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Technical Paper 320 AD

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OPTIMUM PATCHING TECHNIQUE FOR SEISMIC

SENSORS EMPLOYED IN A GRID

Sterling Pllette, Billy Biggs, and Lawrence Edwards

HRB-Slnger, Inc.

and

Harold Martlnek

U.S. Army Research Institute for the Behavioral and Social Sciences

BATTLEFIELD INFORMATION SYSTEMS TECHNICAL AREA

O

U. S. Army

Research Institute for the Behavioral and Social Sciences

Septamber 197S

Approvtd for public rtltmt: dittribution unlimitfd.

78 11 22 036
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

JOSEPH ZE1DNER

Technical Director

WILLIAM L. HÄUSER

Colonel, US Army

Commander

Research accomplished under contract

to the Department of the Army

HRB-Singer, Inc.

NOTICES

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FINAL DISPOSITION: Thu raport may ba dtttroyad »whan it n no longar naadad. Plaaw do not raturn it to

lha U S. Army Rataarch Inatituta for tha Bahavioral and Social Scwnoai.

NOTE: Tha lindingt in thu raport ara not to ba conttruad ai an official Oapartmant of tha Army poaition,

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Technical Paper 32(/ (/Vi H^B 'TH'JLjZ^

TITLE fand SubH(U) ^N «J d.^i^ii^e== ^T~

READ INSTRUCTIONS

BEFORE COMPLETING FORM

;iPIENT,S CATALOG NUMBER

i OPTIMUM HATCHING JCECHNIQUE FOR ^piSMIC^ENSORSl

I^MPLOYED IN A JJRID , ^

fVPE OF REPORT » PERIOD COVERED

CNTERIM

77^

.Sterling IPHe tte, Billy/Biggs,

Edwards,

18. CONTRACT OR GRANT NUMBERC«)

»Uikit-yrr. .

^Bf Lawrence ?\/pi

Harold/Martinet

IL^T DMiC19-74-C-]ftyäfa,

> PERFORMING ORGANIZATION NAME AND ADDRESS

HRB-Singer, inc,</

P.O. Box 60

Science Park, State College, PA 16801

10. PROGRAM ELEMENT. PROJECT, TASK

AREA • WORK UNIT NUMBERS

11. CONTROLLING OFFICE NAME AND ADDRESS

Army Research Institute for the Behavf6ral add I j

Social Sciences I » •

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14. MONITORING AGENCY NAME • AOORZSyil dl/tmtmifV9*(ConlnlllHtOIHc»)

io IJU^E}_IS. SECURITY CLASS, (ol Ihl» nport)

Unclassified

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Approved for public release; distribution unlimited.

17. DISTRIBUTION STATEMENT (ol lh» mbitrmel antararf In Block 20, II dllltrtit from Roporl)

It. SUPPLEMENTARY NOTES

It. KEY WORDS (Conllnuo on ravaraa atda II nacaaaatr And Idonllly by block numbtr)

Target acguisition

Belt

Gate array

Unattended ground sensors

Seismic sensors

Grid

Surveillance Training

>0. ABSTRACT (ConlHtuo an ravaraa <(#> II nacaaaafr and Idontlty bjr block numbt)

The requirement was to determine the effect of four patching techniques

and associated job aids, training, sensor density, and target activity on

operator performance in detecting targets when using unattended ground sensors

employed in a grid configuration.

Trained operators interpreted tactical data recorder (RO 376) plots both

before and after training using four patching techniques and associated job

aids in the grid employment of seismic sensors. The plots represented both

(continued) £P

DO i 'iJTn 1473 «WTIOM OF t Nov M is OBSOLETE ^Ur^ljyglfled
Unclassified

tieuWITY CLMWPICATON 0^ TMjj PAOIfUM« Palm m*—4

20.

9 sensors per square kilometer grid and 24 sensors per square kilometer grid.

Completeness of target detection, number of false alarms, speed estimation

error, and target direction deviation were measured.

Patching technique training (and job aids) increased detection completeness by 42%: The row patching technique was selected as the best. Use of

the 9-sensor grid resulted in fewer false alarms and equal detection completeness as compared to use of the 24-sensor grid.

7* ; •

I

i

Unclassified

MCUMTV CLAttmCATMN or TNI« PASIONMI Data
Technical Piper 320

j

•

OPTIMUM PATCHING TECHNIQUE FOR SEISMIC

SENSORS EMPLOYED IN A GRID

Sterling Pllette, Billy Biggs, and Lawrence Edwards

HRB-Slnger, Inc.

and

Harold Martlnek

U.S. Army Research Institute for the Behavioral and Social Sciences

BATTLEFIELD INFORMATION SYSTEMS TECHNICAL AREA

Submitted M compltta and

tschnlcally accurate, by:

Edgar M. Johnson

Technical Area Chief

Approved By:

A.H. Blmbaum, Acting Director

ORGANIZATIONS AND SYSTEMS

RESEARCH LABORATORY

Joseph Zeldner

TECHNICAL DIRECTOR

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

6001 Etwnhowar Avenue, Alexandria, Virginia 22333

Office. Deputy Chief of Staff for Personnel

Department of the Army

September 1978

Army Project Number

2Q7e2717A721

Intelllgenoe Syeteme

ferpeMk
ARI Research Reports and Technical Papers are intended for sponsors of

R&D tasks and other research anv military agencies. Any findings ready for

implementation at the time of publication are presented in the lotter part of

the Brief. Upon completion of a major phase cf the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.
FOREWORD

The Battlefield Information Systems Technical Area of the Army Research Institute is concerned with demands of the future battlefield for

increased man-machine complexity to acquire, transmit, process, disseminate, and utilize information. The research is focused on the interface

problems and interactions within command and control centers and is concerned with such areas as topographic products and procedures, tactical

symbology, information management, user-oriented systems, staff operations and procedures, and sensor systems integration and utilization.

One area of special interest is the human factors problems in presentation and interpretation of surveillance and target acquisition information. One relatively new source of intelligence information is

remote monitoring of the battlefield using seismic, acoustic, and magnetic unattended ground sensors. Wher these remote sensors are activated

by enemy personnel or vehicle movement, a monitor display located behind

our lines indicates the activity. The operator can derive from this display not only the presence of the enemy but also such information as the

direction and speed of convoys and personnel, the number of vehicles in

a convoy, and the composition of the convoy (e.g., armored versus wheeled

vehicles).

This publication concerns training needs and patching techniques

for remote sensors employed in a grid pattern for surveillance of large

areas where enemy attack will be cross country. This use of sensors imposes different problems on the operator than the use of sensors in

strings along roads or trails. Results show that special training is

required, and the row patching technique is the most promising. Moreover, the results of grid density indicate that maximum separation of

sensors (the most cost effective) does not reduce operator performance.

Research in the area of sensor systems integration and utilization

is conducted both in-house and contractually. The effort is responsive

to requirements of Army Project 2Q762717A721 and to special requirements

of the U.S. Army Intelligence Center and School, Fort Huachuca, Ariz.,

the Assistant Chief of Staff for Intelligence, and the Remotely Monitored

Battlefield Sensor System (REMBASS) Project. Special requirements are

contained in Human Resource Needs 74-21 and 75-5.

The research was made possible by the excellent cooperation of the

participating personnel of the unattended ground sensor platoon of the

163d Military Intelligence Battalion, attached to NASSTER (Fort Hood,

Tex.). Special thanks are given to LTC Temperly, CPT Latsin, and SFC

Stallings for their cooperation.

W

EPh afeibtaER

hnica rector
OPTIMUM PATCHING TECHNIQUE FOR SEISMIC SENSORS EMPLOYED IN A GRID

BRIEF

Requirement:

The requirements are to develop ways in which seismic sensors employed in a grid array can be patched to an RO 376 readout device, and

to identify the preferred technique for field use. In addition, (a) requirements are to determine if specialized training of unattended ground

sensor (UGS) operators is required for interpretation of activations of

seismic sensors employed in a grxd, (b) to find whether, or to what extent, operator performance is affected by two densities of sensors in a

grid employment, and (c) to determine the interactive effects of sensor

density, target activity, and patching techniques on operator performance.

Procedure:

Four techniques for patching seismic sensors employed in a grid

array to the RO 376 Event Recorder were developed. Operator performance

using the techniques was compared under two sensor density levels—9 versus 24 sensors per square kilometer—and two levels of target activity—

high and low. The value of training specific to the use of the patching

techniques and associated job aids—target log, speed chart, and a specially designed ruler—was determined. Five 2-hour scenarios based on

materials collected in field exercises were used in assessing operator

performance in detecting vehicular targets under the experimental conditions described above. Fixed- and rotary-wing aircraft activity, artillery shell bursts, and random noise were included in the scenarios to

help ensure operational realism. Twenty-four school-trained UGS operators participated in testing under the experimental conditions. An additional eight operators serving as a control group were not given the

special training.

Findings:

Row patching was identified as the preferred technique; it resulted

in fewer false alarms, greater accuracy in estimating target speed, and

more efficient use of equipment. It was also preferred by more operators. The patching technique training significantly enhanced target detection from 36% to 51%, but did not reduce the number of false alarms.

Operators indicated that all the job aids were useful, but that the

scale on the ruler was of value only while they were gaining familiarity

with the way the sensors were deployed in the grid. The percentage of

targets detected under the low-target-activity condition WF.S twice that
detected under the high-target-activity condition. Use of the 9-sensor

grid resulted in the same detection performance and half the number of

false alarms as use of the 24-sensor grid.

Utilization of Findings:

The row patching technique is preferred for field use with operators trained in the patching technique. If the operators have not had

such training, and if operational conditions require a higher detection

rate in spite of a possible increase in false alarm rate, the column

patching technique is preferred.

For detecting vehicular activity, the 9-sensor grid (500-m spacing between seismic sensors or NINISIDS) is preferred to the 24-sensor

grid (250-m spacing). This preference exists in view of the similarity

of results, and considering cost and equipment availability and occasional reduction in the number of false alarms.

If high target activity is observed, procedural changes should be

made (such as assigning additional operators or increasing the number

of targets estimated by intelligence analysts). Training in the use

of the patching technique and associated job aids should be incorporated

in the UGS school content at Fort Huachuca. Knowledge of the system's

capability can be useful to intelligence officers in the selection and

utilization of field personnel to enhance the reconnaissance resources

of the Army.

An error analysis should be conducted and a training package should

be developed and validated, to increase the detection completeness to

higher levels, reduce speed calculation error, and reduce error in determining target direction.
OPTIMUM PATCHING TECHNIQUE FOR SEISMIC SENSORS EMPLOYED IN A GRID

CONTENTS

Page

BACKGROUND 1

OBJECTIVES 4

METHOD OF INVESTIGATION 5

Population and Sample 5

Patching Techniques and Job Aids 5

Independent Variables . 9

Dependent Variables 10

Operator Training and Job Aids *. . . 12

Research Design 13

Apparatus 15

Test Procedure 15

RESULTS 18

Value of Training 18

Evaluation of Patching Techniques 23

Accuracy of Target Detection 27

Identification of the Optimum Patching Technique 32

CONCLUSIONS 34

Optimum Patching Technique 34

Value of the Training 34

Interpretation Job Aids 34

Sensor Density and Target Activity Effects 35

Field Performance Expectations 35

APPENDIX A. SKETCH MAPS AND X-T PLOTS 37

B. TARGET QUALITY DISTRIBUTION—NORFOLK SCENARIOS ... 55

C. ORIENTATION BRIEFING 57

D. INTRODUCTION TO THE GRID EMPLOYMENT PATTERN

(LECTURE/DISCUSSION) 59

E. TEST PROCEDURE TRAINING 63
Page

APPENDIX F. BOW PATCHING TECHNIQUE TRAINING 71

DISTRIBUTION 95

LIST OF TABLES

Table 1. Research design 14

2. Schedule of administration on-site (Fort Hood) 16

3. Analysis of variance summary table for pre- and posttest

detection completeness 19

4. Duncan's Multiple Range Test for comparison of

patching techniques 20

5. Operator detection completeness for patching

technique and pre-/posttest 20

6. Analysis of variance summary table for pre-/posttest

false alarms 21

7. Operator average false alarms for patching

technique and pre-/posttest 21

8. Average completeness of detection for control

group operators 22

9. Control group operator average false alarms 23

10. Analysis of variance summary table for detection

completeness—comparison of patching techniques .... 24

11. Analysis of variance summary table for false alarms—

comparison of patching techniques 26

12. Average number of false alarms per half hour by grid

density and patching technique 28

14. Duncan's Multiple Range Test for patching technique

comparison—24-sensor grid 29

15. Average number of false alarms by target activity

and sensor density per half hour 29

13. Duncan's Multiple Range Test for patching technique

comparison—9-sensor grid 28

• •
Page

16. Analysis of variance summary table for direction

deviation—evaluation of patching technique 30

17. Speed deviation scores for patching technique x grid

density x target activity (m/minute) 31

18. Friedman's analysis of variance by rank on

operator confidence 33

19. Expected completeness detection performance by grid

density, target activity level, and patching

technique 36

20. Expected detection accuracy performance by grid

density, target activity level, and. patching

technique 36

LIST OF FIGURES

Figure 1. Example of a 9-sensor grid used at a natural

chokepoint 3
OPTIMUM PATCHING TECHNIQUE FOR SEISMIC SENSORS

EMPLOYED IN A GRID

BACKGROUND

Unattended ground sensors (UGS) represent part of the Army's capability for detecting ind locating enemy activity at a remote location.

UGS can be used alone or combined with ground-surveillance radars, nightvision devices, aerial surveillance—side-looking airborne radar (SLAR),

infrared (IR), photography, and visual—signal intelligence, patrols,

and observation and listening posts to produce timely and reliable intelligence information. Several types of UGS are in the Army inventory

and are categorized according to the method of remote sensing: seismic,

acoustic, magnetic, ele^tromagnetic, and infrared. UGS are tactically

employed for offensive and defensive operations by units ranging from

small independent patrols to full divisions.

Offensive operations include the following activities:

1. Target acquisition: The sensor's real-time detection capability leads to immediate reaction.

2. Landing (drop) zone monitoring: Sensors monitor enemy activity

for future airmobile assault.

3. Combat sweep: Sensors monitor enemy withdrawal or attack

activity.

4. Ambush: Sensors establish enemy habits, and are employed with

a remote firing device and command-detonated mines.

Defensive operations are used for the following purposes:

1. Base camp defense: Sensors provide warning of enemy presence,

and extend the listening post/observation post detection range.

2. Convoy security: Sensors provide ambush detection and warning.

3. Border surveillance: Sensors provide warning of enemy presence

and fire control information for real-time reaction.

1USA Operational and Tactical Concepts for Employment of Unattended

Ground Sensors. United States Army Combat Surveillance and Electronic

Warfare (USACSEW) School, Fort Huachuca, Ariz. ST-30-20-2. February

1971.
4. Beach defense: Sensors provide warning of counterattack in

beachhead situations.

UGS can be employed in three ways: string, grid, and alerting.

In the string employment, the sensors are employed along a potential

transportation route—land or water. Whether sensors are hand emplaced

or air delivered, the objective is to implant them accurately so that

their location with respect to the route and their separation distances

are known. This precaution enhances the manual readout function by permitting relatively accurate information as to the direction, speed, and

length of column to be derived from the sensor activation patterns. If

hand emplaced, the sensor can be accurately located on a map and "seated"

properly in the ground. Enemy movement is expected to be along the

route(s) and direction(s) identified. Various combinations and mixes

of sensor types have been field tested by the Army.

