quiet. shallow draft, trailerable catamaran.

Figure 7

The five gradiometer signals in a 45-m section of data on a

pass near a Mk83 1000-lb bomb.

Figure 8

The Mobile Underwater Debris Survey System: (a) artist’s concept detailing key features of the system and (b) photograph

of system in transit to test site with the dead-weight depressor housed out of water for speed and manueverability.

Signal (nT/m) Cross-Track Position (m)

Along-Track Position (ft)

= TARGET POSITION AND MOMENT VECTOR

= ESTIMATED TARGET POSITION AND MOMENT VECTOR

(a) (b)
Three/1997 37

250M

100M

A dead-weight depressor is suspended off of the back of the

catamaran to maintain the appropriate depth for sensor operation and to house part of the sensor suite (a RESON Seabat

ahead looking sonar, a CSS-developed high frequency

sidescan sonar, and a Raytheon-leased LS 4096 laser

linescanning electro-optic sensor. A second neutrally buoyant tow body trailing the deadweight depressor houses a CSSdeveloped low frequency synthetic aperture sonar and the

SGMS.

Reference 3 gives a detailed description of the feasibility demonstration test field layout, the testing procedures,

and the performance of the acoustic and electro-optic sensors. Specific details relevant to the gradiometer demonstration are given here.

B. Gradiometer Results from

the Feasibility Demonstration

A linear target field was laid out to evaluate the SGMS

performance. The field consisted of a row 200 m in length of

eight small and medium-sized targets (mortar and artillery

shells ranging in caliber from 60 up to 203 mm) running

north-south; a second shorter row of three medium-sized targets (two oil drums and a Mk82 500-lb bomb) parallel to,

and 9 m east of, the first row; and a third row of two targets

(a Mk83 1000-lb bomb and a Mk84 2000-lb bomb) 9 m east

of the second row. A marker and a sonar calibration panel

with ferrous anchors were laid at one end of the linear field.

Estimates of the magnetic moments for the targets obtained

from the D/C/L algorithm described in Section II.C are tabulated in Table I. These magnetic moments ranged from 0.03

A-m2

 for one 60mm mortar shell up to 120 A-m2 for the

2000-lb bomb.

The detection range for these targets is also tabulated

in Table I. Absolute range is given for a gradiometer with

sensitivity of 3 pT/m-Hz1/2 at 0.1 Hz assuming a 10 dB signal-to-noise ratio and relative range is given for fixed gradient sensitivity (normalized to 1 for a 60mm shell with magnetic moment of 0.03 A-m2

). Observe that the 2000-lb bomb

is detectable at 8 times the range of the 60mm mortar shell.

The predicted results from the D/C/L algorithm to estimate location and to classify the targets according to magFigure 9

Gradiometer targets found in one run over the linear field with the open circles ° indicating the actual target locations and the

solid circles • indicating the positions predicted by the D/C/L algorithm. The size of the solid circles • indicates the predicted

magnitude of the target’s magnetic moment.
38 Naval Research Reviews

netic moment are displayed in Fig. 9 for one pass of the system through the target field. Open circles designate the actual location of the targets, while the solid circles indicate

the predicted positions. The size of the solid circles indicates the magnitude of the targets’ magnetic moment. For

the feasibility demonstration, the nominal performance of

the gradiometer channels was on the order of 3 pT/m-Hz1/2

at 0.1 Hz. The D/C/L algorithm was effective in localization

and classification (by moment magnitude) all of the targets

in this pass with the exception of the 1000-lb Mk83 bomb.

This exception provides an example of multi-target localization for which there are 2 targets (the 500-lb bomb and

the 105mm shell) in this data window in addition to the 1000-

lb bomb. In this case the algorithm successfully localized

the 500-lb bomb and the 105mm shell with high signal

strength, but failed to localize the more distant 1000-lb bomb

with a relatively weak signal strength. The ferrous anchors

for the marker and calibration panel at the right hand side of

the map and two clutter objects in proximity to the 106mm

shell (not a part of the target set) were also detected during

this run. Double detections are displayed in Figure 9 for two

targets, the 500-1b bomb and one of the two oil drums. These

represent target detections by the D/C/L algorithm in two

separate data segments. The high degree of overlap for the

double detections is suggestive of the degree of accuracy

obtained with this algorithm.

