Data Collection and Analysis for Personnel Detection at a 

Border Crossing

by Thyagaraju Damarla, Tom Walker, and Ronald Sartain

ARL-TR-5426 January 2011

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Army Research Laboratory

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ARL-TR-5426 January 2011

Data Collection and Analysis for Personnel Detection at a 

Border Crossing

Thyagaraju Damarla, Tom Walker, and Ronald Sartain

Sensors and Electron Devices Directorate, ARL

Approved for public release; distribution unlimited. 
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Data Collection and Analysis for Personnel Detection at a Border Crossing

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Thyagaraju Damarla, Tom Walker, and Ronald Sartain

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14. ABSTRACT

There is considerable interest in detecting people crossing the border with fewer false alarms and high confidence. This 

capability requires understanding the phenomenology of various sensor modalities and developing algorithms based on the 

phenomenology. In an effort to develop this capability, U.S. Army Research Laboratory scientists went to the southwest 

border to collect data using acoustic, seismic, passive infrared (IR), profiling, electric field, magnetic field, radar, sonar, 

visible, and IR imaging sensors. In this report, we discuss the data collection effort and resultant data, phenomenology of 

various sensor modalities, and robust detection algorithms. In the future, the acoustic sensor data will be processed to 

determine the characteristic features of human voice (formants, etc.), the seismic data will be processed using the ground 

transfer function to determine the cadence of the person walking (as opposed to an animal), and the radar and ultrasonic data

will be processed to determine the Doppler frequency resulting from various limb movements. 

15. SUBJECT TERMS

Border crossing, data collection, signature analysis, personnel detection

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iii

Contents

List of Figures v

List of Tables vi

1. Introduction 1

2. Sensor Suite 1

2.1 Acoustic...........................................................................................................................1

2.2 Seismic ............................................................................................................................2

2.3 Passive Infrared (PIR).....................................................................................................3

2.4 Magnetic (B-field)...........................................................................................................3

2.5 Electrostatic .....................................................................................................................4

2.6 Modified Toy Radar........................................................................................................4

2.7 ARL Compact Radar.......................................................................................................5

2.8 Ultrasonic ........................................................................................................................5

2.9 Profiling...........................................................................................................................6

2.10 Visible Imaging ...............................................................................................................7

2.11 Visible and Infrared Imaging ..........................................................................................7

2.12 Meteorological Station ....................................................................................................8

3. Data Collection 9

3.1 Data Collection Objectives............................................................................................10

3.2 Participating Teams.......................................................................................................10

3.3 Sensor Placement ..........................................................................................................11

3.4 Data Collection Equipment ...........................................................................................12

3.5 Data Collection Scenarios.............................................................................................13

4. Data Analysis and Algorithm Development 22

4.1 Acoustic Sensor Data Analysis for Personnel Detection ..............................................22

4.1.1 Detection of Formants.......................................................................................22

4.1.2 Detection of People Using Energy in Several Bands........................................23
iv

4.2 Seismic Data Analysis...................................................................................................25

5. Data Collection and Algorithm Results 27

5.1 Voice Analysis and Algorithm Results.........................................................................28

5.2 Seismic Data Analysis and Algorithm Results .............................................................28

5.3 Ultrasonic Data Analysis for Counting Number of Targets..........................................29

6. Conclusion 30

7. References 31

List of Symbols, Abbreviations, and Acronyms 32

Distribution List 33
v

List of Figures

Figure 1. Single microphone and an array (tetrahedral) of microphones with windscreens. ........2

Figure 2. Tri-axis seismic sensor. ...................................................................................................3

Figure 3. Picture of PIR sensor.......................................................................................................3

Figure 4. Picture of fluxgate magnetometer. ..................................................................................4

Figure 5. Picture of E-field sensor. .................................................................................................4

Figure 6. Mattel Hot Wheels radar gun. .........................................................................................5

Figure 7. ARL compact radar. ........................................................................................................5

Figure 8. Ultrasonic sensor. ............................................................................................................6

Figure 9. (a) Two-pole profiling sensor, (b) typical human silhouette, and (c) Brimrose 

profiling sensor. .........................................................................................................................6

Figure 10. Picture of daylight video camera set up along trail and close up. ................................7

Figure 11. Picture of emplaced TRSS dual imager and ARL data recorder, plus a close-up 

shot of imager. ...........................................................................................................................8

Figure 12. Picture of Vantage Pro 2 weather station. .....................................................................8

Figure 13. (a) Photo of a wash, (b) a trail, and (c) a choke point. ..................................................9

Figure 14. Typical sensor layout...................................................................................................11

Figure 15. Typical ARL sensor suite. ...........................................................................................12

Figure 16. Run #2. .........................................................................................................................13

Figure 17. (a) Run #3: one man walking with a 40 lb backpack, (b) Run #4: one man running, 

(c) Run #5: one woman walking, (d) Run #6: one woman walking with 20-30 lb 

backpack, (e) Run #7: one man and one woman walking, and (f) Run #8: two women 

walking.....................................................................................................................................14

Figure 18. (a) Run #9: two men running, (b) Run #10: two men running with backpacks, (c) 

Run #11: three men walking, (d) Run #12: two men and one woman walking, (e) Run 

#13: three men running, and (f) Run #14: two men and one woman running.........................15

Figure 19. (a) Run #15: three men running with backpacks, (b) Run #16: four men and three 

women walking, (c) Run #17: one dog with one woman walking, (d) Run #18: one 

horse with handler walking, (e) Run #19: one horse and one mule with handlers walking, 

and (f) Run #20: one horse, one mule, and one donkey with three handlers walking.............16

Figure 20. (a) Run #21: one horse, one mule, and one donkey with three handlers walking, 

(b) Run #22: one horse with payload with handler walking, (c) Run #23: one horse and

one mule with payloads with two handlers walking, (d) Run #24: one horse, one mule, 

and one donkey with payloads with three handlers walking, (e) Run #25: one ATV going 

5 mph, and (f) Run #26: another ATV going 10 mph. ............................................................17

Figure 21. Sample voice data........................................................................................................23
vi

Figure 22. (a) Portion of voice signal in figure 16 and (b) its spectrum.......................................23

Figure 23. Acoustic data processing. ............................................................................................25

Figure 24. (a) Hoof signature (b) hoof signature after noise removal. .........................................26

Figure 25. Micro-Doppler signature of a person walking for the active ultrasonic sensor. .........26

Figure 26. Flow chart for ultrasonic data processing....................................................................27