In the grid employment (sometimes called field, belt, gate, or gate

array), UGS are deployed in a regularly spaced, two-dimensional pattern

to cover a given geographical area or field (Figure 1). The grid is

normally used in defensive operations such as early warning and combat

surveillance. Whether hand emplaced or air delivered, the objective is

to implant the sensors so that their locations are known and ground distances between the sensors are about equal. Hand emplacement is best

for accurate sensor location and proper seating. The grid is designed

to maximize the probability of detecting and acquiring enemy forces intruding in any portion or from any direction within a large area (several square kilometers) . Because the path of the target is estimated,

the operator can make only gross estimates of speed. Until special operator training procedures and job aids are developed, the accuracy of

estimates of speed, direction, and number of targets will be below that

usually obtained with the string employment of UGS.

In the alerting employment, UGS are used to cover a given route

or ground area. For various reasons, however, their exact locations

and the ground distances between them are not accurately known. This

situation can occur from an inaccurate string or grid employment, as

when sensors are delivered by mortar or artillery in areas controlled

by enemy forces or when sensors have been air delivered under poor visibility conditions. Whatever the cause, the operator knows only the approximate location of the sensors. The presence of activity can be reliably detected, but additional information such as speed, number of

targets, and direction cannot be computed accurately.

The U.S. Intelligence Center and School teaches the string and

alerting concepts. Other than providing a brief overview, however,

the school does not train students on monitoring and interpretation procedures for the grid type of employment. In the past, UGS operators

were not likely to encounter grid monitoring situations. However, because of the shift in emphasis from the Southeast Asia type of conflict,

the possibility of grid applications in area intrusion situations has

increased.
mW

en

en

O»

H* 500 Km

cn

' /

:/. ca

/ ' Trails

Figure 1. Example of a 9-sensor grid used

at a natural chokepoint.
A UGS operator (MOS 17M20) should be qualified to work with grid

employment using a field tactical recorder. Although by the 1980's the

Remotely Monitored Battlefield Sensor System (REMBASS) program may automate (computerize) target readout functions at division level, there will

always be need for a manual readout capability using tactical recorders

at the battalion and brigade levels. The main reason for this need is

the flexibility of transporting and using current equipment. The manual

readout system also will always be needed at the division level as a

backup to the automated system.

At present, only one research effort to find how well school-trained

UGS operators are able to perform with grid arrays is known to have been

reported. This work determined tha*- there was relatively poor operator

performance and suggested the need for research on readout techniques,

training requirements, operator performance parameters, and patching

techniques.

The assignment of sensors in the field to pens or columns on the

RO 376 tactical recorder is called patching. The patching technique

used with grid employment xs more complicated than that used with string

employment because a two-dimensional array of sensors must be represented

by essentially one dimension (the columns) on the RO 376. Sensors in

the field can be assigned pens on the readout display in many ways, especially as the number of sensors increases. One particular arrangement

might aid the operator with one task, and another arrangement might interfere or detr? c. Research was needed to determine whether there is

a patching ternique that will optimize operator performance.

Performance parameters of UGS operators in a variety of situations

must be investigated for two major reasons: (a) to provide the commander

with an assessment of the value of information generated by UGS under

operationally relevant target conditions, and (b) to provide guidelines

for improving operator target reporting performance.

OBJECTIVES

Specific objectives of the present research are as follows:

1. To define alternative sensor patching techniques for use with

the grid employment of sensors and determine which technique

enhances operator performance the most.

2

Edwards, L. R., Rochford, D. S., and Shvern, U. Comparison of Four

Unattended Ground Sensor Displays. ARI Technical Paper 281, April

1977. (NTIS No. AD A039 056)
'.

2. To determine whether basic specialized training in patching

techniques will enhance operator performance in interpreting

returns from seismic sensors employed in a grid array,

3. To develop interpretation job aids to assist the operator,

4. To determine the effects on operator performance of two levels

of sensor density in a grid and two levels of target activity,

5. To determine the interaction of patching techniques with sensor

density xn a grid and target activity,

6. To provide estimates of the completeness and accuracy a commander can expect from relatively untrained and inexperienced

operators when they interpret activations of sensors employed

in a grid array, and

7. To determine how these estimates vary for two levels of sensor

density in a grid and two levels of target activity.

METHOD OF INVESTIGATION

Population and Sample

The population of concern was the Army enlisted UGS operator (MOS

17M20) trained at the U.S. Army Intelligence Center and School at Fort

Huachuca, Ariz. Thirty-two enlisted UGS operators of the 163d Military

Intelligence Battalion stationed at Fort Hood, Tex., served as UGS operators for the research.

Patching Techniques and Job Aids

Four patching techniques were developed for ii vestigation after

consideration of Army employment methodology and the characteristics

of the sensor activation records available. The records had been collected at Fort Bragg in 1973 during a test of a UGS system. For this

field exercise, sensors had been arranged symmetrically within a square

grid, 1,000 m on a side. The target runs always started at the top or

bottom, never on the sides, and passed through the entire grid. Many

of the employment techniques mentioned previously are adaptable to such

a grid, and the results of testing could be generalized to them. From

the point of view of Army employment methodology, it was decided to include the column, perimeter, row, and zone patching concepts.

Column Patching Technique. For this technique, a string concept

was desired in which sensors are employed in vertical columns parallel

to the direction of expected enemy approach. Depending on the path, a

target passing through or around the grid activates sensors in the columns as though the sensors are deployed along a roadway. Each column
is patched to the pens on the RO 376 recorder systematically: The column 1 sensors are patched first, starting at the left side of the recorder with pen 1; the column 2 sensors are patched next, etc. The sensors are numbered in columns on the sketch map used by the operator.

Such a sketch map or grid is shown in Appendix A (Figure A-l). As shown,

Column I is composed of sensors 1, 2, and 3;

Column II is composed of sensors 4, 5, and 6; and

Column III is composed of sensors 7, 8, and 9.

The sensor activations associated with the target paths drawn on

this grid are presented in the X-T plot (Figure A-2). Normally, the

UGS operator would analyze the activations first, then draw the target

paths on the grid. In the present application, the order of procedure

is reversed for purposes of discussion.

Figure A-3 is an example of the column patching technique in which . <

24 sensors instead of 9 are involved. The activations associated with

the target paths drawn on this grid are presented in the X-T plot (Figure A-4). Because of the manner in which these sensors were deployed,

it was necessary to patch four of the sensors so that they appear twice

on the X-T plot. As is apparent from Figure A-3, sensors 2 and 7 are

associated with the same sensor. The same relationship exists with 8

and 13, 14 and 19, and 20 and 25. As shown.

Column I is composed of sensors 1, 2, 3, 4, and 5;

Column II is composed of sensors 6, 7, 8, 9, 10, and 11;

Column III is composed of sensors 12, 13, 14, 15, 16, and 17;

Column IV is composed of sensors 18, 19, 20, 21, 22, and 23; and

Column V is composed of sensors 24, 25, 26, 27, and 28.

To assist the operator in determining quickly which pens were associated with which columns, a ruler was developed that could be placed

directly on the X-T plot. The calibrations on the ruler correspond directly with the pens on the recorder. The ruler is called the UGS

ruler. As shown below, one side of the ruler has a 24-pen (sensor grid)

scale and the other a 9-pen (sensor grid) scale. One objective of the

research was to assess the value of this job aid.

rrn

i 1 ; 2 ; 3 : 4

i : ! •

I—I r— I ; > I i I1 I

'.5 7 3 & If, I! 1.' «I 'A 13 »'17 IS 1C 2a 21 22 23124 25 26 27 28 24 SENSOR GRID

COLUUN PATCHING PT 01119 dOSN3S '6I Sit (|tlt{* til tl

... J.l.jJJ.LLu

Parineter Patching Technique. A perimeter concept was desired to

which sensors are deployed as several separate perimeters surrounding

a central point. Each perimeter is patched to the pens of the recorder

systematically: The outer perimeter sensors are patched first, then the

6

•'
inner perimeter sensors, then a central solitary sensor. The pens coinciding with the outer perimeter of sensors show in a concentrated space

on the X-T plot the first indication of a target passing through the

field. Any target that enters this grid must pass through or around

one or more of these perimeters.

An example of the perimeter patching technique for a 9-sensor grid

shows how the sensors are numbered throughout the grid (Figure A-5). The

sensor activations resulting when targets traverse the 9-sensor grid are

presented on the X-T plot of Figure A-6. As shown,

Perimeter I is composed of sensors 1-8, and

Perimeter II is composed of sensor 9.

An example of the perimeter patching technique for a 24-sensor grid

showing how the sensors are numbered throughout the grid is presented

in Figure A-7. The X-T plot is presented in Figure A-8. As shown.

Perimeter I is composed of sensors 1-14,

Perimeter II is composed of sensors 15-23, and

Perimeter III is composed of sensor 24.

The ruler for the 24-pen scale and the 9-pen scale of the perimeter

patching technique is shown below.

M 11 M 11' i i—-—111 iTi'i rm

'ij2itU|& .: ;.„:.' • ,. /ISIB;!? 13 I9|20;2I 22 23124

I—I u i , ! : i - i ! : !

24-SENSOR GRID

PERIMETER PATCHINS PT OIKS OOSNJSe

I ! I I !"!;!

•P <[•»{ jtill»

Row Patching Technique. In the row concept, sensors are deployed

in successive lines perpendicular to the expected direction of enemy

attack. As a target penetrates successive rows, additional information

about the target becomes available. Any target entering or leaving the

grid or going around the side of the grid must activate sensors in one

or more rows. The sensors are patched to the recorder systematically:

Row 1 sensors are patched first, starting at the left side; row 2 sensors

are patched next, etc. An example of the row patching technique for a

9-sensor grid shows how the sensors are numbered within the grid (Figure A-9). The X-T plot for the 9-8ensor grid is presented in Figure

A-10. As shown.

Row I is composed of sensors 1, 2, and 3;

Row II is composed of sensors 4, 5, and 6; and

Row III is composed of sensors 7, 8, and 9.

7
An example of the row matching technique (24-sensor grid) shows

how the sensors are employed within the grid (Figure A-ll). The activations formed from the target paths shown on the 24-sensor grid are

presented on the X-T plot (Figure A-12). As shown,

Row I is composed of sensors 1, 2, 3, 4, and 5;

Row II is composed of sensors 6, 7, 8, and 9;

Row III is composed of sensors 10, 11, 12, 13, and 14>

Row IV is composed of sensors 15, 16, 17, 18, and 19; and

Row V is composed of sensors 20, 21, 22, 23, and 24.

The ruler for the 24-pen scale and the 9-pen scale of the row

patching technique is shown below.

1,2,3:406 7 8 9:10 1. 12 in-ilülS 17 18 19 20 21 222324 24-SENSOR GRID

ROW PATCHING PT OIHO a0SN3S-6

I | j | ! i M

t 8 £ 9|S|»IC'Z;i LLLI i UJÜ

Zone Patching Technique. In the zone concept, sensors are patched

to portray distinguishable areas or zones adjacent to each other. By

knowing the location of the zones, an operator can trace the path of a

target from one zone to another (e.g., in border surveillance). Each

sensor within each zone is patched to the pens of the recorder in clockwise order: Zone 1 sensors are patched first, starting with pen 1 of

the recorder; zone 2 sensors are patched second, etc. An example of

this zone patching technique for a 9-sensor grid shows how the sensors

are numbered throughout the grid (Figure A-13). The sensor activations

resulting from targets traversing the paths shown on the 9-sensor grid

are presented on the X-T plot of Figure A-14. As can be seen on the

9-8ensor grid.

Zone I is composed of sensors 1, 2, and 3;

Zone II is composed of sensors 4, 5, and 6;

Zone III L'l composed of sensor 7;

Zone IV i.s composed of sensors 8, 9, and 10; and

Zone V U\ composed of sensors 11, 12, and 13.

Figure A-15 is an example of the zone patching technique for a

24-sensor grid. Figure A-16 presents the corresponding X-T plot. As

can be seen on the 24-sensor grid.

Zone I is composed of sensors 1, 2, 3, 4, 5, 6, and 7;

Zone II is composed of sensors 8, 9, 10, 11, 12, 13, and 14;

Zone III is composed of sensor 15,

Zone IV is composed of sensors 16, 17, 18, 19, 20, 21, and 22; and

Zone V is composed of sensors 23, 24, 25, 26, 27, 28, 29, and 30.
In both grids, some sensors were patched twice because of the geographical location of the sensors.

Hie 24-pen scale and the 9-pen scale of the ruler for the zone

patching technique are shown below.

TTTTT

12134 5|6'7

I

81910 II l2:l3J14{1S|16i17|l8!I9J20I21122 23!24I25J26 27128^130 24 SENSOR GRID

ZONE PATCHING PT aiao a0SN3S-6 El Zl 11 01 6 [Xirrrn

Independent Variables

In addition to the patching technique (the primary independent variable in the present research), additional independent variables were

training effects, sensor grid density, target activity, and scenarios.

Training Effects. Comparison of operator performance before training (Pretest) and after training (posttest) shows the effect attributable

to training in patching techniques. This variable was used only to determine the need for a patching technique and associated training and

for operator aids when sensors are employed in a grid configuration.

Sensor Grid Density (Two Levels). Sensor grid density refers to the

number of sensors placed within a 1 km2ground area. Two sensor densities were used: one in which 9 sensors were sprced 500 m apart and one

in which 24 sensors were spaced about 250 m apart. The two levels involved similar geometrical patterns using the same sensors and forming

a 1 km2. Other grid density patterns that might have been selected

would not have resulted in symmetrical sensor patterns for both conditions. Symmetry was required for efficient use of sensors and for easy

computation of speed and target density.

With respect to string emplacement standards, a 250-m and even a

500-m ground separation between sensors is not large. With respect to

a grid, however, a smaller separation distance may help in tracing a

target's path through the grid to determine direction. Determining a

target's path and direction from a grid pattern requires a more detailed

analysis of sensor activation than does making this determination from

the string employment along a trail. Placing sensors closer together

on the ground provides closer activation patterns and may help the operator determine path and direction. Determining whether an operator

can extract more information from a 250-m separation or a 500-m separation is important from the standpoint of cost, emplacement time, manpower comnitment-, sensor availability, and equipment reliability.
Target Activity (Two Levels). Target activity was operationally

defined as the number of distinguishable activation patterns appearing

on the readout display within a given period of time. Each distinguishable activation pattern is a target that represents one or more vehicles

in a column. Two levels of target activity (low and high) were tested

tc permit application of the results to more than one battlefield condition. Low target activity was designated as two or three targets within

a half-hour time period. High target activity was designated as five

to seven targets within a half-hour time segment. These two target conditions are consistent with a discussion of the topic in a Fort Hood

MASSTER field report3(see footnote 2).

Scenarios (Five). To assess objectively the performance of the

operators, five 2-hour scenarios were developed from sensor activations

collected using operational equipment in the field at Fort Bragg. The

activations were recorded on tapes, and included activation patterns

produced by armored vehicles and wheeled vehicles and by typical environ- '

mental, artillery, and aircraft noise. Since this was a controlled exercise, target location and time were known and could be related to

sensor activations in developing school solutions. To select target

activation patterns for the scenarios,' monitor performance (Appendix B)

on 113 of the targets used in previous research (Edwards et al., 1977)

was analyzed to determine the level of difficulty (p value) for each of

the targets. Targets were selected to provide a realistic range of

difficulty. No changes were made to the original target activation

patterns.

To satisfy the requirements for five 2-hour scenarios, 43 different

targets were selected. These 43 targets were presented to the operator

twice, once as they had been taped originally and once with the sensor

field rotated 180 degrees so that the activation patterns would not be

recognized. Each 2-hour scenario was developed to contain a systematic

arrangement of the two sensor grid densities and the two target activity

conditions. Each scenario contained four 30-minute segments, and each

segment contained one sensor gVid density and one target activity condition. Each scenario was produced in four variations, representing the

four sequences or orders of grid and target activity conditions.

Dependent Variables

Analysis was in terms of the dependent variables—detection completeness, false alarms, target direction, target speed, and confidence.