IV. Clutter Rejection

Generally active acoustic approaches have proven to

be an effective means to detect, classify and localize tethered sea mines or bottom mines proud with respect to the

bottom in deeper waters. However the shallow-water bottom mine environment is an especially difficult acoustic environment in which to operate. Interfering reverberations

from the air/sea and sea/bottom interfaces, bottom topographical features, general harbor debris, and mine burial

present a difficult acoustic environment for bottom mine

detection. In coastal regions, the density of debris clutter may

lead to a high false-alarm rate using conventional imaging

sonar approaches alone.

For effective clutter rejection, it is very desirable to

use distinctly different sensor approaches. The application

of two or more collocated sensors operating simultaneously

has the potential to reduce false alarms and provide robust

detection in a wide variety of background conditions. For

mine reconnaissance and hunting, the combination of magnetic sensors with sonars provides such an alternative. In the

MADOM sea testing, more than 90% of the acoustically

mine-like clutter was not magnetically mine-like.

Several investigations have been conducted recently

using automated neural network approaches to assess the

merit of magnetic and acoustic data fusion [9], [10]. The

following result was obtained courtesy of L. Smedley and

G. Dobeck [10]. A set, consisting of 215 sonar images containing an assortment of drill targets and clutter objects, was

assembled using data collected from sea tests with the SGMS

and the MADOM low frequency synthetic aperture sonar.

For each sonar image, the magnetic detections were co-registered. An attractor-based k-nearest neighbor neural network

was developed using the magnetic and acoustic features given

in Table II.

Receiver operating curves (ROCs) for this data were

established for different detection ranges, recognizing the

fact that the two sensors are effective over different ranges.

The ROC for the individual sensors and for the two-sensor

data fusion (obtained from this neural network) are displayed

in Fig. 10 for tracks on both sides of the vehicle in a range

where both sensors were effective. For these tracks, there

were 93 drill targets in the 215 images.

We observe in this figure the improved detection and

classification obtained using the data fusion. For an acceptFigure 10

The probability of detection and classification for acoustic and

magnetic sensors alone and for neural-network fusion data

from both sensors given as a function of the number of false

alarms per image in the two tracks from 10 to 27 meters on

either side of the vehicle.

Magnetic Only

Acoustic and Magnetic Fusion

Acoustic Only

Number of False Alarms per Image

Probability of Detection and Classification
Three/1997 39

able rate of 0.1 false alarms per image, the probability of

detection and classification was 0.5 for the acoustic sensor

alone and 0.77 for the magnetic sensor alone. The probability of detection and classification increased to 0.9 for a rate

of 0.1 false alarms per image using this neural-network data

fusion. Although these results were taken from a small data

set of 215 images, we believe that the trend clearly demonstrates the substantial benefit of magnetic and acoustic data

fusion for shallow water mine reconnaissance and UXO survey.

V. The Thin-Film

Gradiometer

We believe that the current technology, represented by

the SGMS sensor, is reaching its performance limit. This

technology is largely characterized by the use of bulk and

wire niobium (Nb) superconducting components. Advances

in Nb thin film technology to obtain increased low frequency

sensitivity and the relative simplicity of the thin-film processing in contrast to labor-intensive assembly of bulk

SQUID packages and the hand winding of wire loops are

appealing. For mobile applications, the greater intrinsic balance, i.e., common-mode rejection of the Earth’s magnetic

field, obtained from thin-film lithography compared to

manual winding of wire loops and the removal of bulk magnetic components, including superconducting diamagnetic

components such as shield canisters, is important to reduce

anomalous signals in the gradiometer sense loops arising from

acceleration-induced relative motion of parts. A project to

develop a high sensitivity, all thin-film gradiometer sensor

for mobile deployment is being pursued by the CSS, IBM

Research [11], Ball Aerospace [12], Lockheed-Martin [13],

Quantum Magnetics [14], and the Naval Research Laboratory [15] for demonstration in the Joint Countermine Advanced Concept Technology Demonstration.

A. Cryogenic and RoomTemperature Electronics

Major advances in Nb thin-film fabrication technology has led to the development, for the first time, of high

quality low frequency SQUID based magnetic sensors utilizing Nb-AlOx

-Nb tri-layer technology on a 5” scale. This

work has led to the production of totally unshielded gradiometers which have been successfully demonstrated to operate in the Earth’s magnetic field.