Figure 27. (a) Acoustic data and (b) detections. ...........................................................................28

Figure 28. (a) Seismic data and (b) detections..............................................................................29

Figure 29. Results of ultrasonic data processing. .........................................................................29

List of Tables

Table 1. Apportionment of CompactRIO channels and sampling rate.........................................12

Table 2. Sensor locations for test site 2, Tuesday, December 8, 2009. ........................................18

Table 3. Test scenarios and data collection start and end times for test site 2, Tuesday, 

December 8, 2009. ...................................................................................................................18

Table 4. Sensor locations for test site 2, Wednesday, December 9, 2009. ...................................19

Table 5. Test scenarios and data collection start and end times for test site 2, Wednesday, 

December 9, 2009. ...................................................................................................................19

Table 6. Sensor locations for test site 3, Thursday, December 10, 2009......................................21

Table 7. Test Scenarios and data collection start and end times for test site 3, Thursday, 

December 10, 2009. .................................................................................................................21
1

1. Introduction

Detection of people is one of the important tasks in Intelligence, Surveillance, and 

Reconnaissance (ISR) missions. For example, for perimeter protection, one would like to detect 

intruders both during day and at night, so security can be alerted for appropriate action. In urban 

operations, one would like to ensure that once a building is evacuated, no one re-enters the 

building―this implies that sensors need to detect people entering the building. The Department 

of Homeland Security has a requirement to detect illegal immigrants crossing the border. There 

are numerous other applications where personnel detection is important. 

Detecting people is a challenging problem. For example, acoustic sensors may analyze sound to

determine if human voices are present; however, if people are not talking, the acoustic sensors 

will not be able to detect people based on voice analysis alone. Other sensors may be combined 

with acoustic sensors such as seismic, non-imaging passive infrared (PIR), sonar, ultrasound, 

radar, magnetic, and electric field sensors. Multiple sensing modalities are beneficial since no 

single sensor can detect the presence of humans in every situation and circumstance. There is an 

emphasis on non-imaging sensors because they require lower power, last longer, and generally 

are less expensive than imaging sensors. Imaging sensors require higher power and frequent 

replacement of batteries—hence, there is a higher chance of compromising the mission. 

Traditionally, personnel detection research concentrated on footstep analysis using seismic 

sensors. When a person walks, the person’s foot impacts the ground causing vibrations, which 

are detected by the seismic sensors. In general, a person walks with a rhythm and has a cadence 

frequency of approximately 1.5 Hz. One objective of the research was to detect and estimate this 

cadence frequency to distinguish between a person and other targets, such as animals. However, 

when there are several people walking in a group, it is difficult to estimate the cadence of an 

individual person and, in fact, it is quite difficult to distinguish between people and animals 

walking. In order to aid in differentiating people from animals, a multimodal sensor suite was 

used. Each sensor has unique characteristics that complement other sensing modalities in 

conducting ISR; section 2 provides a detailed description of the sensors used in this experiment.

2. Sensor Suite

2.1 Acoustic

The acoustic sensors used for this experiment were piezoelectric microphones. Acoustic sensors 

are low-cost sensors used to detect sound energy generated by people, animals, or machines. 

Unlike other sensors, these sensors are omnidirectional and do not require line of sight to a 

target. If more than one microphone is employed, these acoustic sensors can provide a line of 
2

bearing to the source of the sound. Figure 1 shows the tetrahedral array of microphones used in 

collecting the data. The microphones used for this experiment were three Knowles microphones

(part number [P/N] BL-1994) with a frequency response of 20 Hz to 20 kHz placed at the base of 

the tetrahedron array, and a Bruel & Kjaer (B&K) 1⁄2-in-diameter microphone (P/N 4191) with a 

frequency response up to 50 kHz placed on the z-axis of the tetrahedron. Each B&K microphone 

also required a preamplifier (P/N 2669) and power supply (P/N 5939L). The data were collected 

at 10 K samples per second, providing an effective bandwidth of 5 kHz, which allows capture of

both voice and (audible) footstep signatures with sufficient fidelity to detect personnel. 

 

Figure 1. Single microphone and an array (tetrahedral) of 

microphones with windscreens.

2.2 Seismic

Seismic sensors are similar in operation to acoustic sensors and are used to capture vibrations in 

the ground caused by footfalls from people or animals walking, or by vehicles traveling in the 

vicinity, among other sources. The geophone seismic sensors used during our experiment were 

manufactured by Geospace (P/N GS-3C) and they measure vibrations in all three axes. They 

were oriented towards magnetic north and were buried about 15 cm deep to increase coupling 

with the seismic signals (figure 2 shows a photo of the sensor). The sensitivity and frequency 

response of seismic sensors generally vary depending on design and manufacturer and are 

typically purchased taking mission requirements into account.
3

Figure 2. Tri-axis seismic sensor.

2.3 Passive Infrared (PIR)

These non-imaging devices are inexpensive sensors that detect the nearby presence of a warm 

body, e.g., a human. The field of view (FOV) of the sensor is determined by the lens in front of 

the actual sensing element. Two different PIRs were employed, one with an FOV of 15° and one 

with 60°. In addition, the PIR sensors have plates that are charged differentially; they are charged

positively or negatively depending on the direction the person is traveling. Hence, it is possible 

to determine the direction of motion of a target. The sensors reliably detect targets, but do not 

distinguish whether the target is human, animal or a warm inanimate object. It only records the 

thermal signature generated by the target. Figure 3 shows a photo of the PIR sensor that was 

fabricated at the U.S. Army Research Laboratory (ARL) and used for the experiment.

Figure 3. Picture of PIR sensor.

2.4 Magnetic (B-field)

These sensors can be used to detect ferromagnetic materials carried by people, e.g., keys, 

firearms, and knives. For the experiment we used Model 1540 three-axis fluxgate magnetometers

from Applied Physics Systems. They have a sensitivity of 30 μV/nT and are used to detect low 

frequency components up to 5 Hz. The data from the fluxgate magnetometer were collected at 10 

samples per second. Figure 4 shows a photo of the emplaced fluxgate magnetometer.
4

Figure 4. Picture of fluxgate magnetometer.

2.5 Electrostatic

The sensor used for the experiment was an ARL-built E-field sensor. The sensor consists of two 

parallel plates with the lower plate grounded with a spike driven into the ground. The plates are 

charged and when a person having a static E-field (due to rubbing of clothes, etc.) walks nearby

the plate potential is affected. The sensor output was recorded at 10 K samples per second. 