3

USA Armored Cavalry Troop Test Report, Vol. III. HQ, Modem Army

Selected Systems Test and Evaluation (MASSTER) at Fort Hood, Tex.,

January 1972.
Petection Coapleteneas. If em operator reported a target in the

scenario when the records (ground truth) of the Fort Bragg exercise

indicated that a target should have been causing activations of the

designated pens, the response was classified as a correct detection.

Detection results are reported as detection completeness (i.e., the number of correct detections divided by the number of targets presented x

100).

False Alarm(s). If an operator reported a target in the scenario

when no target was causing activations of the designated pens, the response was classified as a false alarm. Ir addition, if an operator

reported two or more targets on the same pen when in actuality there

was only one, any additional response was classified as a false alarm.

Target Direction. When the direction and speed of a target as it

leaves the grid area are known, an estimate can be made of its location

later. Because such information is useful to the Arm/ field comnander,

direction deviation scores were analyzed. A school-solution direction

was compared to the operator's estimated direction of a target path at

the point where it left the grid. An 18-point sector scale was used,

each sector being 10°. The operator's response was scored as zero degrees if the target path that he drew was in the right sector. If the

target path was drawn in a sector other than the school-solution sector,

a deviation score was determined—expressed as the number of sectors of

deviation, in absolute units. Thus, a high score indicated poor performance. If a target was reported as heading north when in fact it was

heading south, a deviation score of 18 (180°) was assigned.

Caution should be exercised in generalizing results of target

direction estimates to the operational situation. Because of the space

restraints in the area assigned for the collection of sensor activation

data at Fort Bragg, most targets, after passing through the grid, were

required to travel along a trail running parallel to the last row of

the grid and approximately 100 to 200 m from it. Thus, most of the targets activated the last row of sensors, which resulted in a target path

through the grid and then along the bottom of the grid. Estimates of

direction were thus more difficult to calculate than if the target had

kept going straight, as would normally occur in most operational

situations.

Target Speed. Deviation scores were computed for target speed by

comparing the operator's responses with estimates based on known starting times and true paths of vehicles traversing the grid. If a correct

speed (in m/min) was given, a score of zero deviation resulted. For

an incorrect score, the absolute deviation in m/min from the school

solution was determined.
Confidence. The operator was asked to rate his confidence on a

4-point scale (25%, 50%, 75%, and 100%) that the activation pattern he

was reporting on was a valid target pattern. Operator confidence on

valid targets was compared with operator confidence on false targets.

A confidence differential was obtained by subtracting average confidence

on the false alarms from the average confidence on the right detections.

A high score indicated that the operator's confidence rating was a valid

measure.

Operator Training and Job Aids

The participating operators had all graduated from the U.S. Army

Intelligence Center and School at Fort Huachuca. However, they were not

familiar with use of the grid deployment, test procedures, or patching

techniques. None was familiar with the job aids to be tested. It was

necessary, therefore, to train the operators to a reasonable level of

skill in these operations. For this reason, a four-part familiarization/

training package was developed:

Part I—Orientation Briefing (Lecture) (Appendix C)

Part II—Introduction to the Grid Deployment Pattern (Blackboard

Lecture/Discussion) (Appendix D)

Part III—Test Procedure Training (Self-paced Workbook/Supervisor

Guidance) (Appendix E)

Part IV—The Grid Deployment of Seismic Sensors Using (Column/Row

Perimeter/Zero) Patching (Self-paced Workbook/Supervised

Guidance) (Appendix F)

The orientation briefing (part I) was given by the instructor to

give the operators an idea of what the exercises involved. The grid

deployment familiarization (part II) was designed to acquaint the operators with the UGS grid. The instructor presented several target intrusion situations on the biackboard to illustrate basic differences and

similarities between string and grid deployment.

The test procedure training (part III) was intended to teach the

operators the procedures expected of them and the use of the target

reporting forms (target logs). The target log has a sensor grid sketch

in the top half and the seven-step reporting procedure in the bottom

half. The seven-step reporting procedure is a job aid in that it tells

the operator what information he is to provide. Functionally, it simulates the reporting requirement of the field coimnander. Included in

this self-paced training is a discussion of a speed table—also a job

aid. Use of the speed table eliminates arithmetic errors commonly made

by operators. The speed table and instructions for its use are presented in Appendix E.

12
For each patching technique, a training workbook (part IV) was

prepared. Section I deals with the 24-sensor grid condition and section

II with the 9-sensor grid condition. Each section addresses the following questions:

What is the (column, perimeter, row, and zone) patching technique?

How do you detect targets?

a. Example 1—Target 1

b. Example 2—Target 2

c. Example 3—Target 3

How do you estimate distance?

How do you determine the midpoint time difference?

How do you calculate speed?

How do you determine target type?

Practice targets (with X-T plots).

The training workbook for the row patching technique is presented

in Appendix F. In addition to the 24-pen and 9-pen measurement scales,

the UGS ruler has two additional scales (on the other side of the ruler)

as shown below:

100

1 2 3

"i i r i r" : " r 1 '"i n i r-j-r

200 300 4C0 500 600 700 BOO 900 1000 1100 1200 1300 1400 1500

DISTANCl IN METERS

UGS RULER anna son

sainNtN tmna son MI 3WII

4 5 6 7 8 9 10 11 12 13 14 IS 16 17 18

UuluiliuJuMlwluiluJiuiiio.LiuljJn.ilj.>!,!: l,l.i:,u<l4ilij..Li<l<ml<iiili>tlnuUiJiiiiliiJii»liniL'.l

19 20 21 22 23 24 25 26 27 28 29 30

.l,..J,.:,l.1.1|,U:!,JU,.l,J,1„|..,.l,UU.„UlJ...IU.a,.Ul„.^,U'.U,ll..l_J

The top scale shown is a distance-in-meters scale and is used to

measure the distance covered by a target with the 1,000-m square grid

on a scale from 0 to 1,500 m. The bottom scale is a time-in-UGS-rulerminutes scale used to measure activation midpoint time differences on a

scale from 0 to 30 minutes. Use of all the scales on the UGS ruler as

explained to the operators is discussed in Appendix F.

Research Design

The research design is presented in Table 1. Two evaluations were

undertaken: (a) a pretest and a posttest comparison to determine the

value of patching technique training; and (b) a comparison of the effectiveness of four patching techniques, performed with the intention of

selecting one for further experimentation and operational use.

Value of Training. The following independent variables were analyzed for both detection completeness and false alarms: pretest/posttest

performance (2), patching techniques (4), scenarios (2), scenario-order

effects (2), groups (4), and interactions of sessions and scenarios with

patching techniques. Scenarios A and B were counterbalanced within each

13
Table 1

Research Design

(Valt

ALUATION

e of Trai

PRETEST

.Session

I

ning)

EVALUATION II

(Pätehing Teehnique Comparison)

OPERATORS 1

POSTTEST

Seq. I* 1 TRG

PT I

Session 2

Seen A+B

Seq. I *

Session 3

Seen C(18T)

Seq. II *

Session 4

Seen D{17T)

Seq. Ill *

Session

Seen E(l

Seq. IV

PT I PT II PT III PT IV

1,2,3 Seen A Seen B

4,5.6 Seen B Seen A

7,0,9

PT II

been A

PT II HT III PT IV PT 1

Seen ü

10,11,12 been B Seen M

13,14,1!)

PT III

Seen A

PT III PT IV PT I HT II

been B

16,17.18 Seen B been A

19.^0.^1

PT U

Seen A

PT IV PT I Pi II PT III

Seen B

d2,z3,t4 Seen B Seen A

No TRG

t5,z9

2b, 3U

27,31

2«, 3k;

PI I-A

PT 11 -

HT III -

PT IV -

ft

b

ü

PT I - B

PT 11 - B

PT HI - A

PT IV - A

* Scenario Sequences - tour 30 minute target tapes

Sequence I Sequence III

1 Mign Act, 9-sensor grid

2 LOW Act, "

3 Hign Act, 24-sensor grid

4 Low Act,

Sequence II

4 Low Act, 24-sensor grid

1 High Act,

2 Low Act, y-sensor grid

j High Act,

2 Low Act, 9-sensor grid

1 High Act,

4 Low Act, 24-sensor grid

3 High Act,

Sequence IV

1 High Act, 24-sensor grid

4 Low Act,

3 High Act, 9-sensor grid

2 Low Act,

14
patching technique investigated to control for scenario effects (Table 1).

The experimental design used to evaluate the effects of training was a

single Latin Square replicated on the between-subjects factor of patching techniques (I, II, III, IV). The Latin Square factors were scenarios

A and B and training. The primary reason for analyzing the patching

techniques and the scenario variability was to reduce the size of the

error term for the pretest/posttest comparison. The purpose was not to

provide a conclusive test of the comparative value of the patching techniques—the purpose of a separate evaluation—but to investigate the

value of training for a representative sampling of all the patching techniques combined on the basis of detection completeness and false alarms.

Comparison of Patching Techniques. The following independent variables were analyzed for all the dependent variables: patching technique

(4), sensor grid density (2), target density (2), and all interactions

between the three variables. The groups, scenario main effects, and interactions involving scenario were analyzed to reduce the error variance.

There was no interpretation of the scenario effect because it is completely confounded with sessions and sequence (order) effects.

Apparatus

Twelve RO 376 tactical recorder simulators were used to display

pretest and posttest UGS activation data previously recorded on KO 376

X-T plots. The simulators presented the X-T plots at a speed equivalent

to that of an operational RO 376 event recorder (12 inches per hour).^

The simulators used the same viewing area size as the RO 376. The X-T

plots were therefore presented with the same speed and format they would

have in a field situation.

Test Proofdure

The training and test schedule on a daily basis is presented in

Table 2. Because only 12 RO 376 simulators were available and 24 operators were required for experimental design reasons, it was necessary to

divide the operators into two groups and alternate the training/test

cycle. While one group was being trained, the other group was being

tested.

During the morning of the first day, the first group of operators

(odd-numbered) was given a 30-minute familiarization briefing and a

90-minute program of instruction and practice on the test procedure.

During this time, questions were answered and response feedback was

given. A 2-hour uninterrupted pretest was then administered requiring

4Operator and Organization Maintenance Manual for Recorder, Signal Data

RO 376/UGQ Resdel Model No. 91662-1, DAAB07-69-0195, June 1969.

15
"

Table 2

Schedule of Administration On-Site (Fort Hood)

DAY 1 (Tues)

AM Onentation and Grid Briefing 8:00-8:30

Test Procedure Training 8:30-10:00 f

Pretest (Session 1) 10:00-12:00

PM

12 Drive Mechanisms

10 Scenarios

Op 1, 3, 5 PT-I Row 1'atchi nq

Op 7. 9, 11 PT-II Lane Patch inq

Op 13, 15, 17 PT-III Per Patchi ng

Op 19, 21, 23 PT-IV Zone Patch inq

Training (Session 2) 1:30-4:00 Orientation and Grid Briefinq

No Drive Mechanisms 1:30-2:00

Op 1, 3, 5 PT-I Test Procedure Trq 1:30-2:00

Op 7, 9, 11 PT-II Pretest (Session 1) 2:00-4:00

Op 13, 15, 17 PT-III 12 Drive Mechanisms

Op 19, 21, 23 PT-IV 12 Scenarios

Op 2, 4, 6 PT-I

Op 8, 10, 12 PT-II

Op 14, 16, 18 PT-III

Op 20, 22, 24 PT-IV

DAY 2 (Wed)

AM Posttest (Session 2) 8:00-11:00

12 Drive Mechanisms

Op 1, 3, 5 PT-I

Op 7, 9, 11 PT-II

Op 13, 15, 17 PT-III

Op 19, 21, 23 PT-IV

PM Training (Session 3) 1:00-4:00

No

Op

Op

Op

Op

Drive Mechanisms

1, 3, 5 PT-II

7, 9, 11 PT-III

13, 15, 17 PT-IV

19, 21, 23 PT-I

Traininq (Room 2) 8: DOflo Dri ve Mechanisms

Op 2, 4, 6 PT-•I

Op 8, 10, 12 PT-•II

Op 14 16 , 18 PT-•III

Op 20 22 , 24 PT-•IV

11:00

Posttest (Session 2) 1:00-4:00

12 "Drive Mechanisms

Op 2, 4, 6 PT-I

Op 8, 10, 12 PT-II

Op 14, 16, 18 PT-III

Op 20, 22, 24 PT-IV

DAY 3 (Thurs)

AM Posttest

Öp

Op

Op

Op

7, 9,

13, 15,

19, 21,

(Session

5

11

17

23

3) 8:00-

PT-II

PT-III

PT-IV

PT-I

11:00 Training (Session

Öp 2, 4, 6

Op 8, 10, 12

Op 14, 16, 18

Op 20, 22, 24

3) 8:00-11:00

PT-II

PT-III

PT-IV

PT-I

16
DAY 3 (Thurs) cont.

PM Training (Session 4)

Op 1, 3, 5 PT-III

Op 7, 9, II PT-IV

Op 13, 15, 17 PT-I

Op 19, 21, 23 PT-II

Posttest (Session 3) 1:00-4:00

Op 2, 4, 6 PT-II

Op 8, 10, 12 PT-III

Op 14, 16, 18 PT-IV

Op 20, 22, 24 PT-I

Day 4 (Fri)

AM Posttest (Session

Op 1, 3, 5

Op 7, 9, 11

Op 13, 15, 17

Op 19, 21, 23

4) 8:00-

PT-III

PT-IV

PT-I

PT-II

11:00 Training (Session 4) 8:00-11:00

Op 2, 4, 6 PT-III

Op 8, 10, 12 PT-IV

Op 14, 16, 18 PT-I

Op 20, 22, 24 PT-II

AM Training (Session

Op 1, 3, 5

Op 7, 9, 11

Op 13, 15, 17

Op 19, 21, 23

PM Posttest (Session

Op 1, 3, 5

Op 7, 9, 11

Op 13, 15, 17

Op 19, 21, 23

DAY 5 (Mon)

5) 1:00-4:00

PT-IV

PT-I

PT-II

PT-III

5)

PTPTPTPT8:00-11:00

IV

I

II

III

Posttest (Session 4) 1:00-4:00

Op 2, 4," 6 PT-III

Op 8, 10, 12 PT-IV

Op 14, 16, 18 PT-I

Op 20, 22, 24 PT-II

Training (Session 5) 1:00-4:00

Op 2", 4, 6 PT-IV

Op 8, 10, 12 PT-I

Op 14, 16, 18 PT-II

Op 20, 22, 24 PT-III

PM

DAY 6 (Tues)

AM Control Group Posttest (Session 5)

Op 2, 4, 6 PT-IV

Orientation and Grid Briefing 8:00-8:30 Op 8, 10, 12 PT-I

Op 14, 16, 18 PT-II

Test Procedure Trg 8:30-10:00

Pretest 10:00-12:00

8 Drive Mechanisms

8 Scenarios

Posttest (Session 5)

PRE (AM)

25 PT I - A

26 PT II - A

27 PT III - A

28 Pi - A

29 PT I - B

30 PT II - B

31 PT III - B

32 PT IV - B

POST (PM)

PT I - B Row

TPT II - 3 Lane

PT III - B Peri

PT IV - r; Zone

PT I - A Row

PT II - A Lane

PT III - A Peri

PT-IV - / Zone

Op 20, 22, 24 PT-III

17
use of the 12 RO 376 simulators. During the afternoon session, each operator in this group was administered patching technique training as

specified in the table. The training session did not require use of

the RO 376 simulators. During the same afternoon session, the operators

in the even-numbered group were administered the familiarization training and the pretest that the first group had received that morning.

During the morning of the second day, the odd-numbered group received the posttest (session 2) for the training that had been given

the previous afternoon. The operators in the even-numbered group were

given training. This procedure required a total of 5 days to complete

test requirements.

