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Technical Paper 328 ~ ! 

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THE VALUE OF SPECIAL TRAINING FOR THE

INTERPRETATION OF UGS EMPLOYED IN A GRID

Sterling Pilette and Bill Biggs ~~~~

U.S. Army Research Institute C’

HUMAN FACTORS TECHNICAL AREA

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U. S. Army

Research Institute for the Behavioral and Social Sciences

October 1978

Approved for pubhc refease ‘itribution u n t mt e d.

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T~~ T~~~~

U. S. ARMY RESEARCH INSTITUTE

FOR T HE BEH AVIORAL A ND SOCIAL SCI ENC ES

A Field Operating A gency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

WILLIAM L. HAUSER

JOSEPH ZEIDNER Colonel , US Army

Technical Director Commander

Research accomplished under contract

to the Department of the Army

HRB—Singer , Inc.

NOTICES

DISTRIBUTION: Primary distribution of this report has been mede by ARI. Please address correspondence

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oncerning dittribution of reports to: U. S. Army Reseerch Institute for the Behaviorel end Social Sciences ,

ATTN. PERI.P . 5001 Eisenhower Avenue . Alexandria . Virginia 22333.

FINAL DISPOSITION: This report may be destroyed when it is no longer needed. Please do not return it to

the U. S. Army Research Institute for the Behavioral and Social Scienc es.

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Technical Paper 328

— 4. TITLE (w~~~S~~~~ gjj ~~~~~~____..._ _ .__._ .-..—.-——- —~~~~~ / 5. TYPE OF REPORT & PERIOD COVERED

INT~ RPRETATIdN OF UGS EMPLOYED 

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Haroldl Martinek/ (Army Research Institute) ___________________________

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AREA & WORK UNIT NUMBERS

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State College, PA 16801 II, CONT ROLLING OFFICE NAME AND ADDRESS 12. REP

U.S. Army Intelligence Center & School ( /

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Fort Huachuca , AZ 85613 ‘S

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U.S. Army Research Institute for the Unclassified

Behavioral and Social Sciences ______________________________

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FICAT iON DOW NGRA Di NG

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: 17. DISTRIBUTION STATEMENT (of the abafract entered in Block 20, if different from Report)

IC. SUPPLEM ENTARY NOTES

IS. KEY WORDS (Continue on reverie aide if nece.aary mid identify by block number)

Unattended ground sensors Target acquisition

Remotely monitored sensors Sensors

Grid deployment Surveillance

Train ing Int elligen ce ‘ Wo rkload 20. ABSTRACT (Continue on reverie dde If nec....ry and identify by block number)

‘~~

Based on an error analysis of unattended ground sensor operator target detection data, a self—paced training program was developed to reduce the frequency

and magnitude of operator errors and increase target detection rate. The

first training unit dealt with solitary targets—-only one vehicle or vehicle

convoy traveling through the grid. The second dealt with target clusters--

several vehicles or vehicle convoys traveling through the grid at one time . —

DD I J AN 73 1473 EDIT ION OF I NOV 65 IS OBSOLETE Unclassified

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SECU RITY CLASSIFICATION OF THIS PAGE(Wh .n Data Entered)

Item 20. (Continued)

To assess the value of training a pre-posttest design was used. Operators - monitored for 2 hours activation data from seismic sensors employed in a grid ,

received the special training, and again monitored for 2 hours. The two monitoring periods were counterbalanced to control for differences in the target

activation data. Four workload levels (numbers of targets activating the

sensors) were used to ascertain training effects across a range of workloads

typical of operational conditions.

The self-paced training program resulted in improved operator performance in

target detection and target speed and direction estimation accuracy of both

practical and statistical significance. Target detection improved from 58%

to 79%, averaged over all conditions. Target detection after training averaged

61%, 66%, 94%, and 97% for the four workload conditions (27, 15, 8, and 5

targets/hour) . Speed and direction estimation improved by 23% and 20% respectively as a result of training. The false alarm rate was negligible under all

conditions. Conclusions and recommendations for the implementation of this

training program and for utilization of grid deployment are provided.