A cryogenic probe assembly for high performance in

mobile operation has been manufactured with 3 tensor gradiometer circuits mounted on a single-crystal silicon rod and

mounted to the dewar neck plug (Fig. 11). The gradiometer

circuits consist of 2 counterwound magnetometer loops, each

3.8 cm square with a baseline of 5.3 cm, monolithically

coupled to the SQUID. The precision lithography in conjunction with a configuration in which the sense loops, the

SQUID washers, and their modulation and feedback coils

are all pattered as gradiometers has provided extreme balance in order to maintain full sensitivity in the presence of

field changes on the order of 1000 nT.

A benchtop version of high frequency flux-lock loop

(FLL) feedback electronics with a modulation frequency of

16 MHz, a factor of 15 to 30 times the current frequency

available with commercial electronics, has been developed

in order to assure specified signal-to-noise ratios required

from the all thin-film gradiometer channels (using air-core

thin-film output transformers in place of wire-wound ferrous-core transformers) and to provide a high bandwidth for

electromagnetic interference immunity [16].

B. The Advanced Liquid Helium

Dewar

A dewar prototype, referred to as the Advanced Liquid Helium Dewar, was developed to assure that the dewar

would not limit sensor performance (Fig. 12). A flexible design approach supported by detailed thermal, mechanical and

field calculations was pursued. Stringent material selection

and magnetic screening standards were established. The

materials typically were chosen to be as magnetically clean

as possible, with residual magnetizations 10 orders of magnitude smaller than that for soft steel. The magnetic gradient

stability and the eddy current stability are 100 times better

than the preceding state-of-the-art established with the

SGMS.

An exchange gas cooling approach was pursued in

place of the convective cooling utilized in the SGMS dewar.

Figure 11

The TFG Cryogenic Probe Assembly.
40 Naval Research Reviews

New innovations for thermal management have been implemented - anti-slosh baffles, thermal filters to isolate sensor

area from bath temperature fluctuations, aluminized-mylarblankets for radiation shielding custom etched to reduce the

eddy currents, and thermal networks of 99.999% pure aluminum wire for temperature uniformity. Temperature stability in motions typical of tow operations is on the order of 1

μK/Hz1/2 at 0.1 Hz a factor three orders of magnitude better

than for the SGMS dewar [17].

C. Field Testing of the

Integrated Sensor

The integrated sensor prototype has been evaluated

recently under field conditions. For this testing, the benchtop

room temperature FLL electronics (which have a significant

magnetic signature) are operated off a 15-m cable outside

the test facility (remotely positioned some 13 m away from

the sensor so as not to limit the performance of the sensor in

motion). Stationary measurements have been conducted in

this test setup in order to establish a baseline for the motion

testing; i.e., to quantify any deterioration of performance in

motion. In this configuration for the sensor, white noise on

the order of 50 fT/m-Hz1/2 (10 μΦ0/Hz1/2) has been demonstrated. The knee for l/f noise occurred at approximately 1

Hz and the noise floor rose to approximately 200 fT/m-Hz1/2

(40 μΦ0/Hz1/2) at 0.1 Hz. The nominal balance of the gradiometers is estimated at 1xl04

/m (as measured indirectly from

a comparison of compensated and uncompensated motion

spectra obtained in preliminary motion testing), a factor of

at least 20 greater than obtained with the wire-wound SGMS

loops without the trimming procedures required for wire

loops. The dynamic range of the gradiometers is 6x106

.

For comparison, these electronics evaluated in a laboratory environment (integrated with a preliminary version

of the gradiometer circuit currently being evaluated) operating off a 2-m cable has white noise on the order of 4 μΦ0

/Hz1/2

with about 1/3 of the noise from the preamplifier. The knee

for l/f noise also occurred at approximately 1 Hz and the

noise floor rose to approximately 7 μΦ0/Hz1/2 at 0.1 Hz. The

electronics demonstrated a very high closed loop bandwidth

exceeding 2.5 MHz and a very high slew rate greater than

lx106Φ0/sec at frequencies up to 1 MHz [16]. In comparison,

the SGMS has a bandwidth of 100 kHz and a slew rate of

2.5x104Φ0/sec.

D. Development of a FieldDeployable TFG

The existing TFG is being upgraded to a ruggedized

field-deployable version. A fully-populated 5-channel cryogenic probe assembly is being developed to replace the 3-

channel laboratory unit. A compact field-deployable roomtemperature electronics package is being developed to replace the benchtop FLL electronics currently utilized for the

laboratory version and to provide automated sensor control

signal digitization, and data linking. Miniaturization of the

electronics into a single integrated unit mounted onto the

sensor (as required to obtain sensitivity of the integrated sensor in motion) represents a major undertaking. The entire

analog and digital electronics for 5 gradiometer and 3 magnetometer channels is being packaged into a unit 43 cm in

diameter and 56 cm in length. The package must have magnetic signature consistent with the sensitivity requirements

in motion, power reduced by a factor of 30 compared to the

laboratory prototype, and dimensions to minimize the length

of the sensor body section for underwater deployment as

much as possible. Production is in progress with final assembly and testing to be completed in 1997 for integration

in an underwater tow system for mine hunting, specifically

for demonstration in the Joint Countermine Advanced Concept Technology Demonstration, and for UXO surveys.