During the testing, when operators thought they had detected a

target, they annotated the test scenario by drawing a circle around the

sensor activations that they felt constituted the target pattern and

numbered the pattern. The circled patterns were consecutively numbered

for all the targets detected in a particular 30-minute segment. After

having recorded each target detection, the operator analyzed the target

pattern and recorded the following information on the Target Log Sheet:

direction of the target through the sensor field, midpoint tin minutes)

of the first sensor activation and the last sensor activation, total

time of first sensor activation, column length, time elapsed between

the two midpoints, estimated distance, estimated speed, and confidence.

During the last day, a control group of eight operators was administered the pretest and posttest but received no training. Each operator

in this group was tested on a separate patching technique to determine

the effect, if any, of the 2-hour pretest and posttest exposure.

RESULTS

Value of Training

Completeness of Detection. A frequency poll 3on of the data was

judged to approximately fit a normal curve (M 43%> Mdn = 41%); the

planned analysis of variance was therefore performed. The analysis of

variance results for completeness of detection (detection rights/total

possible) are presented in Table 3. Statistically significant effects

were noted for patching technique and pretest/posttest. The significant

pretest/posttest effect indicates that the patching technique training

was effective in increasing operator completeness of detection. This

increase from 36% to 51% represents an additional two targets per operator during a 2-hour period.

Although this increase has obvious practical value to the field

commander, a 51% detection rate is still below the full potential of

the UGS systems. This relatively low detection rate is probably due to

the complexity of the operators' task of detecting and separating overlapping activations in heavy target activity situations and to their

18
lack of exposure to the grid employment patterns of the type tested.

Additional and more comprehensive training concerning the grid itself

is needed if the grid employment pattern is to be used effectively in

the field.

Table 3

Analysis of Variance Summary Table for Pre- and

Posttest Detection Completeness

Source of Sums of Mean Significance

variance df squares square F level

Patching technique (T) 3 18,624.42 6,208.14 5.18 .05

Scenario order (A/B)

(B/A) i 494.09 494.09 .41 MS

Groups of subjects 3 5,297.08 1,765.69 1.48 NS

e

l

16 19,112.08 1,194.51

Pre-/posttest (P/P) 1 9,576.75 9,576.75 20.46 .01

Scenario (S) 1 70.09 70.09 .15 NS

T X P/P 3 2,781.42 927.14 1.98 NS

T X S 3 2,943.41 981.14 2.10 NS

e2 16 7,488.58 468.04

Total 47 66,387.92

The significant patching technique effect indicates that when the

pretest and posttest results are averaged, the column patching technique

results in significantly higher completeness results than any of the remaining three patching techniques (Table 4). During the operator debriefings, it became apparent that the operators did well with the column

technique because it was most similar to the string technique they were

familiar with. The averages by patching technique and pretest/posttest

are given in Table 5.

False Alarms. A frequency polygon of the false alarm data, although moderately skewed to the right (M - 2.6; Mdn 1.5), was judged

acceptable for an analysis of variance (Table 6) to be conducted. The

only effect approaching statistical significance was the patching technique. Since the significance level is only 10%, this result must be

considered tentative. The average number of false alarms by patching

technique and pre-/posttest results is shown in Table 7.

19
Table 4

Duncan's Multiple Range Test for Comparison of Patching Techniques

Means

Perimeter

32.4%

Zone

40.5%

How

41.4%

Column

59.3%

Shortest

significant

range

32.4%

40.5%

41.4%

8.1 9.0

.9

26.9*

18.8*

17.9*

R2 13.25

R3 • 13.90

R4 = 14.30

*p < .05.

Table 5

Operator Detection Completeness for Patching

Technique and Pre-/Posttest

Overall

Column Perimeter Row Zone averages

Test % % % % %

Pretest 59 22 31 33 36

Posttest 60 42 52 48 51

Averages 60 32 42 41 44

• •

20
Table 6

Analysis of Variance Summary Table for Pre-/Posttest False Alarms

Source of

variation

Sums of Mean Significance

df squares square F level

3 32.92 10.97 2.96 .10

1 .75 .75 .20 NS

3 14.92 4.97 1.28 NS

16 59.33 3.71

1 1.34 1.34 1.01 NS

1 1.34 1.34 1.01 NS

3 1.99 .66 .50 NS

3 1.99 .66 .50 NS

16 21.34 1.33

Patching technique (T)

Scenario order (A/B) (B/A)

Groups of subjects

ei

Pre-/posttest

Scenario (S)

T X P/Y

T X S

«2

Total

(P/P)

47 135.92

Table 7

Operator Average False Alarms for Patching Technique

and Pre-/Posttest

Test Column Perimeter Row Zone

Overall

average

Pretest

Posttest

Average

2.8

2.5

2.7

.5

.5

.5

.8

.8

.8

1.7

.7

1.2

1.5

1.1

1.3

The number of false alarms was not reduced significantly by the

training for any of the patching techniques tested. This result is

not surprising—the training was not intended specifically to reduce

the number of false alarms, and the false alarm rate was very low initially. To decrease the false alarm rate, an error analysis would have

to be undertaken to develop an appropriate training package.

21
Control Group. A control group of eight operators was administered

the test materials in the sane fashion as the other operators except the

control group was not given patching technique training. The purpose of

using the control group was to determine the effect due to learning (or

experience) resulting from initial exposure to scenario materials (i.e.,

I .etest). If the control group showed no significant performance increase as a result of the pretest, then, for those groups that were

trained, any performance increase observed in the posttest was attributed

to the training and not to the experience derived from taking the pretest.

Table 8 summarizes the pretest/posttest conpleteness-of-detection

results for each patching technique. The overall pretest and posttest

averages are about 25%, indicating that no performance increase resulted

as a function of the pretest exposures. The four patching techniques

show only minor fluctuations between the pretest and posttest. For this

reason, no statistical tests were computed.

Table 8

Average Completeness of Detection for Control Group Operators

Overall

Column Perimeter Row Zone averages

Test % % % % %

Pretest 28 12 26 31 24

Posttest 28 16 24 35 26

Table 9 summarizes the pretest/posttest false alarm data for the

control group. The pretest overall average was 1.0 and the posttest

overall average was .4. Because of the small number of false alarms, an

analysis to determine the statistical confidence of this drop could not

be conducted. Also, it is apparent that the number of false alarms

fluctuated from 0 to 2.5 within patching techniques. The control group

analyses do not indicate that the practice gained by initial exposure

to the scenarios (pretest condition) had a significant effect on the

posttest. Thus, Mie increase in performance found previously from pretest to posttest is attributable to the patching technique training

rather than to practice effects.

22
Table 9

Control Group Operator Average False Alarms

Test Column Periaeter Row Zone

Overall

average

Pretest

Posttest

1.0

0

.5

1.0

0

.5

2.5

0

1.0

.4

Evaluation of Patching Techniques

Completeness of Detection. The task of scoring the detection rights

was divided equally between two staff personnel prior to conversion of

the data into ooapleteness-of-detection percentages. To determine the

reliability of this procedure for this and future research,, the judges

exchanged and independently resoored the material. A random sample of

63 pairs of scores yielded a product moment correlation coefficient -of

r = +.87, which was considered acceptable.^ A frequency polygon of the

data was judged to be a good approximation of a normal curve (M = 37.0}

Ndn 36.5). The planned analysis of variance was therefore conducted.

The summary table is presented in Table 10. Target activity showed the

only significant effect of importance. The scenario effect and scenario

interaction effects, which were significant, are not easily interpretable

because scenario effect is not a pure variable but a confounding between

scenarios, sessions, and possible transfer of learning effects between

patching techniques. Scenario effects alone do not have a direct bearing

on the objectives of the present experiment. However, findings dealing

with scenario effects may be important for future scenario development.

A significantly greater percentage of targets was detected in the

low-target-activity condition (89%) than in the high (42%). This outcome indicates that operators are able to detect a greater percentage of

targets when fewer targets are present; the reason is probably the increased difficulty of working with more targets and overlapping target

activations caused by several targets passing through the grid at about

the same time. Observations made during testing and posttest debriefings revealed that the operators found it difficult during these periods

to differentiate the activations of one target from the activations of

another target. In the low-target-activity condition, however, the task

5

Because of dichotomous data, the normal distribution assumption was

questionable. Therefore, a fourfold point correlation coefficient warn

calculated at r» m+.86, which again is acceptable.

23
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24
of differentiating the activities of one target from those of smother

was simpler.

The two nonsignificant effects of importance were patching technique and grid sensor density. The patching techniques resulted in

similar detection completeness results: column, 68%; row, 67%; perimeter, 61%; and zone, 65%. These results do not support expectations,

based upon results from the evaluation of training effects, that showed

a significantly higher detection rate for the column patching technique.

Apparently, the training given on all patching techniques, plus the

little experience gained in the first part of the experiment, was enough

to counteract the initial advantage shown by the column patching technique.

No significant difference occurred for the sensor grid density variable. The 9-sensor grid condition resulted in 65% detection completeness, and the 24-sensor grid condition resulted in 68% detection completeness. These results indicate that seismic sensors (MINISIDS) employed

500 m apart in a 9-sensor grid result in the same target detection rate

as sensors employed 250 m apart in a 24-ssnsor grid, and are considerably less expensive.

The significant interactions of the scenario variable with target

activity and grid density (A X S, 0 X S, and A X D X S) indicate that

differences between the 9-sensor and 24-sensor grid densities depend on

which scenario or scenario target activity combinations occur. In some

cases, use of the 9-sensor grid resulted in a higher detection completeness, but in others the opposite result was found. Since scenario effects cannot be defined for use in an operational situation, it must be

concluded that, for the general case, there is no performance difference

between the two grid densities. Similarly, the conclusion regarding

target activity as stated above must apply in the general operational

situation. The scenario results are of major importance only in that

they show the need for controlling this variable in future research or

operational tests.

False Alarms. A frequency polygon of the total false ali rms per

operator was judged (for 8 hours of work) to be an approximation of a

normal curve (M = 5.8; Mdn 5.5). The operators made a total of 139

false alarms. The analysis of variance results are shown in Table 11.

The si j.-iificant results of importance were for target density, density

by patching technique interaction, target activity, and density by

activity Interaction.

The average number of false alarms reported by the operators was

3.875 for the 4 hours using the 24-sansor grid, and 1.875 using the

9-sensor grid. Use of the 9-sensor grid with 500-m spacing resulted

in half the number of false alarms as use of the 24-sensor grid with

250-m spacing. It had been hypothesized that more sensors closer together might provide the operator with more information and therefore

result in fewer false alarms. Instead, the additional sensor activations appear to have misled the operater into thinking that there were

25
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ore targets in the area than actually existed. It might be possible to

train the operators to use the inputs of the additional sensors, but

these results show how operators performed with the training they already

had received by the tine of the present testing.

The significant density by patching technique interaction (Table 12)

is of major importance. For the 9-sensor grid, row patching resulted in

a significantly lower false alarm rate than did perimeter patching (Table

13). For the 24-se. sor grid, the column patching resulted in a significantly higher false alarm rate than any of the other three patching techniques (Table 14). These results are consistent with those found in the

evaluation of training effects.

The significant activity variable indicates that the low-targetactivity condition resulted in a higher error rate (3.40 per operator

per 4 hours) than the high-target-activity condition (2.32 per operator

per 4 hours). The significant activity by density interaction presented

in Table 15 indicates that this higher error rate is attributable to use

of the 24-sensor grid.

Apparently, given more time to look for targets (as in low activity),

the operators tended to find false ones in the more involved and confusing 24-sensor grid.

Accuracy of Target Detection

Detection rights and false alarms were combined for the purpose of

computing accuracy values for each of the four patching techniques. Accuracy was computed using the formula detection rights divided by detection rights plus false alarms x 100,%. The accuracy measure, therefore,

answers the question, "Out of the total number of detections reporteo,

what percentage was correct?" The following accuracy percentages were

computed: column (89%), perimeter (90%), row (93%), and zone (91%).

Direction Deviation. The data were scored, and an interjudge reliability test was performed.6

A frequency distribution of the data was

judged to be an acceptable approximation to the normal curve to conduct

an analysis of variance (M - 4.5 sectors; K^-> - 4.4 sectors). The summary is presented in Table 16.

The test was performed on a random sample of direction scores. The

Pearson product moment correlation coefficient for 62 pairs of scores

was calculated at r * +.86. This coefficient is high enough to recommend the same scoring procedure for future evaluations, although improvement is desirable.

27
Table 12

Average Number of False Alarms per Half Hour by Grid

Density and Patching Technique

Operator

average

Grid for grid

density Column Perimeter Zone Row density

9-sensor .18 .33 .29 .12 .23

24-sensor .79 .33 .41 .39 .48

Average for

patching technique .48 .33 .35 .26

Table 13

Duncan's Multiple Range Test for Patching Technique

Comparison—9-Sensor Grid

Means

Row

.12

Column

.18

Zone

.29

Perimeter

.33

Shortest

significamt

range

.12

.18

.29

.06 .17

.11

.21*

.15

.06

R2 - .194

R3 - .204

R4 - .211

*p < .05.

28
*p < .05.

Table 14

Duncan's Multiple Range Test for Patching Technique

Comparison—24-Sensor Grid

Means

Perimeter

.33

Row

.39

Zone

.41

Column

.79

Shortest

significant

range

.33

.39

.41

.06 .08

.02

.46*

.40*

.38*

Rj • .194

R3 = .204

R4 -= .211

Table 15

Average Number of False Alarms by Target Activity

and Sensor Density per Half Hour

Grid density Low activity High activity

Operator

average for

grid densities

9-sensor

24-seasor

Average for activity

.24

.61

.43

.23

.35

.29

.23

.48

29
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The only significant effect found, the residual, indicates that

a significant amount of unexplained variability is present. This variability may be the result of not analyzing (not subtracting out) the

effect of the target activity variable that was such a strong variable

in the detection completeness and false alarm analysis. In the present

analysis, it was necessary to collapse the data across this variable to

fill all the cells of the statistical design.

The nonsignificant patching technique effect indicates that the

absolute deviation in the operators' reported target path was similar

for all patching techniques. The mean deviation found (4.5 sectors,

or 45°) is a substantial error if a target's future location is to be

predicted. Since target direction assessment was not emphasized in

the training provided as part of the present research, a future effort

should determine whether significant improvement is possible if additional training is provided. As mentioned earlier, the results of target direction should be generalized to the operational situation with

caution because of the atypical target paths required after the target

has passed ti:rough the grid.

Target Speed. A frequency polygon of the data shows a marked positive skew with a bimodal tendency and was judged as not approximating

the normal curve (M 93.2 m/min; Mdn = 81.5 m/min). Mean performances

are reported in Table 17 as a function of patching technique, grid density, and activity level. The larger the value in this table, the

greater the deviation from the school solution. All values in the

table are in m/min.

Table 17

Speed Deviation Scores for Patching Technique X Grid

Density X Target Activity (m/min)

Technique

9-sensor

Low

grid

High

24-sensor grid

Low High

Overall

averages

Row

Column

Perimeter

Zone

Overall averages

87

100

101

99

98

86

103

116

8

88 106

81 96

77 92

78 8o

81 95

922

96

90

-

31
The average deviations for the patching techniques varied between

90 m/min (2.8 miles/hr) and 96 Vmin (3 miles/hr). The difference of

6 m/min, which is less than .2 miles/hr, was considered to be of little

practical value.

The largest deviation difference in the table occurred in the

9-sensor-grid high-target-activity condition in which row patching resalted in a deviation of 86 m/min and perimeter patching in a deviation

of 116 in/min. The difference is 30 m/min or roughly 1 mile/hr. It

was decided that this variation in performance did not warrant the

computation of an overall analysis of variance.

Operator Confidence. The confidence analysis was based on the

difference between average confidence scores of right detections minus

average confidence in false alarms. This approach depends on the assumption that the operators have false alarms in most or all cells of

the experimental design. There were large differences in the number

of false alarms made by various operators, and also a large number of

unfilled cells. Only 23% of the cells of the experimental design held

data. A t test calculated for the overall mean (20%), t 2.67, was

significantly different from zero at the .05 level, indicating that

UGS operators can assess the probability that a target report is valid.