Figure 5 shows a photo of an emplaced E-field sensor.

Figure 5. Picture of E-field sensor.

2.6 Modified Toy Radar

This is a stock Mattel Hot Wheels Radar Gun with a transmit frequency of 10.525 GHz that 

displays the velocity of the target in km/hr (or mi/hr) that was modified to provide the Doppler 

frequency of the target (fd=2v/λ). The toy radar used during the data collection is shown in 

figure 6.
5

 

Figure 6. Mattel Hot Wheels radar gun.

2.7 ARL Compact Radar

The ARL compact radar is a Doppler-based, linear frequency modulated, low-power radar 

system with an electrically scanned patch-array antenna that allows azimuth and elevation 

control of the radar beam. The radar system provides sufficient resolution to detect movement of 

individual arms and legs on humans or animals walking or running―the limb movement 

velocity information is used to differentiate between humans and animals. Figure 7 shows a 

photo of the ARL compact radar.

Figure 7. ARL compact radar.

2.8 Ultrasonic

This sensor transmits an acoustic tone at 40 kHz and receives the signals scattered by the target, 

both active and passive ultrasonic measurements were conducted. For the passive method, a 

narrowband ultrasonic microphone receiver (UR) was used to measure ultrasonic emissions from 

footsteps. This UR had a resonance frequency of 25.5 kHz and a bandwidth of 1 kHz at –6 dB, 

the directivity at –6 dB was 60°. For the active method a 40-kHz ultrasonic continuous-wave 

(CW) Doppler sensor (UDS) was employed. The sensor was assembled from two 40-kHz 

ultrasonic transducers (MATSU/PAN EFR-RCB40K 54). These transducers have a typical 

bandwidth at –6 dB of 2 kHz and a directivity pattern at –6 dB of 55°. One of the transducers, 

acting as a transmitter, emitted an ultrasonic wave, while the other acted as a receiver. Signals 

from the UR and the UDS were simultaneously recorded using a 12-channel, 24-bit at 1 V data 

acquisition board (DAQ) (Echo AudioFire12) and a laptop computer with Loud Technologies 
6

software (Tracktion 2). The DAQ, having a sampling rate of 96 kHz, acquired signals from the 

sensors. These signals were stored on the laptop computer in a .wav format. The UR and UDS 

were attached to a 1.2-m tall tripod. In the test configurations, the beam pattern of the UR and 

UDS were oriented along the walking track. Figure 8 shows the ultrasonic sensor used.

Figure 8. Ultrasonic sensor.

2.9 Profiling

This sensor records the silhouette of the target rather than the image of the target. One 

implementation of the profiling sensor is shown in figure 9a with two vertical poles consisting of 

IR transmitters mounted on one pole and the receivers mounted on the other. A sample human 

silhouette is shown in figure 9b. A more covert type of profile sensor built by Brimrose that can 

be emplaced some distance from the trail is shown in figure 9c.

 (a) (b)

(c)

Figure 9. (a) Two-pole profiling sensor, (b) typical human silhouette, and (c) Brimrose 

profiling sensor.
7

2.10 Visible Imaging

Each ARL sensor suite also included a Kodak Zi8 video color camera to record ground truth for 

each data run; the video/audio data were stored on 32-GB SanDisk memory cards. The video 

camera has a resolution of 1920x1080 pixels at 30 frames per second using a 6.3-mm f/2.8 fixed 

focus lens. The camera also records audio. Figure 10 shows a picture of the visible video camera 

set up along the trail and a close-up view.

 

Figure 10. Picture of daylight video camera set up along 

trail and close up.

2.11 Visible and Infrared Imaging

These imagers are commonly used in the ISR realm based on the adages that a “picture is worth 

thousand words” or “seeing is believing.” The dual imager used for this experiment came from a 

Tactical Remote Sensor System (TRSS) and it contains both a visible and IR camera. The visible 

color video camera has a resolution of 768x494 active pixels, 1⁄4-in charge-coupled device 

(CCD), and has approximately 450 TV lines. The FOV is approximately 25°. The IR camera 

used is an uncooled long-wave (8–12 micron) microbolometer with FOV of 25° and a focal 

plane array resolution of 320x240. The data were collected at 30 frames per second and recorded 

using an ARL integrated four-channel digital video recorder consisting of four Datavideo 

120-Mbyte hard drives and associated control circuitry. Figure 11 shows the emplaced TRSS 

dual imager and ARL recording equipment used for the experiment, along with a close up of the 

TRSS imager.
8

 

Figure 11. Picture of emplaced TRSS dual imager and ARL data recorder, plus a close-up shot of imager.

2.12 Meteorological Station

The air temperature, wind speed and direction, dew point, barometric pressure, and humidity 

along with a global positioning system (GPS) time stamp were recorded once every minute using 

a Vantage Pro 2 Weather Station from Davis Instruments for the duration of the experiment at 

each test site. At the end of each day, the meteorological data were retrieved from the Vantage 

Pro Remote Controller and downloaded onto a laptop and stored in a text file. Figure 12 shows a 

picture of an emplaced Vantage Pro 2 Weather Station.

Figure 12. Picture of Vantage Pro 2 weather station.
9

3. Data Collection

In order to collect data in a real-world situation, the ARL team went to the U.S. southwest 

border. Two trips were made: the first visit was to consult with the Department of Homeland 

Security, which faces the illegal immigration problem on a daily basis, and determine the actual 

test sites where the experiments would be conducted. After discussions with the border security 

division and watching surveillance videos, it was determined the experiment would occur at 

three different types of locations, namely a wash, a trail, and a choke point. Figures 13a–c show

pictures of a wash, trail, and choke point (respectively) that are typically found along the 

southwest border.

 (a) (b)

(c)

Figure 13. (a) Photo of a wash, (b) a trail, and (c) a choke point.
10

3.1 Data Collection Objectives

Several objectives were envisioned for the field test experiment on the southwest border, these 

included the collection of personnel, animal, and vehicle signatures using an array of sensors for 

the purpose of understanding target phenomenology and providing signatures for accomplishing 

the following:

• Developing multi-sensor fusion algorithms

• Developing detection and classification algorithms for single- and multi-sensor systems

• Supporting target counting and direction of travel algorithms

• Supporting algorithm development to determine if payloads are being carried by humans or 

animals

3.2 Participating Teams

The following lists the teams that participated in the experiment and what tasks they handled:

• ARL

– Networked Sensing and Fusion Branch

• Acoustics, seismic, PIR, E and B-field, and digital video (truth data)

– Radar Branch

• Compact radar

• Armament Research and Development Center (ARDEC)

– Test support

• University of Mississippi

– Acoustic, seismic, active and passive ultrasonic, and toy radar

• University of Memphis

– Profiling sensors (parallel optical tripwire and microbolometer)

• Brimrose Co.