VI. High Tc

Superconducting

Technology

As a result of nitrogen cooling, the development of

sensors utilizing the high-Tc

 materials with nitrogen cooling

provides an opportunity for significant size reduction, an ease

of maintainability and convenience in comparison to the lowTc

 technology with helium cooling, factors critical to gain

widespread acceptance of the superconducting technology

over other magnetic sensor approaches.

Figure 12

The Advanced Liquid Helium Dewar.
Three/1997 41

A. Perspective on Nitrogen

Cooling for Naval Operations

A broad-based assessment of refrigeration technology

including liquid, solid, and triple-point nitrogen dewars and

active cryocoolers was conducted [1], [18]. The conclusion

from this assessment is that liquid nitrogen dewars represent

the best choice for near-term development of a high performance high Tc

 superconducting gradiometer for mobile applications. The design for an open (vented) liquid nitrogen

dewar with dimensions 45 to 75 cm in length and 30 to 50

cm in diameter has been established consistent with sensitivity goals (Fig. 13). These dimensions are consistent with

available space in underwater tow bodies of interest for U.S.

Navy applications. A final choice for dimensions in any final dewar design would be based on a tradeoff between space

and hold time. Results of the concept analysis indicate that a

dewar with dimensions of 43 cm in diameter and 75 cm in

length would have a hold time of approximately 33 days.

This hold time is over 6 times greater than the hold time for

the Advanced Liquid Helium Dewar which has the same diameter but is 150 cm long, twice the length of the nitrogen

dewar.

The benefits for naval mobile applications which can

be obtained from these reduced cryogen requirements include: (1) a significant reduction in down time during operations; (2) affiliated reductions or elimination in labor requirements for cryogen support during critical phases of an operation; (3) reduced failures in the cryogenic circuits or in

the dewar (such failures typically occur during cryogen recycling); and (4) the elimination of an additional footprint

on ship deck required for helium storage. In addition, the

use of liquid nitrogen in place of liquid helium significantly

reduces supply logistics as a result of the wide availability

of nitrogen on the market at domestic and most foreign ports

and the availability of a large number of liquifiers in the U.S.

Fleet, with at least 54 units identified. Significant cost savings are expected from reduced costs for cryogen supply.

B. Device Development under

this Project

Since 1993, there have been a number of laboratory

results reported on magnetometer prototypes with white noise

better than 200 fT/Hz1/2. A number of test samples, magnetometer circuits and gradiometer circuits have been developed in conjunction with this project. This included a report

of 26 fT/Hz1/2 at 1 Hz for a 2x2-cm magnetometer [19]. As

an element of this program, the impact of flux trapping for

unshielded operation of high Tc sensors in the Earth’s magnetic field has been investigated and identified to be more

problematic than for the corresponding low Tc niobium thinfilm sensors. Approaches are being pursued to circumvent

current limitations in high Tc fabrication technology [20]-

[24]. In particular, the three-sensor gradiometer approach

described in Section VII.B is an example of one means to

circumvent these limitations. In that approach, magnetic-field

coils are utilized to null out the Earth’s magnetic field at the

sensing circuit. The field nulling significantly reduces noise

associated with non-ideal magnetization effects in the highTc

 superconducting material.

C. 3-Axis High Tc

Magnetometer for Stationary

Applications

A 3-axis magnetometer prototype developed by

Conductus under a SBIR contract has been evaluated (Fig.

14). This sensor is being developed for stationary operation

with a focus on geophysical applications. Results obtained

to date are very promising. The sensor has been operated in

the field totally unshielded without any deteriorated performance compared to its performance shielded. Magnetometer performance of 140 fT/Hz1/2 at 1 Hz has been demonstrated under stationary field conditions. This performance

is a factor over 50 times better than the performance obtained from the best commercial fluxgate magnetometers.

The 3 High-Tc

 SQUID magnetometer circuits in this unit

have been working reliably for over one year without failure

[25].