Operators' confidence estimates could be used to lower the false alarm

rate if higher accuracy is desired, but with some loss in the completeness of the report.

In view of the high number of cells with no data, the only way to

tabulate the data so an analysis could be conducted was by patehing

technique and operator group (eight groups, three operators in a group).

Results of a nonparametric test7conducted on the resultant data are

presented in Table 18. The nonsignificant outcome indicates there is

no difference between patching techniques in the confidence measure

used.

Identification of the Optimum Patching Technique

Five dependent variables (completeness of detection, false alarms,

direction deviation, speed deviation, and confidence) plus operator

preference were considered in identifying the most favorable patching

technique. After data collection had been completed, the operators

were asked to name the one technique they preferred to work with. Operator preference showed the largest differences between patching techniques. Of the 24 operators participating, 11 preferred to work with

the row patching technique, 9 with the column technique, and only 2 each

Friedman's two-way analysis of variance, pages 166-172 in Siegel, S.

Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill

Book Company, Inc., New York 1956.
with the perimeter and zone techniques. Since the zone and perimeter

patching techniques had no compensating advantages from the results

on the five dependent variables, they were considered for elimination.

The zone technique has the additional disadvantage of requiring more

pens and columns on the RO 376 recorder than do the other techniques.

Use of the perimeter patching technique resulted in significantly more

false alarms than did use of the row patching technique for the 9-sensor

grid condition. For these reasons, the zone and perimeter patching

techniques were eliminated from further consideration.

Table 18

Friedman's Analysis of Variance by Rank

on Operator Confidence*

Column Perimeter Row Zone

Operator 19.5 16.5 20.5 23.5

groups

2

N = 8 X »1.88 Nonsignificant at .05 level.

Siegel, S. Nonparametric Statistics for the Behavioral Sciences.

New York: McGraw-Hill Book Conpany, Inc., 1956, pp. 1666-172.

Direction deviation, speed deviation, and confidence rights minus

confidence wrongs showed no significant differences between patching

techniques. In the evaluation of training effects, column patching

was significantly superior for target detection. As mentioned earlier,

these results were probably due to the operators' greater familiarity

with this format, particularly in the pretest condition. Column patching also resulted in more false alarms than did any of the other techniques, but the difference was significant at- only the 10% level.

In the evaluation of patching techniques, the column patching

technique resulted in significantly more (twice as many) false alarms

than did the row technique for the 24-sensor condition. Although the

24-sensor grid is not recommended for the detection of vehicles, it

may have to be used for the detection of personnel or in other cases

where redundancy of sensors demands it for increased sensor reliability

or soil conditions (low and variable detection radius). Therefore, the

row patching technique was identified as preferred, both for field use

and for further evaluation.

33
CONCLUSIONS

Optimum Patching Technique

None of the patching techniques tested showed a clearcut superiority over the others on all the dependent variables tested. However,

on the basis of results for false alarms, posttest debriefings, and

equipment requirements, it was concluded that the most favorable patching technique for field use and further evaluation is the row patching

technique.

The column patching technique resulted in significantly higher

detection completeness when pretest and posttest results were averaged.

In the evaluation of training effects, operator debriefings indicated

that the operators initially felt more comfortable with the column

patching technique because it is most similar to the standard string

technique with which they are familiar. However, both evaluations indicated that more false alarms might occur when the column technique

is used. Therefore, in using operators not trained in the grid employment, the column patching technique is preferred if the commander is

willing to accept a few more false alarms for more target detections.

Using trained operators, the row patching technique is still favored.

Value of the Training

It was concluded that the patching technique training significantly enhanced operator target detection performance for interpreting readouts from seismic sensors employed in a grid array. It was

also concluded that the relatively small false alarm rate was not reduced by the training. The percentage of targets detected increased

from 36% in the pretest to 51% in the posttest. However, because only

half the targets were detected even after the patching technique training, an error analysis should be conducted and an appropriate training

package developed and validated to bring the 51% detection completeness

to higher levels.

Interpretation Job Aids

The posttest debriefing indicated that operators found the sevenstep reporting procedure, speed table, and UGS ruler consistently useful. The patching technique scales of the UGS ruler, however, were not

used as frequently as the ground distance and X-T plot time scales.

During periods of low target activity when isolated targets were easily

detectable, the patching technique scale was not heavily used. During

periods of high activity when target activations overlapped, the patching technique scale was used to help separate and distinguish the activations of one target from those of another. However, the operators

reported only limited assistance for this purpose, claiming that the

34
ruler provided only a start toward the difficult analytical task of

sorting out the activations. Also, the operators noted that the patching technique scale was most useful when they were initially gaining

familiarity with the way the sensors are deployed in the grids. It was

observed by the researchers that once this familiarity was gained, use

of the UGS ruler for this purpose diminished.

Sensor Density and Target Activity Effects

It was concluded that the 24-sensor grid array and the 9-sensor

grid array resulted in similar operator performance and confidence.

In view of the apparent savings in cost and manpower, the 9-sensor grid

(500-m spacing) should be employed for both further experimentation and

field use for the detection of vehicular traffic. (In actual practice,

the spacing of sensors would depend on the detection radiur , gain setting,

etc.)

Of the targets available for detection, a greater percentage of

targets was detected in the low-target-activity condition (89%) than

in the high-target-activity condition (42%). Further research on training using the grid employment of sensors should emphasize the highactivity condition. Until operators can handle the high target activity,

additional UGS operators will be required during high target activity.

In lieu of added personnel, intelligence estimates based on UGS will

have to be carefully increased to account for the operator underestimation of the number of targets present when target activity is high.

Field Performance Expectations

The following detection completeness and accuracy values (Tables

19 and 20) are what the commander can expect from relatively untrained

and inexperienced operators when interpreting activations of sensors

employed in a grid. The following sample means are considered to be

estimates of the population values for the respective conditions. Of

course, variations in the skill of the operator, terrain, soil conditions, noise, and target type will affect these estimates.

It was concluded that the major problem in employing sensors in a

grid array is not the accuracy but the completeness of the operator's

performance (i.e., not the false alarms but the detections).

35
Table 19

Expected Completeness Detection Performance by Grid Density,

Target Activity Level, and hatching Technique

9-sensor grid 24-sensor grid

Low target High target Low target High target

activity activity activity activity

Technique % % % %

Column 88 46 91 45

Perimeter 90 34 84 36

Row 84 48 94 42

Zone 85 39 94 42

Average 87 42 91 41

Table 20

Expected Detection Accuracy Performance by Grid Density,

Target Activity Level, and Patching Technique

9-sensor grid 24-sensor grid

Low target High target Low target High target

activity activity activity activity

Technique % % % %

Column 96 96 79 84

Perimeter 89 91 86 95

How 93 98 86 95

Zone 93 93 85 93

Average 93 95 M 92
APPENDIX A

SKETCH MAPS AND X-T PLOTS

Target 2

Figure A-l. Sketch map for column patching using

a 9-sensor grid.

37

<

DC

<

Q

OL
Artillery er..:

11 min.

V A '(

. mw*.

9 min.

i :i n

Hellcopterc:;^^

7 min.

M \f. 1!l

/ I l(»; ilf

IS 1/

'.'ii n\ vi •.M 'M m

H Y. Ill

IV »I )9 BV Wi

>i) H M M vii n

rigun A-2. x-T plot for ooluan patching

using a g-Mnoor grid.

.
Target path 5 Taracst path <l

Target path C

---fA A ;{!>:—iff

n I 'PÄplumn IV Column III/ \Column IV. vColuihn V

/

I \\ ' * ' \ / \

3

10 V

n 17 23

i • i • i • i' i • i' r' i' i • i

I0U 200 3DU <I00 OUO 600 700 600 000 1000

Ntm

rigux« A-3. Ikatoh map for ooluan patching

using a 24-aanaor grid.

39 ,
Artilleryj—^v

13 min. S'

Helicopter

7 min.

9 min.

Figure A-4. X-T plot for column patching

using a 24-sensor grid.

40
Target 2 Path

Target 3 Pat

Figure A-5. Sketch map for perimeter patching

using a 9-sensor grid.

UJ

•X

re

ui

on

<

41
X

JL

X

;

i •

ii

i i

a ;» n 13 U

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21; V3

.,..,UJJ

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f

-

;

i. i. .

ri?

1 j L

|}5 8/; 27

Figur« A-6. X-T plot for p«riMt«r patching

using • 9-MMor grid.

42
(

Figur* A-6. Continued.

43
Target

Target 5 Path

6 p\thN

o

\c

^-Target 4 Path

- fentneter I

14

Perimeter II • — ~ -~o—•• - — —o16 17

V Peri meter III

\i\ / \

V \ i

\

o o < h \J 24

i \\ /

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s

i K^

o.

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7k

( 13

OUT

i-^-^if—•- =14= %

(/i

rr.

01

1 T T T T T

I0U 2UÜ JUU 400 UM COO 700 600 300 1000

icitn

Figure A-7. Sketch map for perimeter patching

using a 24-sensor grid.

44
15 min

7 min.

Artillery

- ^ i \ 9 min.

rä—l Helicopter

Figure A-8. x-T plot for perimeter patdiing

using a 24-sensor grid.

45
Target 2 path

Target 3 jr)a

<

in

o:

Figure A-9. Sketch map for row patching

using a 9-sensoz grid.

46
1/ )'.>

Artillery C::::.

11 min.

Helicopter

7 min.

'^i ?;i K 'i? »!

20 n\ J>4| 20- 28: $0

min.

II n- 23! U7 a?;

Figur« A-10. x-T plot for row patching

using a Q-sensor grid.

47
Target 5 path

S

3

00

48
'> 21 'M K V V).

I

;-•

-

:l

26 2H 30

9 min.

7 min.

119: 21: ii H 27; 29;

Figure A-12. X-T plot for row patching

using a 24-aenaor grid.
-Target 2 Path

Target 1 I'ath

Target'3 Path

Figure A-13. Sketch map for zone patching

using a 9-sensor grid.

<

re

•a:

s:

50
9 min

Helicopter

7 min.

»£3oi i?i Mi VJ :iir 20 92 74 M 70: 30

IS! 15: 17' 21 23: PS 27 29;

ä 0; 10; )2 14; 1<5; ;iC 20 22; 74 2öi ?o; 30

Figur« A-14. X-T plot for son« patching

using a 9-sensor grid.

51
Target 5 path

Target 6 path

DC

00

Figure A-15. Sketch map for zone patching

using a 24-sensor grid.

52
Artniery1

-'

15 min.

Helicopter

7 min.

9 min.

Plgure A-16. x-T plot for patching

using a 24-sensor grid.

53
APPENDIX B

TARGET QUALITY DISTRIBUTION-NORFOLK SCENARIOS

Field 1

Tape

Field 2

Total

Good* Fa1rb Poorc Good Fair Poor

5 5 6 1 7 3 2 28

8 4 2 2 7 6 1 28

5 4 3 3 7 5 3 27

Training 4 Training 23d

5 7 4 5 6 4 4 30

23 20 15 27 18 10 113

Note. From Edwards et al.. 1977.

a

Five targets were detected between 67% and 100% of the time. Th"* were

good or easily detected targets.

Five targets were detected between 33% and 66% of the time. These were

of medium difficulty

c

Six targets were detected between 0 and 32% of the time. These were difficult targets.

Data on the difficulty levels of training targets were not available.
APPENDIX C

ORIENTATION BRIEFING

Monitor: Paraphrase the following.

I want to welcome everyone here today and thank you for coming. We are

glad that you could make It and can participate In the exercises we have

planned. We think you will find it worth-while. You will be participating

In a five-day program and we will be spending the next several hours briefing

you and giving you an orientation as to what its all about. Before going

any further I want to Introduce myself and my associate and find out who you

are.

Introductions

•Our purpose in coming here is to evaluate, with your assistance, four

different ways in which seismic sensors in a grid deployment pattern can be

patched to an RO-376 readout device. We have been asked by the Army Research

Institute for the Behavioral and Social Sciences and the Department of the

Arv to administer this exercise to you. The Army Is Interested in the

development of Improved displays of unattended ground sensors to maximize

Information output and make the Job easier for you. Your task in this study

will be to act as a sensor operator and Interpret various X-T plot presentations using different patching techniques. Many of the skills you have acquired

in school and on the job will apply to these tasks, however, some of the

patching techniques will be new to you and details such as measurement and

reporting procedures will differ. In these cases, training and Instructions

will be provided. If at any time during your work with us, you do not understand something or you are not sure of what you are to do - ASK. You will not

be penalized and asking might prevent your having to repeat some of your work.

We'll be using simulated RO-376 drive mechanisms. If any of the equipment

appears to be malfunctioning. Inform one of us immediately.

Previous studies of this kind have dealt primarily with sensor strings

emplaced along roads, trails, or other infiltration routes. Here, we are

applying seismic sensors to an area intrusion problem. In such a situation,

we would have sensor fields emplaced over a wide geographical area that an

enemy force would utilize should he elect to maneuver his forces cross-country

and not along the existing road network. Such a situation could be expected

in a mid-intensity conflict in Western Europe. This type of sensor field

would be used to help detect and identify different tactical maneuvers such as

reconnaissance probes, feints, or major attacks and is referred to.as a gated

array or grid array.

For our experiment, we have taped actual sensor activations from a grid

array during field exercises using various types of targets. The target

activations were collected under simulated battlefield conditions complete

with noise activations produced by artillery fire, helicopters, and wind.

These tapes will not be played back to you in real time, but in the form of
pre-prepared X-T plot scenarios. You will interpret these scenarios and extract information using our procedures and forms. Since we know where and when

target activations actually occurred, we can score your reports for accuracy

and thereby determine which patching technique can best be used in this

particular situation.

Each of you will participate every day this week. During that time, you

will be given training on each patching technique and given practical exercises

to familiarize you with the patching technique. Then, you will work with each

patching technique for two hours. During the program you will be given

appropriate breaks, lunch, etc. If you cannot be here during the time presently

scheduled, tell us now so we can reschedule you. You must be here for all

scheduled times or we cannot use your results.

I would like to emphasize that we are not giving you a test to see how

good an operator you are. The purpose of this study is to determine which

patching technique is best for extracting target information. Our primary

concern, therefore, is not in how good you are as an operator. However, you

and your superiors are interested in how good you are. I am sure they will

not base the next promotion on how well you do these practical exercises.

Still, these activations are actual activations recorded in the field and

your accuracy in interpretTng is one indication or example of what you can do.

You will be able to compare what you can do to what others did as a group.

You will be able to get your score and the group average from your commanding

officer. He will be able to objectively assess you against the others on this

one sample of one of your duties. However, there are no standards of

performance—even if you do worse than everyone you still could be a competent

operator.

All we ask is that you interpret the X-T plots to the best of your ability

and try to make sense out of what sometimes might appear to you to be rather

difficult. Let me stress that we have tried to make these records as realistic

as- we could.

You are important because you as a group represent the hundreds of

specialists that have graduated and will graduate from the UGS school. Amy

deployment plans for U6S equipment and personnel will be partly influenced

based upon what ^ou can do.

Monitor: Begin the briefing on grid deployment patterns.

58

•
APPENDIX 0

INTRODUCTION TO THE GRID EMPLOYMENT

PATTERN (LECTURE/DISCUSSION)

The Grid array consists of unattended ground sensors (UGS)

ployed in a matrix within a designated field area as opposed to the

string pattern in which UGS are employed in sequential alignment along

a roadway. The grid array can be used for area intrusion surveillance

problems encompassing entire border areas or smaller gate (gap) areas.

where coverage by radar or other means is limited or not feasible.