– Thermopile profiling sensor

• Night Vision and Electronic Sensors Directorate (NVESD)

– Pyroelectric profiling sensor
11

3.3 Sensor Placement

The experiment used four ARL sensor suites placed approximately 50 m apart along the 

prescribed travel path. Each sensor suite consisted of acoustic, seismic, PIR, magnetic, E-field,

and toy radar sensors. Figure 14 shows a typical deployment of the entire sensor ensemble at a 

test site and figure 15 shows a photo of an individual ARL sensor suite. The ARL compact radar

was placed at a location in-line with the trail that allowed good coverage of the subject 

movement along the entire travel path, while the TRSS visible and infrared imager was placed at 

a vantage point off to one side of the trail. 

Figure 14. Typical sensor layout.
12

Figure 15. Typical ARL sensor suite.

3.4 Data Collection Equipment

Each ARL sensor suit used a 12-channel, 16-bit National Instruments CompactRIO control and 

data acquisition system for capturing the various sensor signals. Major advantages of this 

recording system include low power consumption; small size and operation from 12-V

automotive batteries for portability; low noise operation (no cooling fan) that produced very little 

acoustic and electrical noise that might otherwise corrupt sensor signals; and the inclusion of a 

GPS receiver that accurately time stamped all the recorded data. The partitioning of the 12 

CompactRIO channels and the sampling rate for the sensors are shown in table 1. Each ARL 

sensor suite also included a Kodak Zi8 daylight video camera to record ground truth that was 

stored on 32-GB SanDisk memory cards.

Table 1. Apportionment of CompactRIO channels and sampling rate.

Sensor Type No. Channels Sampling Rate Resolution

Acoustic 4 – Analog 10000 16 bit

Seismic 3 – Analog 10000 16 bit

PIR 1 – Analog 10000 16 bit

E-field 1 – Analog 10000 16 bit

Toy Radar 1 – Analog 10000 16 bit

Magnetic 1 – Digital (3 axis) 10 24 bit

Acoustic 

Sensors

PIR 

Sensor

E- Field 

Sensor

Compact 

RIO

Digital 

Video

Buried 

Seismic 

Sensor
13

3.5 Data Collection Scenarios

The following data collection scenarios were used for the experiment. For the cases where a 

small animal was used, the handler walked behind the animal. For the large animals, the handlers 

walked in front.

Run #1: Pre-test checkout of equipment to make sure sensors and recording equipment were 

operating properly

• Acoustic Sensors: We applied a 1-kHz signal to microphones using an acoustic source 

(B&K P/N 4230).

• Seismic Sensors (oriented magnetic north): Dropped a 6.5-kg steel weight from 1-m height 

at 1, 5, and 10 m due north from sensor.

• PIR Sensors: One person walked through FOV of sensor at 1, 5, and 10 m from sensor.

• Magnetic Field Sensors: A person walked past the sensor with a 1-m ferrous metal pipe

(5 cm diameter) at 1, 5, and 10 m from sensor.

• E-field Sensors: We used a piezoelectric sparker (from a butane lighter) at 1, 5, and 10 m

from sensor.

• ARL compact Radar: One person walked from 1 to 30 m away from the radar at constant 

velocity and then back towards the radar.

Run #2: One man walking (figure 16)

• We noted the GPS time at the beginning and end of the data run.

• We collected background ambient data for 2 min.

• A person walked from beginning to end of designated path, waited 10 s, then returned

along same path and stood still for another 10 s when finished.

• While walking, the person was instructed to talk, sing, hum, etc.

Figure 16. Run #2.
14

Figures 17 through 20 illustrate Runs #3 though #26. Runs #3 though #26 followed the same 

data collection procedures as Run #2; they only varied in the participants and activities 

performed.

 (a) (b)

 (c) (d)

 (e) (f)

Figure 17. (a) Run #3: one man walking with a 40 lb backpack, (b) Run #4: one man running, (c) Run #5: one 

woman walking, (d) Run #6: one woman walking with 20-30 lb backpack, (e) Run #7: one man and 

one woman walking, and (f) Run #8: two women walking.
15

 (a) (b)

 (c) (d)

 (e) (f)

Figure 18. (a) Run #9: two men running, (b) Run #10: two men running with backpacks, (c) Run #11: three 

men walking, (d) Run #12: two men and one woman walking, (e) Run #13: three men running, and 

(f) Run #14: two men and one woman running.
16

 (a) (b)

 (a) (b)

 (a) (b)

Figure 19. (a) Run #15: three men running with backpacks, (b) Run #16: four men and three women walking, 

(c) Run #17: one dog with one woman walking, (d) Run #18: one horse with handler walking, (e) Run 

#19: one horse and one mule with handlers walking, and (f) Run #20: one horse, one mule, and one 

donkey with three handlers walking.
17

 (a) (b)

 (c) (d)

 (e) (f)

Figure 20. (a) Run #21: one horse, one mule, and one donkey with three handlers walking, (b) Run #22: one horse 

with payload with handler walking, (c) Run #23: one horse and one mule with payloads with two 

handlers walking, (d) Run #24: one horse, one mule, and one donkey with payloads with three handlers 

walking, (e) Run #25: one ATV going 5 mph, and (f) Run #26: another ATV going 10 mph.
18

Tables 2–7 provide information on sensor locations and test schedules. All times listed are local 

mountain standard time (MST); individual test subjects abbreviated as M1 (man 1), M2 (man 2), 

W1 (woman 1), etc.

Table 2. Sensor locations for test site 2, Tuesday, December 8, 2009.

Sensor Type

Elevation 

(ft)

Waypoint

Name

Site 

Photo

(Y or N) Notes

MET Station 4423 Wpt 002 y

Compact RIO #2A 4417 Wpt 003 y Site also contained radar gun and digital 

video camera

Compact RIO #3A 4416 Wpt 004 y Site also contained radar gun and digital 

video camera

Compact RIO #5A 4440 Wpt 005 y Site also contained radar gun

TRSS 4459 Wpt 006 y

ARL Compact Radar 4443 Wpt 009 y Radar mounted on top of vehicle

Brimrose PFx and 

Memphis FLIR

4447 Wpt 008 y

Memphis PFx 4437 Wpt 007 y

Table 3. Test scenarios and data collection start and end times for test site 2, Tuesday, December 8, 2009.