FILL

LINE

SERVICE

ACCESS

PORT

SLOSH

BAFFLE

VEHICLE

SECTION DEWAR OUTER

WALL

MULTI

LAYER

INSULATION

END

SUPPORT

RODS (4)

ACCESS

NECK

PLUG

VENT

LINE

21" 17"

SENSOR VOLUME

 7.0" X 12.0"

CONVECTION

BAFFLE

30"

Figure 13

Liquid nitrogen dewar concept with design versatile for

multiple applications. This dewar has an outer diameter of

43 cm (17”) (compatible with operation in a 53-cm (21”)

vehicle) and a length of 75 cm (30”). The dewar is projected

to have a hold time of 33 days.
42 Naval Research Reviews

VII. The Three-Sensor

Gradiometer

One advantage of the niobium-based superconducting

technology, especially for mobile operation, is the ability to

fabricate large scale counter-wound gradiometer sense loops

using either niobium wire or multi-layer thin films with crossovers. This allows signal subtraction using very low-noise

passive circuits prior to signal processing with active amplifier circuits, which greatly reduces dynamic range requirements from the active electronics. A gradiometer can be configured using two independent magnetometers with signal

subtraction performed at the output of the two magnetometers. The CSS pursued this approach in the early 1970’s

using fluxgate technology. Good stationary performance was

obtained at that time, but there was insufficient dynamic range

in the processing of the differential signals to operate the

sensor in motion.

In order to circumvent this dynamic-range limitation

arising by differencing two individual magnetometer signals

during mobile operation, a novel approach patented by IBM

Research is being pursued [26], [27]. A third magnetometer

is used for common mode rejection, feeding back a signal to

the two primary magnetometers which nulls out the ambient

background field. This concept is denoted as the three-sensor gradiometer (TSG). The concept is depicted in Fig. 15

for one case in which there are two primary high Tc

 SQUIDbased magnetometers and a third fluxgate magnetometer for

field nulling.

A. The Fluxgate Version of the

TFG

The basic TSG concept has been successfully pursued

using room temperature fluxgate magnetometers (in place

of the SQUID magnetometers). A laboratory prototype of

the fluxgate TSG has been developed and demonstrated (Fig.

16). This sensor features four commercial 3-axis triad sets

of fluxgate magnetometers in a planar square array with a

one-foot diagonal baseline. One triad set of fluxgate magnetometers serves as a reference to measure the 3 mean magnetic field vector components at the array. The remaining

three sensor triads are each mounted inside their own 3-axis

Helmholtz coil sets which null the mean magnetic field at

the sensor triad. The residual signals at the triads are processed through their commercial electronics and appropriate combinations are subtracted via differential amplifiers.

In this manner, 6 tensor gradient terms can be calculated of

which 5 are independent. In the laboratory prototype, analog electronics are utilized to implement the magnetic feedback currents with manual feedback adjustments determined

to an accuracy of 5 decimals by a digital signal-processing

routine. Sensitivity better than 0.3 nT/m at 0.1 Hz in motion

has been demonstrated [28], [29].

A Phase I SBlR has recently been awarded to Quantum Magnetics to develop a ruggedized field-deployable

version designed to improve the performance of the laboratory prototype. This version will be compact and light weight

for man-portable operation. It will feature an integrated computer for fully automated sensor control and signal processing and a display to provide the operator easy target detection, classification, and localization.

This sensor offers the opportunity to become the mainstay for man-portable magnetic surveys, replacing the total

field magnetometer by offering unambiguous detection,

moment classification, and localization. Although its range

Figure 14

3-channel high-Tc magnetometer.

Figure 15

The three-sensor gradiometer concept for one case in which

there are two primary high Tc SQUID-based magnetometers

and a third fluxgate magnetometer for field nulling.

THREE SENSOR GRADIOMETER (TSG)

NULLING COIL NULLING COIL

OUTPUT

Left SQUID Fluxgate Right SQUID

Left

SQUID

Feedback

Fluxgate

Feedback

Right 

SQUID

Feedback

+

RF RF RF
Three/1997 43

will be limited by factors of 4 to 10 compared to the superconducting gradiometers described previously, this type of

sensor will provide a low cost approach convenient for many

applications and will avoid a need for cryogens.