Unclassified SECURITY CLASSIFICATION OF THIS PAGE (lPlten Data Entered)

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Technical Paper 328

THE VALUE OF SPECIAL TRAINING FOR THE

INTERPRETATION OF UGS EMPLOYED IN A GRID

Sterling Pil ette and Bill Biggs

HRB-Singer , Inc.

and

Harold Martinek

U.S. Army Research Institute

HUMAN FACTORS TECHNICALAREA

Submitted as complete and Approved By:

technically accurate , by:

Edgar M. Johnson Frank J. Harris , Acting Director

Technical Area Chief - ORGANIZATIONS AND SYSTEMS

RESEARCH LABORATORY

Joseph Zeidner

TECHNICAL DIRECTOR

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAV 1ORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue , Alexandria , Virginia 22333

Office , Deputy Chief of Staff for Personnel

Department of the Army

October 1978

Army Project Number Surveillance Systems

2076271 7A721 DAHC19•75•C~

OO23

Approved for public release ; distribution unlim ited.
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IARI Research Reports and Technical Papers are intended for sponsors of

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

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

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

agencies by briefing or Disposition Form.

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FOREWORD

The Battlefield Information Systems Technical Area of the Army

Research Institute (ARI) is concerned with the human resource demands

of the increasingly complex battlefield systems required to acquire ,

transmit, process , disseminate , and use information. Research focuses

on interface problems and interactions within command and control centers in such areas as topographic products and procedures , tactical

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

An area of special interest is that of human factors problems in

the efficient and effective 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 (UGS) .

When these remote sensors are activated by enemy personnel or vehicle

movement , a monitor display located behind friendly lines indicates the

activity. From this display , the operator can deduce 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.

Previous ARI research has demonstrated a need for special operator

training in the interpretation of activation patterns for effective employment of sensors in a grid for surveillance of large area, enemy intrusion. The research effort reported here developed and demonstrated

the effectiveness of a special training module and provides estimates

of operator—sensor capability using the grid concept.

Research in the area of sensor systems integration and utilization

is conducted as an in—house effort augmented through contracts with

organizations selected for their unique capabilities and facilities for

research on sensor systems. The present research was conducted in collaboration with personnel from HRB-Singer , Inc. , under the program

direction of Robert S. Andrews . The effort is responsive to requirements of Army Project 2Q7627 17A721 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 Project (REMBASS) . Special requirements are

contained in Human Resource Need 74-21, Analysis of Unattended Ground

Sensor Derived Data, and 75-5, Development of Basic UGS Data for Displays, Training, and Operational Use.

JO PH ~~~~~~~ hnical Director 
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THE VALUE OF SPECIAL TRAINING FOR THE INTERPRETATION OF UGS EMPLOYED

IN A GRID

BRIEF

Requirements:

1. To determine the value of specialized training for improving

operator target detection and direction and speed estimation performance when using unattended ground sensors (UGS) employed in a grid.

2. To provide estimates of operator performance in an operational

situation for target detection completeness and direction and speed

estimation accuracy for UGS employed in a grid.

Procedure :

Based on an error analysis of operator target detection data from

previous research , a self—paced training program was developed to reduce

the frequency of operator errors. Ta assess the value ‘-f the training

program , a pre—posttest design was used. Two 2—hour scenarios consisting of various numbers and compositions of convoys traveling cross

country were constructed from activation data collected at a field exercise. Four target workload conditions (2.5, 4.0, 7.5 , and 13.5 targets per 30—minute period) were systematically varied within each 2-hour

scenario . Each operator monitored all four workloads during both the

pretest and posttest scenarios.

Two groups of eight trained operators of the Remote Sensor Platoon

of the 2nd Armored Division at Fort Hood participated in 3 days of training and performance testing. Prior to the pretest, each group was given

an orientation briefing, an introduction to the grid employment of UGS ,

row patching technique training , and test procedure training. Each

operator then interpreted one of the two scenarios to determine his

baseline performance in target detection and direction and speed estimation accuracy. He next completed the specialized two-unit training

program . The first unit dealt with solitary targets (vehicle or vehicle

convoy traveling alone through the grid) , and the second dealt w ith

target clusters (several targets in the grid area at the same time).