It is designed to maximize the probability of detecting and acquiring

enemy forces intruding in any portion or in any direction within a

covered geographical area. The UGS' in the grid array are ployed

in a systematic way with pre-planned distances between the sensors

so that information extraction is enhanced.

For this exercise we are utilizing a sensor grid consisting of

9-sensors, each sensor is 500 meters apart. X-T plot readouts of

various target runs through this sensor grid will be presented to you.

Your task will be to detect these targets, track their path through

the sensor grid, and provide further information about them. You

have already received some information concerning this task. At

this time, we would like to provide you with further information

which should aid you in monitoring sensors in the sensor grid.

After you have detected what you believe is a target, your next

objective is to chart or trace its path across the sensor grid. In

the past, when you have worked with sensor strings, targets coming down

a road will generally activate all the sensors in order. HoweveV", in

a grid formation, the targets may come from any direction and take any

course across the grid. They will also come closer to some sensors

than they will to other sensors. This presents more of an interpretation

problem to the monitor.

We have prepared some examples of targets entering the sensor grid

from different angles and taking different paths through the grid.

We also have copies of the sensor activations caused by these targets.

Example 1 - Here is a target entering a Grid, and crossing the first

line of sensors, passing directly over one of them.

HI

3 •

Activations would first appear on the middle sensor. As the

target proceeds the sensors to the right and left would activate for a

59
shorter period of time. The sensors to the right and left would cease

activating before the middle sensor thus the activations would appear

as below:

i a '

Example 2 - A target traveling in parallel with a line of sensors

would to

f • s • (*•

appear like this on the X-T plot.

This would be very similar to the activation of a sensor string

with a stair-step pattern. All sensors would activate for approximately

the same length of time.

Example 3 - Shows a target approaching a line of sensors at an oblique

angle. -^^

Here the left-hand sensor would activate first, followed by the

middle sensor and the right-hand sensor, however, the middle sensor

would be activated for a longer time because the target came

closest to it.

f!

r

60
In all three.of the above examples, other groups of sensors in

the grid would in the same way, indicate the path of the target as

it travelled the grid. A good general rule to remember when monitoring

a sensor grid is to look at the overall pattern of the sensors being

activated, and then make a determination from this overall pattern,

where the target is traveling.

At times, there may be more than one target present in the

sensor field. Monitors should be able to detect this again by studying

the overall pattern of activations. *. ,, * ,Ä

» 10 0 11 i 12

Example: 13 14 15

In this example, with sensors activating on the left and right

but not in the middle, it must be assumed that two targets are present.

In these situations it is important to take note of sensors that are

not activating as well as sensors that are activating.

10 11 12 13 14 15
APPENDIX E

TEST PROCEDURE TRAINING

Our purpose in coming here this week is to evaluate several display and

target conditions for seismic sensors patched to an R0376 readout device.

We want to determine how different display and target activity conditions

affect your ability to detect and report on targets. You are all familiar with the idea of employing sensors in a string configuration along

a road. Now you will be working with sensors employed in a grid configuration and in a field such as that shown in the top half of Figure 1.

Pull Figure 1 and Figure 2 out of this booklet and lay them on your desk

where you can see both of them clearly. As you can see. Figure 1 shows

9-sensors employed in a grid which is 1000 meters on a side. Compare

this with Figur? 2 which shows 24-sensors also ployed in a grid 1000

meters on a side. The sensor identification numbers are shown in both

figures. In an operational situation, grids this size could be a small

section out of a long sensor network or they could be placed between

natural barriers. For our purposes, assume that each grid is located

in a flat partially wooded field between rugged terrain on the left and

marshy terrain on the right.

The expected direction of enemy approach is from top to bottom. As you

can see, target 1 in both grids has come from the expected direction

and has passed through the center of the sensor field. In the case of

the 9-sensor grid, the target has passed over sensors 11, 15, and 14.

In the case of the 24-sensor grid, at least three more sensors are

involved. As you can see by inspection, the target has passed over

sensor 11, between sensors 9 and 23, and then over sensors 15, 13, and

14. These additional sensors may help when reporting on a target from

an X-T plot.

The bottom half of Figures 1 and 2 shows blank spaces and a seven step

procedure which you will use to report on targets.

YOUR JOB DURING THIS EXERCISE WILL BE TO FIND TARGETS ON X-T CHART PAPER

AND FOR EACH ONE THAT YOU FIND, FILL' IN THE BLANKS FOR THE SEVEN STEPS.

You will be workinq with X-T chart paper and targets simular to that

shown in Figure 3 (second to the last page). Study Figure 3. Notice

the pen/ID chart at the side showing that the X-T £en numbers correspond to the same sensor grid numbers.

At this time we will define what we mean by the word "target". A

target is any vehicle or personnel activity in the field which is

distinguishable from other personnel or vehicle activity. For example,

three tahks 50 meters apart traveling in a convoy formation would be

one target as would a tank traveling alone. These two targets may

enter different sections of the grid simultaneously or at different

times. Because their activation patterns can be separated from one

another, they are classified as separate targets.

63
Target 1

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Figure 2. 24-sensor target log.

65
Step 1 on the Target Log - TARGET NUMBER

Target activations are presented in Figure 3 for the same target as they

might occur for both the 9-sensor and 24-sensor grids. You will notice

that the target activations are circled and numbered. This is exactly

what we want you to do when you detect a target. Circle all the target's

activations and number them with the number of that target. Since this

is the first target, the activations caused by this target are labeled

target 1 and a number 1 is recorded in Step 1 of the Target Log as shown

in both the 9-sensor grid and 24-sensor grid.

Step 2 on the Target Log - ESTIMATED DISTANCE (Meters) ^ /

Study the characteristics of the sensor activations and draw the probable

path of the target through the sensor field on the Target Log grid.

Estimate the total distance (in meters) that the target traveled through

the sensor field using the distance scale shown. Estimate the distance

to the nearest 50 meters - for example, 200 or 250 meters. In the case of

Target 1, the path has already been drawn on both grids. The estimated

distance is about 1050 meters and this has been recorded in the Step 2

blanks.

Step 3 on the Target Log - CONFIDENCE LOW- > HIGH

This step seeks to answer the question, "How confident are you that what you

think is a target really is a target?" Record your confidence using the

following four-point scale:

100% - This means you are positive or certain.

75% - This means you are highly confident, but not positive.

50/50% - This means that you think it probably is a target, but you

are uncertain - it may or may not be a target.

25% - This means that you have only a suspicion, but it should be

recorded and checked out. You have low confidence that

this is a target.

A 100% confidence has already been placed in this column on the Target Log.

Step 4 on the Target Log - FIRST AND LAST SENSORS ACTIVATED

Record the sensor number of the first sensor which activated when the

target entered the grid and the last sensor which activated when the target

left the grid. As shown for both the 9-sensor and 24-sensor grids, the

first sensor is 11 and the last is 14 which is the same as pens 11 and 14

on the X-T plot. Check this yourself by looking at the pen/ID chart on the

right-hand side of Figure 3. This information is important because it

will be used for the next steps.
Step 5 on the Target Log - MID-POINT TIME (min) DIFFERENCE

On the X-T plot, find the mid-point of the activation patterns for the

first and last sensors. Now determine the time difference between the

two mid-points. This i«i done directly off the X-T chart paper as shown

in Figure 3 for both the 9-sensor and 24-sensor grids. Remember, there

are 2-mlnutes between lines (rows) on the X-T chart paper. Estimate this

time to the nearest half minute, for example 3 or 3.5 minutes. For

target 1 the mid-point time difference is minutes. Check your answer

with the appropriate Target Logs for the two grid sizes. If you missed

it, reread this section ^nd/or see the Training Monitor.

Step 6 on the Target Log - ESTIMATED SPEED

Having an estimate of the time that a target traveled through the sensor

field and the distance that was traveled will permit you to get an estimate

of the speed of the target. Only an estimate is possible, however, since

you will not know for sure how close the target traveled to any of the"

sensors. It is possible to obtain a more accurate estimate of speed when

the sensors are deployed along a road because the target is normally

traveling on the road and the distance between the sensor and the road is

known.

An estimate of speed can be obtained by using the speed table provided for

this purpose. The speed table (Table 1) is enclosed in plastic and will

remain at your desk. To use the speed table, find the time column (using

the answer from Step 5) along the top. Line this up with the distance

row (using the answer from Step 2) along the left-hand side. The place

where the column and row converge gives you the speed. In the case of

target 1 the speed is . Check your answer with the one already provided

in the Target Logs. If you missed it, reread the instructions and/or see

the Training Monitor.

Step 7 on the Target Log - TARGET TYPE

Step 7 requires a judgment as to whether the target is vehicle or personnel.

As a rule, if the target is traveling 150 meters per minute or faster, label

it "V" for vehicle. Any speeds lower than this are usually labeled "P" for

personnel, but of course this judgment could be incorrect since it could

also be a slow moving vehicle.

Now you will receive practice on what you have just learned concerning the

SEVEN STEP target reporting procedure. Study target 2 presented on the

X-T plot in Figure 4. Target 2 is the 9-sensor grid target. Tha sensor

ID and pen number combinations are the same as those for Figure 3.

When you have completed all seven target reporting steps for target 2,

start working on target 3 of the 24-sensor grid. These targets are not

the samel When you are finished with target 3 take your booklet to the

Training Monitor.

If you feel you need to review the test procedure before working the practice

targets, do so! If you have any questions, ask the Training Monitor at

Station 1 or Station 2.

67
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69
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70
OBJECTIVE

APPENDIX F

ROW PATCHING TECHNIQUE TRAINING .— .—___ __ —, 1—

To familiarize you with how the row patching technique is used with a grid

employment pattern arid to train you on how to use it to detect and report on

targets using the seven-step reporting procedure.

DEFINITION OF THE' ROW PATCHING TECHNIQUE

For the row patching technique, the sensors are grouped into horizontal rows

on the grid and patched this way on an X-T plot. The top half of Figure A

presents a 24-sensor grid which shows the sensors grouped into five horizontal

rows, from Row I to Row V. The bottom half contains the seven-step reporting

procedure that you are already familiar with. Notice that the sensor numbers are

shown. At this time pull Figure A out of this booklet, write your name in the

upper right-hand comer and place it on your desk where it is clearly visible.

PART I 24-Sensor Grid

Examine the 24-sensor grid of Figure A more closely. Study which sensors

have been assigned to the various rows. Study which sensors are in each row and

fill in the blanks as you get to them.

Row I is composed of sensors 1,2, 3, 4, and 5.

Row II is composed of sensors 6, 7, 8, and .

Row III is composed of sensors 10, 11, 12, 13, and 14.

Row IV is composed of 15, 16, , , and .

Row V is composed of sensors 20, , , , and .

You will notice that all the rows have five sensors except Row II which has

four. There is no special reason why there are only four sensors in this row —

they were just ployed that way. Other sensor ployment patterns could have

been used. The important point is that you understand that the sensors are grouped

into rows.

DETECTION OF TARGETS

Any target that enters this grid will have to pass through or around one or

more of these rows. What this means to you is that you will be able to detect and

report on targets by observing what activation activity is taking place in each

row. In other words, any target entering or leaving the grid or going around the

side of the grid will have to activate sensors in one or more rows.

Now look at the X-T plot in Figure B. The sensors that you have just studied

in the grid are each patched to a pen of the same number on this X-T plot. Pen 1

71
STEP 1

TARGET

NUMBER

STEP 2

ESTIMATED

DISTANCE

(meters)

STEP 3

CONFIDENCE

LOW HIGH

25,50/50,

75,100

STEP,4

FIRST AND

LAST

SENSORS

ACTIVATED

STEP 5

MID-POINT

TIME(niln)

DIFFERENCE

STEP 6

ESTIMATED

SPEED

STEP 7

TARGET

TYPE

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Figure A. 24-sensor target log (learning targets).

72
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Figure B. X-T plot for row patching using 24-sensor grid

(learning targets).

73
on the X-T plot, therefore, refers to sensor 1 on the grid, etc. Throughout

this lesson the terms "sensor" and "pen" will be used interchangeably.

Find your job aid (UGS Ruler). One side of the ruler has a 24-pen scale

and a 9-pen scale. You will notice that the 24-pen scale is broken down into

five groups. When laid on your X-T plot each of these groups represents a row

of sensors.

BY PLACING THIS SCALE ON THE X-T PLOT YOU WILL BE ABLE TO QUICKLY

DETERMINE WHICH SENSORS IN EACH ROW HAVE ACTIVATED.

Now lay this scale on the X-T plot and line it up properly. Notice how quickly

you can tell which sensor in each row is activating. In many situations, this job

aid may help you to 1) detect a target, 2) determine whether the target has traversed

the entire grid and, 3) determine if more than one target is traveling through or

around the grid.

Go back to Figure A. Figure A shows the paths of three targets which we will

una•/••». As you can see, these targets passed through or around various rows and

acthvfted sensors in these rows. Place your job aid on the X-T plot for each of

th^.e thrpc targets and briefly note which pens of each row have activated.

a. Example 1 - Target 1

For target 1, the pattern of activations provides good examples of interpretation principles. All five sensors in Row I have activated in a noticeable stairstep pattern. This indicates that the target traveled somewhere along the row

itself as though the sensors were deployed along a road and this were a trail/road

monitoring situation. However, this situation is perpendicular to the primary

watch direction of the grid and therefore may not be expected to occur frequently.

Because no internal sensors activated, especially sensors in Row II, you should be

able to conclude that the target probably did not penetrate the grid, but merely

traveled ilong the top as shown in the target 1 path of Figure A.

The last three sensors to activate (sensors 14, 19, and 24) are the last

sensors on the right-hand side of Rows III, IV, and V respectively. Again, since

only outer sensors activated, you should be able to conclude that the target probably

did not penetrate the grid. Also, because of the stairstep pattern formed by

sensors 14, 19, and 24, it can be concluded that the target passed these sensors

one right after the other at a relatively constant speed.

It is important to also notice that each sensor activated for about the same

period of time (2 minutes). This indicates that the target had entered the

detection range of each sensor for about the same period of time. Of more importance,

74
this condition Implies that the target traveled the same distance away from each

sensor. If the activations lengths differed, this would imply that the target

traveled closest to the sensor with the longest activation pattern. Other factors,

however, might be responsible for this. For example, the sensor with the longest

activation pattern might have a higher gain setting.

Take a few minutes now and study tlie row patching technique. Start with

the X-T plot and retrace the path of target 1 on the grid and try to visualize

the relationships that we have just discussed.

b. Example 2 - Target 2

Turn your attention now to target 2. First look at the X-T plot, then the grid.

Sensor 1 of Row I shows the first activations. The sensor which shows the first

activations will usually tell you the closest point in the grid where a target

first made contact by entering or going around the grid. The word usually is used

here because in the field another sensor maj^ activate first even though it is further

away from the target because of detection range differences. Next, sensors 6 and 7

of Row II activated. This indicates that the target has penetrated Row II and

traveled somewhere between sensors 6 and 7. The fact that the activation patterns

of sensors 6 and 7 are roughly the same length (1 minute) implies that the target

probably traveled an equal distance away from them. Notice on the grid that this

is where the target 2 path has been drawn.

The next sensor to activate is sensor 12 of Row III. This Indicates that the

target moved out of Row II and into Row III. Now look at the first four sensors

that activated. Notice that sensors 1 and 12 have longer activation patterns than

sensors 6 and 7. What might be the reason for this? How could it help you in

tracing the target's path through the,grid?

YOU MUST USE JUDGMENT IN TRACING A TARGET'S PATH AND BE ABLE TO USE

CLUES FROM THE LENGTH OF ACTIVATIONS.

The lengths of these activations Indicate that the target came closer to

sensors 1 and 12 than it did to sensors 6 and 7. Looking at the grid and the

position of these four sensors, we can see that the target passed over sensor 1,

went between sensors 6 and 7, and went over sensor 12. The target's path, of

course, has been drawn along that route as you can see in Figure A. After sensor

12, the next sensor to activate is sensor 18.