Run 

No.

Test Scenario Start time Stop time Notes

2 One man walking 11:52:00 AM M1

3 One man walking with 40-lb backpack 12:00:00 Noon 12:05:00 PM M2

4 One man running 12:07:00 PM 12:10:00 PM M2

5 One woman walking 12:11:00 PM 12:18:27 PM W1

6 One woman walking with 20-lb 

backpack

12:19:49 PM 12:18:27 PM W2

7 One man and one woman walking 12:26:15 PM M3, W3

8 Two women walking W1, W2

9 Two men running 12:47:24 PM 12:52:21 PM M4, M2

10 Two men running with 40-lb backpacks 12:54:00 PM M2, M3

11 Three men walking 1:00:10 PM 1:06:38 PM M3, M4, M11

12 Two men and one woman walking 1:08:01 PM 1:14:54 PM M4 with large iron pipe, 

M11, W1

13 Three men running 1:16:04 PM 1:20:35 PM M2, M5, M3

14 Two men and one woman running 1:21:43 PM 1:27:20 PM M13, M2, W3

15 Three men running with 40 lb backpacks 1:43:10 PM 1:48:48 PM M2, M1, M4, Compact 

RIO 2A stopped working

16 Four men, three women, and one dog 

walking

1:54:48 PM 2:02:04 PM M1, M2, M4, M6, W1, W2 

with dog, W3. Stopped 

test for lunch break.

17 One dog with one woman walking 2:50:00 PM 2:55:01 PM W2

22a Man leading horse with payload 2:56:00 PM 3:02:04 PM M7

23a Two men leading horse and mule with 

payloads

3:04:11 PM 3:10:08 PM M7, M8

24a Three men leading horse, mule, and 

donkey with payloads

3:11:00 PM 3:17:00 PM M7, M8, M9
19

Table 3. Test scenarios and data collection start and end times for test site 2, Tuesday, December 8, 2009

(continued).

Run 

No.

Test Scenario Start time Stop time Notes

21a Three men walking beside horse, mule, 

and donkey with payloads

3:17:55 PM 3:24:24 PM M7, M8, M9

18 Man leading horse 3:36:50 PM 3:42:26 PM M7

19 Two men leading horse and mule 3:43:52 PM 3:49:58 PM M7, M8

20 Three men leading horse, mule, and 

donkey

3:50:18 PM 3:55 PM M7, M8, M9

22 Man leading horse with saddle pack, 

100 lb load

4:00:00 PM 4:04:51 PM M7

23 Two men leading horse and mule with 

saddle packs, 100 lb loads

4:05:23 PM 4:11:00 PM M7, M8

24 Three men leading horse, mule, and 

donkey with saddle packs, 100 lb loads

4:11:19 PM 4:17:10 PM M7, M8, M9

Table 4. Sensor locations for test site 2, Wednesday, December 9, 2009.

Sensor Type Elevation 

(ft)

Waypoint

Name

Site 

Photo 

(Y or N)

Notes

MET station 4423 Wpt 002 y Same location as day 2

Compact RIO #2A 4447 Wpt 010 y Site also contained radar gun and 

digital video camera

Compact RIO #3A 4445 Wpt 011 y Site also contained radar gun

Compact RIO #5A 4439 Wpt 012 y Site also contained radar gun

TRSS 4459 Wpt 006 y Same location as day 2

ARL compact radar 4437 Wpt 013 y Radar pointed due west

Brimrose PFx and Memphis FLIR 4438 Wpt 017 y Site also contained FLIR and two

linear array pyro PIR’s, all pointed 

340°

Memphis PFx 4437 Wpt 007 y Same location as day 2

Digital movie camera 4447 Wpt 014 y Two digital movie cameras used 

during the test

Mississippi ultrasonic and radar 4413 Wpt 015 y

Black body radiator 4438 Wpt 018 y Used to calibrate FLIR sensors

Table 5. Test scenarios and data collection start and end times for test site 2, Wednesday, December 9, 2009.

Run 

No.

Test Scenario Start time Stop time Notes

25 1 ATV traveling ~5 mph 12:15:45 PM 12:20:13 PM Yamaha Grizzly 750 FI

26 1 ATV traveling ~10 mph 12:24:17 PM 12:27:44 PM Yamaha Grizzly 750 FI

27 1 ATV traveling ~5 mph with man 

running behind

12:30:15 PM 12:34:38 PM Yamaha Grizzly 750 FI and M2 

running

27a 1 ATV traveling ~5 mph with man 

running behind

12:38:17 PM 12:42:27 PM Yamaha Grizzly 750 FI and M2 

running

28 2 ATV’s traveling ~10 mph 12:46:15 PM 12:49:52 PM 2 Yamaha Grizzly 750 FI

29 3 ATV’s traveling ~10 mph 12:52:55 PM 12:57:54 PM 2 Yamaha Grizzly 750 FI and 1 

Yamaha Kodiak Ultramatic 400 

(all single-cylinder engines)
20

Table 5. Test scenarios and data collection start and end times for test site 2, Wednesday, December 9, 2009

(continued).

Run 

No.

Test Scenario Start time Stop time Notes

2 One man walking 1:02:05 PM 1:08:29 PM M1, helicopter noise

3 One man walking with 40-lb backpack 1:11:10 PM 1:17:18 PM M2

4 One man running 1:19:50 PM 1:24:17 PM M2

5 One woman walking 1:27:08 PM 1:34:52 PM W1 (unsure of trail path), ATV 

started during ambient data 

period, added another minute, 

ATV and bird noise during run.

6 One woman walking with 20-lb 

backpack

1:37:31 PM 1:43:59 PM W2

7 One man and one woman walking 1:45:47 PM 1:52:28 PM W3, M3

8 Two women walking 1:53:54 PM 2:00:49 PM W1, W2

9 Two men running 2:02:52 PM 2:07:53 PM M4, M2, noise from Memphis 

gazebo as it flapped in strong 

wind 

10 Two men running with 40-lb 

backpacks

2:10:51 PM 2:16:11 PM M12, M2, helicopter noise 

during run

11 Three men walking 2:18:25 PM 2:25:04 PM M3, M4, M11

12 Two men and one woman walking 2:26:24 PM 2:32:50 PM M4 with large iron pipe, M11, 

W1

13 Three men running 2:42:41 PM 2:47:26 PM M12, M2, M5, ATV noise in 

background

14 Skipped this test run

15 Three men running with 40-lb 

backpacks

2:51:00 PM 2:56:13 PM M4, M13, M2. Dog barked at 

end of run.