B. The High Tc Version of the

TFG

A project to develop and to demonstrate feasibility of

a compact field-deployable high-Tc superconducting gradiometer concept for mobile operation is being pursued by the

CSS, IBM Research, Ball Aerospace, Quantum Magnetics,

the Naval Research Laboratory, and Lockheed-Martin. As

mentioned previously, it has been possible to fabricate highsensitivity gradiometers in niobium technology by using

monolithic wire or thin-film counterwound sense loops.

Neither wire or thin-film monolithic loops can currently be

manufactured using the high-Tc

 technology to provide the

higher sensitivities in motion offered by niobium technology. A high-Tc

 gradiometer preliminary design has been established based on the TSG concept and its performance in

motion has been modeled. The TSG approach circumvents

the current limitations in high Tc manufacturing technology,

providing long baselines by using normal metal wire to connect the two SQUID magnetometers.

Further improvement in performance is expected using the high Tc

 SQUID magnetometers in place of fluxgate

magnetometers as a result of their intrinsically greater sensitivity. A laboratory test article is being developed to evaluate this concept (Fig. 17). This test article incorporates two

flux cubes with each flux cube consisting of 3 orthogonal

high-Tc

 SQUID-based magnetometers with dimensions of 1

cm2. The two cubes, separated by a baseline of approximately

30 cm, permits the synthesis of 3 tensor gradient components. For this test article, a 3-axis fluxgate triad is used to

provide the 3 reference channels required in the feedback

loop to null out the mean ambient background field. This

high Tc gradiometer is expected to surpass significantly the

motion performance of any conventional non-superconducting magnetic sensor technology and is projected to have sensitivity better than that of the low-Tc

 SGMS.

Conclusions and

Summary

Magnetic sensors provide one tool valuable for mobile search operations and surveys for targets with a significant magnetic signature. Superconducting SQUID-based

sensors theoretically represent the most sensitive of known

magnetic sensors. SQUID-based magnetometers have been

demonstrated with sensitivities on the order of 1 fT/Hz1/2 at

frequencies down to 0.1 Hz, while fluxgate and total-field

magnetometers ale demonstrating sensitivities down to l-10

pT/Hz1/2 at 0.1 Hz.

The U.S. Navy has developed the Superconducting

Gradiometer/Magnetometer Sensor, a superconducting gradiometer which has provided long-range detection compared

to conventional non-superconducting magnetic sensors. This

sensor has been utilized to demonstrate a capability for buried mine detection and clutter rejection. As a result of the

multi-channel approach, the sensor provides an accurate localization capability and multi-target discrimination. The

magnetic detection-and-classification signal processing developed in conjunction with the sensor has proven to be effective, providing a fully automated, real time capability. This

Figure 16

Laboratory prototype of a 5-channel fluxgate gradiometer

utilizing the three-sensor gradiometer concept.

Figure 17

Concept for the laboratory prototype of a 3-channel high-Tc

superconducting gradiometer utilizing the three-sensor

gradiometer concept.

FLUX CUBE ASSEMBLY

SOUND

SENSOR

THREE AXIS

MAGNETOMETER

FLUX

CUBE

THREE 

AXIS

COILS
44 Naval Research Reviews

high-tech sensor has provided reliable, rugged performance

in undersea tows conducted over a period of seven years.

The sensor has been operated in the same tow vehicle adjacent to a sonar without a loss in performance. This technology is available off-the-shelf to provide the greatest capability for magnetic detection and localization ever demonstrated.

Work has continued recently with this sensor under the

MUDSS Project to demonstrate its utility for UXO survey.

A new approach incorporating all thin-film niobium

components is being pursued for greater detection range in

mobile operation. A laboratory prototype is being evaluated

with white noise on the order of 50 fT/m-Hz1/2 rising to approximately 200 fT/m-Hz1/2 at 0.1 Hz under stationary field

conditions. A field deployable version is under development

to be utilized in the Joint Countermine Advanced Concept

Technology Demonstration in 1998.

It is only a matter of time before high-Tc

 devices will

be available with performance comparable to that which has

already been demonstrated with low-Tc devices. Magnetometer performance of 140 fT/Hz1/2 at 1 Hz has been demonstrated under stationary field conditions. This performance

is a factor over 50 times better than the performance obtained from the best commercial fluxgate magnetometers.

The benefits in reduced size, longer hold times, and reduced

logistics and support make the high-Tc approach attractive

compared to its low-Tc counterpart

The localization capability afforded by 5-channel gradiometers (and not previously afforded by conventional

magnetic sensors) is expected to add impetus to the acceptance of magnetics for mobile applications. The development

of a 5-channel fluxgate gradiometer is currently in progress.