SINCE SENSOR 18 ACTIVATED, YOU KNOW THAT THE TARGET PASSED NEAR OR

THROUGH ROW IV.

The last sensor to activate, sensor 24, indicates that the target passed

near or through Row V and probably exited on or near sensor 24. As you can see,

75
we have drawn its path in the 1< r half of the grid passing directly over

sensor .

Now look at the X-T plot for target 1. Notice the stairstep pattern as

the target progressed through the grid. You can almost draw a straight line

between the mid-points of these activation patterns. This indicates that the

speed of the target was constant between the rows.

Another aspect of monitoring to keep in mind is single targets versus multiple

targets traveling through the grid. Maltiple targets are two or more targets

traveling around or through the grid at about the same time and whose activations

may or may not overlap on the X-T plot. Try the following exercise while still

looking at the X-T plot. Imagine that target 1 and target 2 are starting at the

same time and progressing through the grid at the same time. In your mind, superimpose target 2 onto target 1 so that the pen 1 activations overlap. Now, actually

fill in the remaining activations of target 2 with your pencil or pen. This will

only take you several minutes. Bq careful as you fill in the activations to reproduce the same time relationships of target 2. Now look at the combined activations of both targets carefully. If you had just now seen these activations for

the first time would you be able to tell that two targets were involved? Vlpuld you

have been able to separate the one long activation pattern on pen 24 into two

targets? Remember, several targets can travel through a grid at the same time or

close to the same time. If the enemy tried this tactic do you think that.you

would be able to distinguish and report on the separate targets? Take a few

minutes and study the combined activation patterns in relation to the paths of

these separate targets on the grid. *

c. Example 3 - Target 3

Look at target 3 on the X-T plot. Line up your UGS ruler on the X-T plot

directly under target 3. Using your UGS ruler to help you with your answer, how

many rows are involved with this entire target ? Judging by the activations

that you can see, the rows involved are I, II, IV, and V. Knowing this gives you

an idea of where the target traveled. Now look at the sensors in Row I that

activated. Sensor 5 is the first to activate closely followed by sensor .

Judging by the length of the activation patterns, which sensor would you say

was closest to the target 7 In a situation like this, the probabilities are

in fa^'or of concluding that the target was further away from sensor 4 than sensor 5.

r 9 is next tp activate indicating that the target has penetrated Row

II v p'-jbably heading in a downward direction, but slightly to the left as

76
Indicated by the path drawn for target 3 on the grid. The next sensors to activate

are sensors 17 and 18 of Row IV. What happened to Row III? Did the target fly

over it? The last sensor to activate is sensor 22 indicating the target passed

through Row V.

To know which sensors were closest to the target for tracing its path, check

the length of the activation patterns and this usually will give you a good

indication of how close the target came to each of them. According to the X-T

plot, of the three sensors 17, 18, and 22, the target passed farthest away from

sensor because of the smaller length of the activation pattern.

Take a few minutes now and study target 3. Start with the X-T plot and

retrace the path of the target on the grid and try to visualize the relationships

that we have just discussed.

Now superimpose target 2 which you studied previously onto target 3 so that

they start in the same time frame. With your pencil or pen, fill in the target 2

activations in the same manner that you did previously with target 1. This will

take you several minutes to do as before. Now look at the combined activations

carefully. If you had just now seen these activations for the first time, would

you be able to tell that two targets were involved? Lay your job aid on the X-T

plot. Does the job aid help you in distinguishing between these targets? Take a

few minutes and study the combined activation patterns in relation to the paths of

these separate targets as shown on the grid.

-

ESTIMATED DISTANCE MEASUREMENT

Once you have detected a target on the X-T plot, numbered it, and drawn what you

think is the path of the target on the grid, you must estimate the distance of that

path. This, of course, is step 2 of the seven-step procedure that you learned

previously.

For this task you will find it helpful to use the other side of the UGS ruler.

Take your UGS ruler and look for the scale which is labeled "Distance in Meters."

To use this scale, place it along a target path that has been drawn on a grid and

measure the length of the path to the nearest 50 meters. Remember that the path

of an actual target traveling across country will never be a straight line because

of turns in the horizontal direction to avoid obstacles and inclines (hills) in

the vertical direction.

BECAUSE OF HILLS AND OBSTACLE AVOIDANCE, ALWAYS OVER-ESTIMATE THE DISTANCE

IN METERS THAT YOU GET FROM THE UGS RULER.

Using your UGS ruler, measure the target paths drawn in Figure A and check

your estimate with the answers already provided. If your answers differ from the

given answers by over 100 meters, consult the Training Monitor.

77
MID-POINT TIME ÜIFFEfOJCH

As you learned previously, an estiimte of target speed can be made only by

knowing the 1) distance that the target 'raveled through or around the grid and

2) the amount of time that the target spont in the grid. Step 2 of the sevenstep procedure requires the estimated distance. Step 3 gives you confidence as

to whether you feel you in fact have detected a real target. Step 4 requires you

to record the n-umbers of the first and last sensors which activated for the

target.

Step 5 requires you to find and mark (on the X-T plot) the mid-points of

the activation patterns of the first and last sensors which activated and record

the time difference. All considered, the time difference between these two midpoints probably gives you the best estimate of how long the target was In the

grid than any other method. An easy way to estimate this mid-point time difference

is to use a scale.

Check the scale on your UGS ruler which is labeled "Time In Minutes." The

scale Includes 0 to 15 minutes and should be adequate for measuring most actlvation patterns that you will be working with. To use this scale simply measure

the distance between the two mid-points as though It were a ruler and you were

measuring Inches. Read the time to the nearest 1/2 minute. This answer would be

recorded in the Step 5 blank.

Take a few minutes now and check the mid-points of the first and last sensors

of targets 1, 2, and 3 on the X-T plot. Measure the mid-point time differences

with your UGS ruler and see how close you come to the school solutions provided

in Figure A. You may feel that using the UGS ruler for this measurement Is not

needed because the answers can be sight-read, but remember that these learning

targets were intentionally simplified for training purposes and field-collected

targets will be more difficult. In the event that your answers differ by over

1/2 minute from the given answers, consult the Training Monitor.

SPEED CALCULATION

Step 6 requires an estimated speed and Step 7 requires a Judgment to be made

concerning the type of target whether vehicle or personnel. In order to save

time and avoid arithmetic errors, ym should use the Speed Table which you have

already been taught to use.

78
PRACTir.E TARGETS

Figure C presents a blank 24-sensor grid Target Log. Pull it out of your

booklet, write your name in the upper right-hand corner and place it 1n a handy

area. Take your other Target Log (Figure A), fold it in half and place it under

your papers where it will not get in the way.

Figure D presents an X-T plot of operationally collected targets for you to

practice on using the patching technique and principles that you have Just learned.

Study this X-T plot for targets. For each target that you detect, remember to

circle all the activations associated with that target, number it. and fill in

the seven-step procedure in the Figure C Target Log. The school solutions for

the Figure C Target Log is presented in Figure E and the school solutions for the

X-T plot is presented in Figure F. Do not look at the school solutions prematurely,

but attempt to detect targets and fill out the seven-step procedure on your own.

DO NOT LOOK AT THE SCHOOL SOLUTIONS UNTIL YOU HAVE DONE YOUR BEST IN

DETECTING AND REPORTING ON TARGETS.

Use all three functions of your UGS ruler; sensor patch groupings, distance

measurement, and time measurement. Within the X-T plot there is one example of

helicopter activity and one example of artillery activity. See if you can spot

these without looking at the school solution. Artillery activity usually yields

a thin band of one or two activations across many sensors located in the area.

Because the seismic disturbance of the helicopter is in the area longer than the

concussion of artillery activity, it will usually produce a grouping of at least

two or more activations across many sensors located in the area.

As you work through these operationally-collected practice targets, remember

that they are not the sterile, ideal examples which were prepared to help you understand the characteristics of a particular patching technique. Th6y contain various»

sources of background noise and the effects of malfunctioning sensors and variations

in sensor detection range due to gain setting, ground/terrain conditions and

weather. To be able to do a good UGS reporting job, you must learn how to detect

and extract target information from X-T plots collected in the field.

Consult the Training Monitors when you feel the need. When you are finished

with your practice targets, take your work to the Training Monitor. He will

determine whether you need additional targets for more practice and/or review.
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STEP 2

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DISTANCE

(meters)

STEP 3

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25.50/50,

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STEP 4

FIRST AND

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ACTIVATED

STEP 5

MID-POINT

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STEP 6

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Figure C. 24-sensor target log (practice targets).

80
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Figure D. X-T plot for row patching using the 24-sensor

(practice targets).

grid

81
Target 5 path

«Target 4 path

8

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STEP 6

ESTIMATED

SPEED

STEP 7

TARGET

TYPE

STEP 1

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Figure E. 24-sensor target log (school solution).

82
29

7 min.

30

29

9 min.

Figure F. X-T plot for row patching using the 24-sensor grid

(school solution).

83
PART II 9-SENSOR GRID

Instead of discussing a 24-sensor grid. Part II of this lesson discusses a

9-sensor grid. In general, the same detection and target reporting techniques

will be used that you learned for the 24-sensor grid. However, because of the

fewer number of sensors Involved, the appearance of the activation patterns

will differ. In most cases, this difference will be a smaller, more compact

activation pattern with less sensors activating.

Figure G presents a 1,000 meter square 9-sensor grid Target Log which shows

the sensors grouped into three rows. At this time, pull Figure G out of this

booklet, write your name in the upper right-hand comer and place it on your desk

where it Is clearly visible. As with the 24-sensor grid, the sensor numbers shown

are also the X-T pen numbers. Study which sensors are in each row by filling in

the following blanks when you come to them.

Row I is composed of sensors 1,2, and 3.

Row II is composed of sensors 4, , and .

Row III is composed of sensors , , and .

TARGET DETECTION

The three example target paths presented in this 9-sensor grid are exactly

the same as those presented in the 24-sensor grid (Figure A). Because the targets

are exactly the same, you will be able to compare the 24-sensor X-T plot presentation with the 9-sensor plot presentation. Feel free to do this at any time in

which you think your understanding can be enhanced. Find your UGS ruler. You

will notice that the 9-pen scale is divided into three groups of sensors. You

will see that the Row I sensors are patched to pens 1, 2, and 3. The Row II

sensors are patched to pens 4, 5,, and 6. The Row III sensors are patched to

pens 7, 8, and 9.

a. Example 1 - Target 7

Place your job aid on the X-T plot (Fig H). Notice which sensor(s) of each row

activated. A stairstep pattern occurred with this target as with the 24-sensor

grid except that less sensors activated because less were available. Even though

less sensors activated, you could perhaps trace as accurate a target path with this

9-sensor grid as you could with the 24-sensor grid.

Since only outer sensors activated, you should be able to trace a target path

roughly similar to the target 7 path shown in the 9-sensor grid (Figure G). Also,

the fact that each sensor activated for about the same period of time, i.e., two

84
tarye/f 8 path

STEP 1

TARGET

NUMMER

STEP 2

ESTIMATED

DISTANCE

(mcLcrs)

sni' 3

(.oNnieja:

low HIGH

25»50/50,

75,100

STEP 4

FIRST AND

LAST

SENSORS

ACTIVATED

STEP 5

Min-POINF

TIME(min)

DIFFERENCE

STEP 6

ESTIMATED

SPEED

STEP 7

TARGET

TYPE

1- uoo loci /,7 10

9*00

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Figure G. 9-sensor target log (learning target),

85
7 min.

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Target 8

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5 5

6 6

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8 8

9 9

/ I

Figure H. X-T plot for row patching using 9-sensor grid

(learning targets).

86
minutes, implies that the target was approximately the same distance away from

the sensors. Of course, you would not know exactly how far away that distance

was, but you would be able to trace a path in the proper direction and area.

Take a few minutes now and study the relationships between the X-T plot and

the path drawn on the 9-sensor grid for target 7. Review the same principles that

you learned in the 24-sensor grid discussion.

b. Example 2 - Target 8

In this example, place your job aid on the X-T plot and follow the progression

of the target from one row to the next as it passed through the grid from sensor 1

to sensor 9. Notice the length of the activation patterns and the time difference

between them. What does it mean when the activation .lengths are similar? A stairstep pattern of activations as in this case implies that the target was traveling

a constant speed.

Remember to keep in mind single targets versus multiple targets traveling

through the grid. Try the following exercise while still looking at the X-T plot.

Imagine that target 7 and target 8 are starting at the same time and progressing

through the grid at the same time. In your mind, superimpose target 8 onto target 7

so that the pen 1 activations overlap. Now, actually fill in the remaining activations of target 8 with your pencil or pen. Be careful as you fill in the activations

to reproduce the same time relationships of target 8. Now look at the combined

activations of both targets carefully. If you had just now seen these activations

for the first time would you be able to tell that two targets were involvod? Would

you have been able to separate the one long activation pattern on pen 24 into two

targets? Remember, several targets can travel through a grid at the same time or

close to,the same time. If the enemy tried this tactic do you think that you would

be able to distinguish and report on the separate targets? Take a few minutes and

study the combined activatioft patterns in relation to the paths of these separate

targets on the grid.

c. Example 3 - Target 9

Place your job aid on the X-T plot. The activations are in which rows? Only

two pens activated for this target. This provides you, however, with enough information

to know that a target probably penetrated the grid and passed from Row I to Row III.

Also, two activated sensors give you the minimum number of sensors needed to make

your speed calculation.

Take a few minutes now and study the relationships between the X-T plot and the

path drawn on the 9-sensor grid. Review the same principles that ycu learned with

the 24-sensor grid.

87
Now superimpose target 8 which you studied previously onto target • so that

they start in the same time frame. With your pencil or pen, fill in the target

8 activations in the same manner that you did previously with target 7. This will

take you several minutes to do as before. Now look at the combined activations

carefully. If you had just now seen these activations for the first time, would

you be able to tell that two targets were involved? Lay yourjob aid on the X-T

plot. Does the job aid help you in distinguishing between these targets? Take a

few minutes and study the combined activation patterns in relation to the paths of

these separate targets as shown on the grid.

DISTANCE MEASUREMENT

As you did with the 24-sensor grid, measure the length of the path of all

three targets using your UGS ruler and then check your answers with those In

Figure G. If your answer diffars from the given one by over 200 meters, consult

the Training Monitor.

TIME MEASUREMENT

As you did with the 24-sensor grid determine the mid-point time differences

of each target using the UGS ruler and check them with the answers given in Figure G.

If your answer differs from the given one by over one minute, consult the Training

Monltor.

SPEED CALCULATION

As you did with the 24-sensor grid, determine the estimated speed of the

targets using the Speed Table and check your answers with those given in Figure G.

If your answer differs from the given answer by 50 meters/minute , consult the

Training Monitor.

PRACTICE TARGETS

Figure I presents a blank 9-sensor grid Target Log. Pull it out of your

booklet, write your name in the upper right-hand corner and place it in a handy

area. Take your outer Target Log (Figure 6), fold it in half and place it under

your papers.

Figure J presents an X-T plot of operationally collected targets for you to

practice on using the patching technique that you have just learned. Study this

X-T plot for targets. For each target that you detect, use the Target Log on

Figure I for your report and fill In the seven-step procedure. The school solution

for the Target Log Is presented in Figure K and the school solution for the X-T

plot is presented in Figure L. Do not look at the school solutions prematurely,

but attempt to detect targets and fill out the seven-step procedure on your own.

88
DO NOT LOOK AT THE SCHOOL SOLUTIONS UNTIL YOU HAVE DONE YOUR BEST IN

DETECTING AND REPORTING ON ALL THE TARGETS.

Use all three functions of your UGS ruler; sensor patch groupings, distance

measurement, and time measurement. Within the X-T plot there is one example of

helicopter activity and one example of artillery activity. See if you can spot

lihese without looking at the school solution. Artillery activity usually yields

a thin band of one or two activations across sensors located in the area.