16 Four men, three women, and one dog 

walking

2:57:25 PM 3:04:29 PM M1, M2, M4, M6, W1, W2 with 

dog, W3

17 One dog with one woman walking 3:05:59 PM 3:12:01 PM W2

18 Man leading horse 3:17:09 PM 3:23:18 PM M7, Memphis gazebo making 

noise flapping in wind

19 Two men leading horse and mule 3:24:16 PM 3:30:19 PM M7, M8, ATV horn beeped 

3:26:10PM.

20 Three men leading horse, mule, and 

donkey

3:34:40 PM 3:41:08 PM M7, M8, M9

21 Skipped this test run

22 Man leading horse with saddle pack, 

100 lb load

3:51:35 PM 3:57:19 PM M7

23 Two men leading horse and mule with 

saddle packs, 100-lb loads

3:59:00 PM 4:04:56 PM M7, M8, cell phone rang 

3:59:10, Memphis gazebo 

flapping noise 4:00:05, walkie 

talkie call ringer 4:02:40, jet 

passed overhead 4:03:31.

24 Three men leading horse, mule, and 

donkey with saddle packs, 100-lb 

loads

4:07:14 PM 4:12:56 PM M7, M8, M9

22a Man leading horse with payload 4:37:00 PM M7

23a Two men leading horse and mule with 

payloads

4:38:00 PM M7, M8

24a Three men leading horse, mule, and 

donkey with payloads

4:40:00 PM M7, M8, M9

22b Man leading donkey with payload 4:42:00 PM M9
21

Table 6. Sensor locations for test site 3, Thursday, December 10, 2009.

Sensor Type Elevation 

(ft)

Waypoint

name

Site Photo 

(Y or N)

Notes

MET station 4477 Wpt 021 y

Compact RIO #2A 4471 Wpt 023 y

Compact RIO #3A 4462 Wpt 025 y

Compact RIO #5A 4439 Wpt 026 y

TRSS Did not set up TRSS on day 4

ARL compact radar 4440 Wpt 028 n Raju’s camera battery died

Brimrose PFx and Memphis FLIR 4413 Wpt 029 n

Memphis PFx 4477 Wpt 022 n

Mississippi ultrasonic and radar 4455 Wpt 024 y

Trail start n GPS receiver battery died

Trail end 4433 Wpt 027 y

Table 7. Test scenarios and data collection start and end times for test site 3, Thursday, December 10, 2009.

Run 

No.

Test Scenario Start time Stop time Notes

0 Two border patrol agents 12:01:59 PM 12:08:32 PM 1 agent with M16 rifle, both 

with sidearms and small 

backpacks

18 Man leading horse 12:12:58 PM 12:19:23 PM M7, small airplane flew over 

12:17:00

19 Two men leading horse and mule 12:22:28 PM 12:28:46 PM M7, M8, red tail hawk called 

from above

20 Three men leading horse, mule, and 

donkey

12:31:45 PM 12:38:12 PM M7, M8, M9

21 Skipped this test run

22 Man leading horse with saddle pack, 100-

lb load

12:48:32 PM 12:54:35 PM M7, heard ATV and walkie 

talkie call ringer 

23 Two men leading horse and mule with 

saddle packs, 100 lb loads

1:00:08 PM 1:06:30 PM M7, M8

24 Three men leading horse, mule, and 

donkey with saddle packs, 100-lb loads

1:08:59 PM 1:15:20 PM M7, M8, M9

22a Man leading horse with payload 1:22:59 PM 1:28:59 PM M7, crow called in 

background

23a Two men leading horse and mule with 

payloads

1:30:52 PM 1:37:33 PM M7, M8

24a Three men leading horse, mule, and 

donkey with payloads

1:41:40 PM 1:47:45 PM M7, M8, M9

2 One man walking 1:52:00 PM 1:57:08 PM M10

3 One man walking with 40-lb backpack 2:00:06 PM 2:05:30 PM M4

4 Skipped this test run

5 One woman walking 2:07:18 PM 2:13:22 PM W1

6 One woman walking with 20-lb backpack 2:15:44 PM 2:21:20 PM W2

7 One man and one woman walking 2:23:49 PM 2:29:53 PM W3, M4

8 Two women walking 2:43:43 PM 2:49:50 PM W1, W2

9 Skipped this test run

10 Skipped this test run

11 Three men walking 2:52:23 PM 2:58:30PM M1, M4 with large iron pipe, 

M11
22

Table 7. Test scenarios and data collection start and end times for test site 3, Thursday, December 10, 2009

(continued).

Run 

No.

Test Scenario Start time Stop time Notes

12 Two men and one woman walking 3:00:42 PM 3:06:54PM M4, M11, W4, jet noise 

overhead 3:04:00

13 Skipped this test run

14 Skipped this test run

15 Skipped this test run

16 Four men, three women, and one dog 

walking

3:11:04 PM 3:17:39 PM M1, M12, M4 with large iron 

pipe, M6, W4, W3, W2 with 

dog

16a Background ambient noise test 3:20:49 PM 3:24:00 PM Radio talk 3:21:18, voices and 

radio talking 3:22:15, crow 

called 3:23:20

4. Data Analysis and Algorithm Development

4.1 Acoustic Sensor Data Analysis for Personnel Detection

The analysis of the acoustic sensor data is primarily concentrated on three aspects: (1) human 

voice detection by way of formant detection (8), (2) spread of energy of human voice in various 

spectral bands (7, 8), and (3) footstep detection for estimation of cadence. 

4.1.1 Detection of Formants

Humans generate their speech sounds by modulating the vocal cords and appropriately opening 

and closing their vocal tract. In general, there are several frequencies associated with voice and 

they are called formants. A small segment of speech signal is shown in figure 21. One would 

notice from figure 21 that whenever a word is spoken a burst of high frequency signal appears 

along with the background noise. This high frequency signal is called the formant and it varies 

from person to person and also on the word spoken. In general, the frequency lies between 200–

800 Hz for the people we tested. Figure 22a shows an expanded version of the first segment of 

the voice signal shown in figure 21, and figure 22b shows the Fourier transform of the segment. 