Sensitivity better than 0.3 nT/m at 0.1 Hz in motion has been

demonstrated.

As a result of a much lower cost and no special support requirements, such sensors will likely be sold in larger

numbers for short-range applications. Their wider usage

would then enhance the opportunity to display the utility of

the greater classification and localization capability afforded

by the 5-channel approach. With greater acceptance of the

5-channel approach, end users will likely want to obtain sensors with greater sensitivity. It is likely that the eventual development of high-performance, reliable high-Tc

 gradiometers will work synergisically with an increased acceptance

of the more powerful magnetic signal-processing approaches.

Hopefully, the time scales for this supply and demand will

be commensurate and so expedite sensor developmental efforts.

Acknowledgments

This work was supported by the U.S. Office of Naval

Research, the U.S. Strategic Environmental Research and

Development Program, and the U.S. Small Business Innovative Research Program. Among the many individuals who

supported these developments at the CSS, the author acknowledges the contributions from G. I. Allen, G. Dobeck,

M. C. Froelich, J. D. Lathrop, D. J. Overway, J. W. Purpula,

L. Smedley, L. Vaizer, R. F. Wiegert, and W. M. Wynn. Finally the CSS would like to acknowledge the contributions

from IBM Research, Ball Aerospace, Conductus, Quantum

Magnetics, Lockheed Martin Federal Systems, the Naval Research Laboratory, and the Jet Propulsion laboratory including the more recent individual efforts from R. H. Koch, J.

Rozen, J. H. Eraker, J. M. Schmidt, R. Cantor, P. V. Czipott,

D. K. Lathrop, D. Gambrel, and R. J. Soulen, Jr.

Biography

Dr. Ted Clem joined the research staff at the Naval

Surface Warfare Center (NSWC) Coastal Systems Station

(CSS) in 1982. His research interests include superconducting sensor design and investigation of noise mechanisms

associated with superconducting materials. The Chief of

Naval Research awarded him for co-authoring the Best Navy

Independent Research Paper in 1989. In addition, he received the CSS’s Award for Special Achievement in Science

and Technology in 1985 for his endeavors to understand

important noise sources in superconducting sensor technology and in 1989 for research with the high temperature superconducting materials. He is also coordinator of low

frequency applications in superconductivity for the Naval

Consortium for Superconductivity. He is a member of the

American Physical Society. Currently he manages the CSS’s

Superconducting Gradiometers Project.

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22. J.Z. Sun, W. Gallagher, and R.H. Koch “Nonlinear

hysteresis in thin film magnetometers,” IEEE Trans.

Appl. Sup., vol. 3(1), pp. 2022-2025, 1993.

23. J.W. Purpura and R.F. Wiegert, “Magnetic hysteresis

in YBa2

 Cu3O7-δ magnetometer sense loops” submitted for publication in IEEE Trans. Appl. Sup., August

1996.

24. R.H. Koch, J.Z. Sun, V. Foglietta, and W.J.

Gallagher, “Flux Dam, a method to reduce extra low

frequency noise when a superconducting magnetometer is exposed to a magnetic field,’’ Appl. Phys Lett.,

vol. 67(5). pp. 709-711, 1995.

25. J.W. Purpura and J.M. Schmidt, “Evaluation of a

three-channel high-temperature superconducting

magnetometer system,” unpublished.

26. R.H. Koch, “Gradiometer having a magnetometer

which cancels background magnetic field from other

magnetometers,” U.S. Patent No. 5,122,744, 1992.

27. R.H. Koch, J.R. Rozen, J.Z. Sun, and W.J. Gallagher,

“A three sensor gradiometer,” Appl. Phys. Lett., vol.

63(3), pp. 403-405, 1993.

28. G.I. Allen, R.H. Koch, and G. Keefe. “Unique manportable 5 element fluxgate gradiometer system,” in

Detection Technologies for Mines and Minelike

Targets, A.C. Dubey, I. Cindrich, M. Ralston, and K.

Rigano, eds., the International Society for Optical

Engineering, Proc. SPIE 2496, pp. 384-395, 1995.

29. R.H. Koch, G.A. Keefe, and G. Allen, “Room

temperature three sensor magnetic field gradiometer,” Rev. Sci. Instrum. 67(1), pp. 230-235, 1996.
46 Naval Research Reviews

Profiles

in Science

Albert M. Bottoms is the first incumbent of the Ellis

A. Johnson Chair of Mine Warfare at the Naval Postgraduate School, Monterey, California. In that capacity since 1992,

he has initiated an ONR-sponsored Symposium Series on

Technology and the Mine Problem that has received international acclaim. He also founded and is president of the

Mine Warfare Association that is dedicated to education and

communication about the “mine problem.”