Because the helicopter is in the area longer than the concussion of artillery

activity, it will usually produce a grouping of two or more activations across

sensors located in the area.

As you work through these operationally-collected practice targets, remember

that they are not sterile, ideal examples prepared to help you understand a

particular patching technique. They contain various sources of background noise

and the effects of malfunctioning sensors and variations in sensor detection range

due to gain setting, ground/terrain conditions and weather. To be able to do a

good UGS reporting Job, you must learn how to detect and extract target information

from X-T plots collected in the field.

Consult the Training Monitors when you feel the need. When you are finished

with your practice targets, take your work to the Training Monitor. He will

determine whether you need additional targets for more practice and/or review.
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STEP 2

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DISTANCE

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STEP 3

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FIRST AND

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STEP 5

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STEP 6

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STEP 7

TARGET

TYPE

I

Figure I. 9-sensor target log (practice targets),

90
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Flgurrf J. X-T plot for row patching using the 9-sensor grid

(practice targets).

91
/Target 10 path

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Figure K. 9-sensor target log (school solution).

92

STEP 7

TARGET

TYPE
Artillery

11 min.

Helicopter!

7 min.

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(school solution).

93
DISTRIBUTION

ARI Dittribution Litt

4 OASD (M&RAI

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1 HQDA(DAPEPBR

1 HQDA (DAMAAR)

1 HQOA (DAPE-HRE-POI

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1 HQDA (DAPE CPS)

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1 Chief. Consult Oiv (DA OTSGI, Advlphi. MD

1 Mil Ant Hum Rn, OODR&E, OAD (EftLS)

1 HQUSARAL, APOStitm, ATTN: ARA&P R

1 HQ Fit« Aimy, ATTN: AFKAOITI

3 HQ Fifth Army, Ft Sim Houiton

1 Oil. Army Stf Studies Ofc, ATTN: OAVCSA (DSP)

1 Ok Chief of Stf, Studies Ofc

1 DCSPER. ATTN: CPS/OCP

1 The Aimy Lib, Pentagon. ATTN: RSB Chief

1 The Army Uta. Pentegon. ATTN: ANRAL

1 Ofc. Aut Sect of the Army (R&D)

1 Tech Support Ofc, OXS

1 USASA, Arlington, ATTN: IAROT

1 USA Rsch Ofc, Durham, ATTN: Life Sciences Dir

3 USARIEM. Natick, ATTN: SGROUECA

1 USATTC, FtClavton, ATTN: STETC MO-A

1 USAIMA, Ft Bragg. ATTN: ATSUCTD-OM

1 USAIMA. Ft Bragg, ATTN: Merquat Lib

1 US WAG Ct. Ii Sch. Ft McClellan, ATTN: Lib

1 US WAG Ctr & Sch. Ft McClellan, ATTN: Tng Dir

1 USA Quartermaster Sch, Ft Lac, ATTN: ATSM-TE

1 Intelligence Material Dev Ofc, EWL. Ft Holabird

1 USA SE Signal Sch. Ft Gordon, ATTN: ATSO-EA

1 USAChaplainCtt&Sch. Ft Hamilton. ATTN: ATSCTE-RD

1 USATSCH, Ft Eustis, ATTN: Educ Advisor

1 USA War Collage. Carlisle Barracks. ATTN: Lib

3 WRAIR, Neuropsychiatry Oiv

1 OLI, SDA, Monterey

1 USACuncept Anal Agcy.Bethesda, ATTN: MOCAWGC

1 USAConceptAnal Agcy, Bethesda, ATTN: MOCA-MR

1 USAConceptAnal Agcy, Bethesda, ATTN: MOCA-JF

1 USA Artie Test Ctr, APO Seattle. ATTN: STEAC MO ASL

1 USA Artie Tut Ctr. APO Seattle. ATTN: AMSTEPLTS

: USA armament Cmd, Redstone Arsenal, ATTN: ATSKTEM

I USA Armament Cmd. Rock Island. ATTN: AMSAR TDC

1 FAANAFEC, Atlantic Citv, ATTN: I ihrary

1 FAA NAFEO, Atlantic City. ATTN: Hum Engr Bf

1 FAA Aeronfuncal Ctr, Oklahoma City, ATTN: AAC 44Ü

3 USA Fid Arty Sch. Ft Sill, ATTN: Library

1 USA Armor Sch, Ft Knox, ATTN:

1 USA Armor Sch, Ft Knox, ATTN:

1 USA Armor Sch. Ft Knox. ATTN:

1 USA Armor Sch. Ft Knox. ATTN

Library

ATSBOIE

ATSBOTTP

ATSBCDAO

2 HQUSACDEC, Ft Ord. ATTN. Library

1 HQUSACDEC, Ft Ord. ATTN: ATEC-EX-E-Hum Factors

2 USAEEC, Ft Benjamin Harrison, ATTN: Library

1 USAPACDC, Ft Benjamin Harrison. ATTN: ATCP-HR

1 USA Comm-Elect Sch, Ft Monmoith, ATTN: ATSN-EA

1 USAEC. FtMonmouth. ATTN-AMSEL-CT-HDP

1 IJSAEC. Ft Monmouth. ATTN: AMSEL-PA-P

1 USAEC. Ft Monmouth. ATTN: AMSEL-SI-CB

1 USAEC, Ft Monmouth. ATTN: C. Far! Dev Br

1 USA Materials Sys Anal Agcy. Aberdeen. ATTN: AMXSY-P

1 Edge.vood Arsenal. Aherdeen. ATTN: SAREA -BL--H

1 USA Ord Ctr & Sch, Ahfrdoen ATTN: ATSL-TE«/i-C

2 USA Hum Engr Lab. Aberdeen. ATTN: Library/Oir

1 USA Combat Arms Tnn Bd. Ft Benring, ATTN: Ad Supervisor

1 USA Infantry Hum Rsch Unit. Ft Banning. ATTN: Chief

1 USA Infentry Bd, Ft Banning, ATTN: STEBC-TE -T

1 USASMA. Ft Bliss. ATTN: ATSS-LRC

1 USA Air Def Sch. Ft Bliss, ATTN: ATSA CTD ME

1 USA Air Oef Sch. Ft Bliss. ATTN: Tech Lib

1 USA Ail Oaf Bd. Ft Bliss. ATTN: FILES

1 USA Air Def Bd, Ft Bliss. ATTN: STEBO-PO

1 USA Cmd & General Stf College. Ft Laavanworth. ATTN Lib

1 USA Cmd & General Stf College. Ft Leavenworth. ATTN: ATSW-SE -1

1 USA Cmd & General Stt College, Ft Leavenworth. ATTN: Ed Advisor

1 USA Combined Arms Cmhl Dev Act. Ft Leavenworth. ATTN: OapCdr

1 USA Combined Arms Cmbt Dev Act Ft Leavenworth. ATTN: CCS

1 USA Combined Arms Cmbt Dev Act. Ft Leavenworth, ATTN: ATCASA

1 USA Combined Aims Cmbt Dev Act. Ft Leavenworth. ATTN: ATCACO I

1 USA Combined Arms Cmbt Dev Act, Ft Leavenworth, ATTN: ATCACC C;

1 USAECOM. Night Vision LKII Ft BtMvoir. ATTN: AMSEL-NV-SD

3 USA Computer Sys Cmd. Ft 3elvoir. ATTN: Tech Library

1 USAMERDC. Ft Relvoir. ATTN: STSFB DO

1 USA Eng Sch, Ft Belvoii, ATTN: Library

1 USA Topographic Lab, Ft Belvoir. ATTN: ETL -TD-S

1 USA Topographie Lab Ft Belvoii. ATN: STINFO Canter

1 USA Topographic Lab. Ft Belvoir. ATTN: ETL-GSL

1 USA Intelligence Ctr & Sch. Ft Huachuca. ATTN: CTO-MS

1 USA Intelligence Ctr & Sch, Ft Huachuc«. ATTN: ATS-CTO-MS

1 USA Intalligencr Ctr & Sch. Ft Huachuca. ATTN: ATSI-TE

1 USA Intelligence Ctr & Sch. Ft Huachuca. ATTN: ATSI- TtX-GS

1 USA Intelligent Ctr & Sch. Ft Huachuca. ATTN: ATSI-CTS-OR

1 USA Intelligence Cti & Sch. Ft Huachuca. ATTN: ATSI-CTD-DT

1 USA Intelligence Ctr & Sch. Ft Huachuca. ATTN: ATSI-CTO-CS

1 USA Intelligence Ctr ft Sch, Ft Huachuca. ATTN: DAS/SRO

1 USA Intelligence Ctr & Sch, Ft Huachuca, ATTN: ATSI-TEM

1 USA Intelligence Ctr & Sch, Ft Huachuca, ATTN: Library

1 COR. HQ Ft Huachuca, ATTN: Tech Ref Div

2 COR. USA Electronic Prvg Grd. ATTN: STEEP-MT-S

1 COR. Protect MASSTER. ATTN: Tech Info Canter

1 Hq MASSTER. USATRAOOC.LNO

1 Research lns*itute. HQ MASSTER. Ft Hood

1 USA Recruiting Cmd, Ft Sherdian. ATTN: USARCPM-P

1 Senior Army Adv.. USAF AGODTAC, Elgin AF Aux Fid No. 9

1 HQ USARPAC. DCSPER. APO SF 90658. ATTN: GPPE-SE

1 Stlmson Lib, Academy of Health Sciences, Ft Sam Houston

1 Marina Corps I int., ATTN: Or jn -MCI

1 HQUSMC. Commandant. ATTN: Coda MTMT 51

1 HQUSMC, Commandant ATN, Code MPI-2n

2 'ISCG Academy, N-v London. ATTN: Ailmission

2 USCG Academy. New London. ATTN' Library

1 USCG Training Cti, NY, ATTN; CO

1 USCG Training Ctr. N Y. ATTN Educ Svc Ofc

1 USCG, Psychol Res Br, DC, ATTN: GP 1/62

1 HQ Mid-Range Br, MC Oct. Quantico, ATTN: PftS Oiv
1 USMwimCoipiUiWmOte.AMC,Atox«idrte,ATTN:AMCGS-F 1 OH ft CMI Inttalbwira MadWn«. Cmdi

1 USATRADOC, Ft Monrot, ATTN: ATBO-EO ' AIW CWOt. IhnilngOW. ATTW: '^»*»t

6 USATRADOC, Ft MomM. ATTN: ATPR-AD • MIIHwr»^fcull|Wi TUnuli, Coiiih^in

1 USATRADOC. Ft MomM. ATTN: ATTS-EA ' MWt»yAtaclw. Fiwi* Emta«». ATTW: Doc SK

1 USA Foren Cmd, Ft MeWnnon, ATTN: Ubnry '

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2 USA Aviation TtttSd, Ft Ruekar, ATTN: STEM-FO ' FrirMilntlfleOlf, A»! Hum 6* Rid» Oh», Mkihlrv

1 USA AODV for AvMon Sitaty, Ft Ruetar, ATTN: Ukrary of Datiwaa. Naw DaW

1 USA A«ev for Arialion Safety. Ft Ruekar, ATTN: SdueAMnr ' Nn Rrt Ofc Ukrary, AKA. hraal Oafcwa Forea»

1 USA Aviation Seh. Ft Ruekar, ATTN: FOOrawarO ' Mtnlnwlawn Oahnala. DOOF/KL A»d *»«laal

1 HOU8A Aviation Sy« Cmd. St Louh. ATTN: AM8AV-ZOR FiydiolflSh*a lakan, Tka Haiua, Nathartandi

2 USA AaMalltnIM Act.. Edwards AFS. ATTN: SAVTE-T

1 USA Air DafSch, Ft Bite. ATTN: ATSATEM

1 USA Air Mobility Rich ft Oav Lab. Moffatt Fid. ATTN: SAVOL-AS

1 USA Aviation Seh. Rai Tng Mgt. Ft Ruekar, ATTN: ATST-T-RTM

1 USA Aviation Seh, CO, Ft Ruekar, ATTN: ATST-O-A

1 Ha DARCOM, Alexandria. ATTN: AMXCD-TL

1 Ha DARCOM, Alexandria. ATTN: COR

1 US Military Aeadatny, Waat Point ATTN: SarMi Unit

1 US Milltarv Kjadamy, Watt Point, ATTN: Ofc of Milt Ldnhp

1 US Military Acadamv. Watt Point ATTN: MAOR

1 USAStandardiiationQp,UK,FFONY,ATTN:MAK-OC

1 Ofc of Naval Rich, Arlington, ATTN: Code 4B2

3 Ofc of Naval Rich, Arlington, ATTN: Coda 488

1 Ofc of Naval Rich, Arlington. *TTN: Coda 480

1 Ofcof Nival Rich. Arlinjron. ATTN: Coda 441

1 Navel AerowcMedRei Lab, Fameeole. ATTN: Aeeta Seh Olv

1 Nevel AmHpc Med Rai Lab, Fameeole, ATTN: Coda LSI

1 Naval AtroipcMad R« Lab.PamacoU. ATTN: Coda L8

1 Chief of NavPar». ATTN: Fart-OR

1 NAVAIRSTA Norfolk. ATTN: Safety Ctr

1 NevOeeenographle. DC, ATTN: Coda 6261, Charta ft Teeh

1 Canter of Naval Anal, ATTN: Doe Ctr

1 NavAirSyiCom. ATTN: AIR-631X

1 NavBuMad. ATTN: 713

1 NevHelieeplarSutSqua 2. FFO SF 96601

1 AFHRL (FT) William AFB

1 AFHRL (TTI Lewry AFB

1 AFHRL (AS1WPAFB, OH

2 AFHRL IOOJZ) Brooks AF6

1 AFHRL (OOJN) Lsckland AFB

1 HQUSAF (INYSO)

1 HQUSAF (OPXXAI

1 AFVTQ (RD) Randolph AF6

3 AMRL(HE)WPAFB.OH

2 AF Inat of Teeh, WPAFB, OH, ATTN: ENE/8L

1 ATC (XPTD) Randolph AFB

) USAF AaroMed Lib, Brooks AFB (SUL-41, ATTN: DOC SEC

1 AFOSR (NLI, Arlington

1 AF Log Cmd, MeCMien AFB, ATTN: ALC/DPCRB

1 Air Force Academy, CO, ATTN: Dapt of Bel Sen

6 NevPers ft Oev Ctr, San Diego

2 Navy Med Neurepsychietrlc Rsch Unit Sen Diego

1 Nav Electronic Lab, San D»go, ATTN: Res Lab

1 Nav TmgCen. Sen Diego. ATTN: Code 800»-Llb

1 N^vPostGraSch. Monterey, ATTN: Code 66Aa

V NevPoetGreSch, Monterey, ATTN: Cede 212«

1 NevTmgEquipCtr, Orlendo, ATTN: Tech Lib

1 USOeptofLebor. DC, ATTN: Manpower Admin

1 USOeptofJintioe. DC. ATTN. Drug Enforce Admin

1 Nat Bur of Standards. DC, ATTN: Computer Info Sectkn

1 Nat Clearing Home fw MH-lnfo, Rockvilla

1 Denver Federal Ctr, Lekewood, ATTN: BLM

12 DctenM DoounwntMton OwMf

4 Dir Psych, Army Hq, Ri»eall Of«, Cenbere

1 Scientific Advw, Mil Bd, Army Hq, R««iell Ofcs, Cenberre

1 Mil and Air Attache, Austrien Embes^

1 Centre de Recherehe Dei Fecteun, Humeine de la Defense

Netionele, Brussels

2 Canadian Joint Staff Washinglon

1 C/Air Staff, Royal CanedienAF, ATTN: Per» SldAnelBr

3 Chief, CenedienDef Rsch Steff, ATTN: C/CROSWO

4 British DefSteff, British Embesey, Weshingion