Clearly, one can see the dominant frequency around 300 Hz. The objective of the signal 

processing is to detect and determine this frequency. As mentioned previously, the carrier 

frequency (formant) is amplitude modulated whose representation may be given as 

( ) ( sin(2 ))cos(2 ) cm m c st A A f t ft = + π π (1)

where “fc” and “fm” represent the carrier and modulating frequencies and “Ac” and “Am” denote 

their magnitudes, respectively. The signal has three distinct frequency components, namely, fc, fc

+ fm, and fc – fm. The spread (see figure 22b) of frequency is then ±fm around the carrier. The 
23

algorithm for detection of human voice consists of estimating the formant (carrier frequency) and 

the spread. If the spread is above some threshold, we declare it is a human voice. Statistical 

analysis is performed on various speech signals in order to determine the threshold value. 

Figure 21. Sample voice data.

Figure 22. (a) Portion of voice signal in figure 16 and (b) its spectrum.

4.1.2 Detection of People Using Energy in Several Bands

It is known (8) that the human voice spans 50 Hz–20 kHz frequency range; however, most of the 

energy is concentrated in four bands, as can be seen in figure 22b. These bands are 50–250, 

251–500, 501–750, and 751–1000 Hz. The energy levels in these bands are the features and 

designated by the feature vector X = {x1,x2,x3,x4}, where xi is the energy in band ‘i’. The feature 

vectors are used to classify whether they belong to human voice or not using a multivariate 
24

Gaussian (MVG) classifier, as described in reference 7. For the sake of continuity, we present a 

short description of the MVG classifier. We assume the energy levels in each band are 

statistically independent and have the Gaussian distribution given by 

{ } 1

1/2 1/2

1 ( ) exp ( ) ( ) 1

2 (2 )

T

i ii ii

i

p X XM XM

π

− = − −∑−

∑

, (2)

where Mi and ∑i denote the mean and variance respectively and T denotes the transpose. Then,

the likelihood that a person is present or not is given by

( | ) ( | ) ( ), {0,1}

1

=∏ =

=

p X H p X H p H j j

N

i

j i j , (3)

where H0 and H1 are the hypotheses that correspond to a person is not present and a person is 

present, respectively. Then, the posterior probability of human presence is given by

( )

( ) ( )

( ) ( ) ( ) ( )

.

| |

|

|

1

1

0 1

1

0

1

1

1

1

p X H p H p X H p H

p X H p H

p H X N

i

i

N

i

i

N

i

i

∏ ∏

∏

= =

=

+

= (4)

Assuming the priors p(H0) = p(H1) = 0.5, we can compute the posterior probability of a human 

present given X. If it exceeds a particular threshold value, we declare that a human is detected. 

The algorithm is given as follows:

Algorithm 1: 

1. Let s(t) correspond to 1-s data

2. S = fft(s) is the Fast Fourier transform of the signal s(t). 

3. Compute the energy in each band. These energies become the feature vector 

X = {x1,x2,x3,x4}. Compute the mean and variances for feature xi for all i.

4. Use equations 2 and 4 with appropriate means and variances for noise and statistics 

collected on people to compute the posterior probability p(H1|X)

5. Use the posterior probability for fusion and declare that a person is detected if p(H1|X) > 

0.6.

An additional component of the acoustic data analysis is footstep detection. This is the same as 

footstep detection using seismic sensor data and is covered in the next section. Figure 23 shows 

the block diagram for acoustic data analysis for personnel detection. 
25

Figure 23. Acoustic data processing.

4.2 Seismic Data Analysis

The main purpose of seismic sensor is to detect footfalls of humans walking within the receptive 

field of the sensor. There is a considerable amount of literature (1–6) in footstep detection. 

Traditionally, they focused on estimation of cadence. However, if multiple people are in the 

vicinity of the sensor and walking, it is difficult to estimate the cadence of an individual person. 

Moreover, if there are animals present, it is difficult to differentiate multiple people walking and 

animals walking by observing the footfalls. However, multiple footfalls superpose one another 

resulting in a frequency of “c” Hz (where “c” is the cadence of a person) along with several 

harmonics of “c.” So, seismic algorithms would look for harmonics of cadence or several strong 

frequency components between 2 to 15 Hz. The seismic algorithm is similar to that of algorithm 

1, once the feature set is determined. For the feature set, we first compute the spectrum of the 

envelop (1, 7) of the seismic signal accumulated for a period of 6 s (to encompass several 

footfalls). Then, the feature set consists of amplitudes of the frequency bins from 2 to 

15 Hz. These amplitudes become the feature set X = {x1, x2,...,xn}. Then, algorithm 1 is used to 

estimate the posterior probability of footsteps present.

As mentioned earlier, the algorithm only determines whether there are footsteps or not. However, 

in order to determine the presence of humans, it is necessary to determine whether these 

footsteps belong to a human or an animal. For this, we invariably turn to acoustics. If there is 

voice, it can be detected and identified as human voice based on the formants as described in 

section 4.1. In order to distinguish between people and animals, we analyze the sound generated 

by the animals walking. When the hoofs of a horse hit the ground, they make a distinct sound. 

Figure 24a shows the signature of a horse walking (for a period of 6 s) and figure 24b shows the 

signature with the noise removed. The noise removal is performed using empirical mode 

decomposition (4, 6) of the original signal into various component signals. From figure 24, it is 

clear that there are three peaks uniformly distributed in each time interval of one second. This 

indicates the cadence of the horse to be approximately 2.8 to 3 Hz. Since the cadence of a person 

is around 1.5 to 2 Hz, one can infer the presence of animals. 
26

Figure 24. (a) Hoof signature (b) hoof signature after noise removal.

In this section, we process the ultrasonic data. This is primarily analyzed to distinguish the 

characteristic arm and leg movements by observing the micro-Doppler pertaining to these limbs. 

Typical micro-Doppler frequencies from moving portions (arms, legs, chest, etc.) of a person

walking for the active ultrasonic sensor are shown in figure 25. Ideally, the micro-Doppler 

signatures from humans will be sufficiently different from animals to enable target classification. 

However, since the analysis of the micro-Doppler signatures is still ongoing, it remains to be 

seen if this capability can be realized. Here, we are able to process the ultrasonic data to count 

the number of targets in the vicinity using the energy content in the micro-Doppler return. 