He is a retired member of the Navy’s Senior Executive Service since 1990. His career includes assignments as

an operations research analyst in the Office of the Chief of

Naval Operations and with naval forces afloat, and with

policy organization in the Office of Secretary of Defense.

He also served as a research and development executive in

the Naval Air Systems Command and in the Navy Laboratory System at the Naval Underwater Systems Center.

Prior to joining the Federal Service in 1970, Mr. Bottoms was a staff member in the Massachusetts Institute of

Technology’s Operations Evaluation Group in the Weapons

Systems Evaluation Group in the areas of mine warfare and

anti-submarine warfare.

Mr. Bottoms has earned degrees from the University

of Pennsylvania, Iowa State University, and the Massachusetts Institute of Technology. His activities have been recognized by the Distinguished Service Award (President,

Naval War College), The Commander’s Award for Meritorious Service (Commander, Defense Systems Management

College) and, in May, 1997, by the Department of Navy

Medal for Meritorious Public Service (conferred by the Superintendent of the Naval Postgraduate School).

Albert M. Bottoms
Three/1997 47

Research Notes

Advanced Sensors for Unmanned Undersea and Airborne

Vehicles

Program develops technologies for remote reconnaissance capabilities including high-resolution synthetic aperture sonar, electro-optical, and high critical temperature

superconducting gradiometer sensors.

Littoral Remote Sensing

A program that will fuse the relevant data available

from National sensors and standoff tactical sensor systems

to provide the on-scene commander with geomorphologic/

environmental and mine/mine field location information.

Shallow Water Airborne Mine Reconnaissance System

A LIDAR system, a derivative of the "Magic Lantern"

system, with improved bottom tracking, higher power and

spatial resolution

Alternative Detection, Classification, and Localization

Development of new detection and classification approaches using prototype neural network technology.

Long Range

Sensors

Shallow Water

Sensors

Synthetic Aperture Sonar Superconducting

Gradiometer

Laser-Illuminated

Electro-Optic

Explosive Ordnance Disposal

High resolution, hand held, imaging sonar systems for

improved mine classification and identification capabilities,

support to special warfare and explosive ordnance disposal.

High Search Rate Sonar System

A program to develop advanced sonar and signal processing technology for integration into unmanned undersea

vehicles, including enhanced swath width toroidal volume

search sonar, advanced side-looking sonar, and computer

aided classification techniques.

Advanced Degaussing Technology

An effort aimed at significantly reducing steel-hull

surface ship vulnerability to magnetic mines; focussing on

closed loop systems and targeting new construction ships.

Rapid Airborne Mine Clearance System Technology

Demonstration

Develop and demonstrate the capability to rapidly target and destroy mines in deep and shallow water from an

airborne platform using supercavitating projectiles and targeting LIDAR.

SENSOR AND GUN

MOUNTED IN

HELICOPTER

NEW SUPER CAVITATING

PROJECTILE

SENSOR 

DIRECTS GUN FIRE TO THE 

TARGET MINE

Safety Zone

The following are several of the major efforts the Navy is supporting which address the key priorities for mine

countermeasures:
48 Naval Research Reviews

Explosive Neutralization and Obstacle Clearance

Efforts investigating technologies utilizing explosive

nets, shaped charges, and explosively formed projectiles; deployment techniques, analytical performance prediction

models and simulations are also developed.

Improved Mechanical Mine Sweeping

Advanced bottom contour following capability for

sweeping close-moored mines.

Environmental Support

Critical to all aspects of mine countermeasures technology development is an extensive knowledge of the environmental parameters that impact the performance of systems

for detection, classification, and clearance of mines and obstacles. The efforts supported here include studies in atmosphere, ocean, ocean bottom and subbottom. Numerical

predictive models and environmentally sensitive and adaptive signal processing techniques are also supported. These

lead to realistic simulation and training aids.

Near-Term Mine Reconnaissance System

A submarine launched unmanned undersea vehicle that

will search for minelike objects and minefields clandestinely.

Advanced Lightweight Influence Sweep System

A system to provide broad and narrow band acoustic

and magnetic energy sweep capability; using novel sparkgap and superconducting technology sources to emulate ship

signatures.

DET/SABRE Fire Control

Thunder Road Magic Carpet