Figure 26 shows the flow chart for the algorithm used in counting the number of targets. For 

processing the ultrasonic data, a 1-s interval of the sampled data is used, and the algorithm 

shown in figure 26 is used to find the energy in each band. Then a sliding window is used, which 

covers 10,000 samples (approximately 0.1 s), and next set of data is obtained and processed.

Figure 25. Micro-Doppler signature of a person walking for the active ultrasonic sensor.
27

Figure 26. Flow chart for ultrasonic data processing.

In section 5, we describe the data collection scenarios used and show some of the results of 

various algorithms on real data collected at southwest border. 

5. Data Collection and Algorithm Results

In order to collect data in a realistic environment, we traveled to the U.S. southwest border and 

performed the experiments on routes typically used by smugglers. We selected three types of 

common travel routes used by illegal immigrants crossing the border:

1. A wash, a flashflood river bed with fine grain sand interspersed with rocks

2. A trail, a worn path through the shrubs and bushes usually heading towards a pick-up 

destination

3. A choke point, a valley between two mountains or large hills

During the data collection experiment, we used several sensor modalities: acoustic, seismic, PIR, 

magnetic, E-field, active and passive ultrasonic, IR and visible imagers, and video sensors. Each 

sensor suite was alternately placed along the selected travel path about 50 m apart. Some of the 

scenarios used for data collection experiment included (1) a single person walking with and 

without a back pack, (2) two people walking, (3) multiple people walking, (4) one person leading 

an animal, (5) two people leading animals, and (6) three people leading animals with and without 

payloads. A total of 26 scenarios with various combinations of people, animals and payload were 

run and data were collected for the three chosen test sites. The data were collected over a period 

of three days, at a different site and different environment each day. At times, there was wind 

and other times it was still and quiet.
28

5.1 Voice Analysis and Algorithm Results

Acoustic data was analyzed for the presence of formants pertaining to human speech. The 

algorithm results are shown in figure 27a for a segment of data collected during the experiment. 

Figure 27b is a bar chart showing the detections of humans. The results of the analysis are 

verified by playing back the recorded audio ground truth and listening for the human voices. The 

detection at time period “9 s” is clearly a false alarm.

Figure 27. (a) Acoustic data and (b) detections.

5.2 Seismic Data Analysis and Algorithm Results

The seismic data was analyzed for footstep detection, as described in section 4.2, and the results 

are shown in figure 28a. In figure 28b, we find that the detection algorithm did not detect initial 

footsteps for about 8 s as the amplitudes of the footsteps are very weak. Once the footsteps are 

detected, the algorithm continued to detect until the footsteps faded away. We also notice there 

are a few misdetections. The initial lapse is due to the fact that 6 s worth of data is needed to 

analyze the foot step detection. The misdetections could be due to the training data.
29

Figure 28. (a) Seismic data and (b) detections.

5.3 Ultrasonic Data Analysis for Counting Number of Targets

The ultrasonic data was analyzed for counting the number of targets using the algorithm shown 

in figure 26. The results are shown in figure 29. Each subfigure in figure 29 corresponds to one 

of the scenarios enacted during the data collection test. These figures show how many people and 

animals are walking as analyzed by the ultrasonic sensor. The x-axis of each subfigure 

corresponds to the number of iterations the algorithm uses; for each iteration, the data set is time

shifted by 0.1 s. The number of distinct peaks in each subfigure shows the number of targets.

Figure 29. Results of ultrasonic data processing.
30

6. Conclusion

The goal of the experiment was to collect personnel, animal, and vehicle signatures at active 

southwestern U.S. border crossing locations using an array of multimodal sensors. The resulting 

data set allows ARL researchers to analyze and better understand target phenomenology, to 

provide signatures for the development of multi-sensor fusion algorithms, and to enable the 

creation of detection and classification algorithms (for single and multi-sensor systems) and 

target counting algorithms. An added benefit of the research will be the determination of which 

sensors are best suited to work in concert, and which algorithms and sensors maximize the 

probability of correctly classifying humans versus animals versus vehicles for tomorrow’s 

unattended ground sensor systems. The initial research findings using border crossing 

multimodal data show great promise in the development of sensor fusion algorithms for target 

discrimination, and likely will continue to be a focus of researchers at ARL and academia in the 

near future.
31

7. References

1. Houston, K. M.; McGaffigan, D. P. Spectrum Analysis Techniques for Personnel Detection 

Using Seismic Sensors. Proc. of SPIE 2003, 5090, 162–173. 

2. Sabatier, James M.; Ekimov, Alexander. Range Limitation for Seismic Footstep Detection. 

Proc. of SPIE 2008, 6963, 69630V-1.

3. Ekimov, Alexander; Sabatier, James M. Passive Ultrasonic Method for Human Footstep 

Detection. Proc. of SPIE 2007, 6562, 656203.

4. Sunderesan, A.; Subramanian, A.; Varshney, P. K.; Damarla, T. A Coupula Based Semiparametric Approach for Footstep Detection Using Seismic Sensor Networks. Proc. of SPIE

2010, 7710, 77100C.

5. Bland, R. E. Acoustic and Seismic Signal Processing for Footstep Detection. Master’s 

Thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and 

Computer Science, 2006.

6. Iyengar, S. G.; Varshney, P. K.; Damarla, T. On the Detection of Footsteps Based on 

Acoustic and Seismic Sensing. in Conference Record of the Forty First Asilomar 

Conference on Signals, Systems and Computers 4–7 Nov. 2007, ACSSC 2007, 2248–2252.

7. Damarla, Raju; Ufford, David. Personnel Detection Using Ground Sensors. Proc. of SPIE

2007, 6562, 656205.

8. Encyclopedia of Acoustics, Vol. 4, Edited by Malcolm J. Crocker, Published by John Wiley 

& Sons, Inc. New York, NY, 1997.
32

List of Symbols, Abbreviations, and Acronyms

ARDEC Armament Research and Development Center 

ARL U.S. Army Research Laboratory

B&K Bruel & Kjaer

CW continuous-wave

DAQ data acquisition 

GPS global positioning system

FOV field of view

ISR Intelligence, Surveillance, and Reconnaissance

MVG multivariate Gaussian

NVESD Night Vision and Electronic Sensors Directorate

P/N part number 

PIR passive infrared

TRSS Tactical Remote Sensor System

UDS Doppler sensor

UR microphone receiver
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RECORDS MGMT

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 R SARTAIN

 T WALKER (5 COPIES)

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 K BENNETT

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ATTN RDRL SES E 

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