The training program was individualized (self-paced) , and expert assistance was reniered when needed. Student mastery was ascertained by the

monitors checking practical and criterion exercise answers.
~ ---—

Operators used three aids in the training and in the posttest; they

used the UGS ruler and the speed table to make time measurements and to

facilitate arithmetic calculations , and used the protractor to make more

precise target direction estimates.

Findings :

The specialized training program with operator aids signif icantly

improved operator performance in target-detection completeness and target speed and direction estimation accuracy . Target-detection completeness improved by 38%, while speed-estimation accuracy improved by 23%.

The accuracy of target-direction estimation improved by 20%. The initially low false—alarm rate showed no significant change . After training , operators detected about 95% of the targets during low workload

conditions (5-8 targets per hour), 66% when the workload was doubled

(15 targets per hour), and 61% when the workload reached 27 targets per

hour.

Utilization of Findings :

The training materials and operator aids have been integrated into

(JGS training at U.S. Army Intelligence Center and School (USAICS), Fort . -

Huachuca , Ariz.

The lesson materials should be given to personnel of all remote

sensor platoons periodically (every 6—12 months) to provide practice

and review .

Intelligence personnel should be trained to properly assess operator ’s reports based on the operator capability data in this report , so

that they can effectively employ UGS and use the resulting intelligence

information .

The grid employment of UGS can be used as an ear ly war ning and

target acquisition system. The grid can also provide approximate speed

estimates for use in a target acquisition system.

The atypical target paths used in this experiment prevent a good

estimation of the operator ’s ability to provide target direction data.

However , results from the few available “normal” target paths indicate

that the operator can give useful direction information (average deviation from true direction was ±26°). The usefulness of direction esti—

mates should be ascertained using additional activation data in a second

experiment. 

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THE VALUE OF SPECIAL TRAINING FOR THE INTERP RETATION OF UGS EMPLOYED

IN A GRID

CONTENTS

Page

INTRODUCTION 1

OBJECTIVES 5

METHOD 5

Development of the Training Content and

Instructional Approach 5

Population and Sample 7

Apparatus 7

Research Design 8

Experimental Design 9

Development of the Test Scenarios 10

Test Procedure 12

RESULTS AND DISCUSSION 12

Detection Completeness 13

False Alarms 16

Target Direction Deviation 16

Target Speed Deviation 19

CONCLUSIONS AND RECOMMENDATIONS 20

Conclusions 20

Recommendations 21

REFERENCES 23

APPENDIX A. ORIENTATION BRIEFING 25

B. INTRODUCTION TO THE GRID DEPLOYMENT PATTERN

(LECTURE/DISCUSSION ) 27

C. TEST PROCEDURE TRAINING 31

D. THE GRID DEPLOYMENT OF SEISMIC SENSORS USING

ROW PATCHING 39

DISTRIBUTION 59
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LIST OF TABLES

Table 1. Experimental design showing workload order

by groups and periods 10

2. Order of administration of 30—minute segments . . 11

3. Schedule of administration at Fort Hood 13

4. Analysis of variance for detection completeness . 14

5. Means and analysis of workload x pretest/posttest

interaction for detection completeness 15

6. Pretest/posttest x period interaction for

detection completeness 16

7. Pretest/posttest false alarms 17

8. Analysis of variance for direction deviations

(absolute) 18

9. Means of absolute direction deviations by periods 19

10. Field detection performance expectations 21

LIST OF FIGURES —

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

choke-point 3

A
THE VALUE OF SPECIAL TRAINING FOR THE INTERP RETATION

OF UGS EMPLOYED IN A GRID

INTROIDUCTION

Unattended ground sensors (UGS) represent a significant part of

the Army ’s capability for detection , location , and acquisition of enemy

activity at a remote location . They can be used alone or combined with

ground surveill ance radars , nigh t vision devices , aerial surveillance——

side-looking radar (SLAR) , infrared (IR), photography , and visual--signal

intelligence , patrols , and observation and listening posts to produce

timely and reliable intelligence information. The Army uses several

types of UGS, which can be categorized according to the method of remote sensing : seismic , acoustic , magnetic , electromagnetic , and infrared . UGS are tactically employed for offen sive and defensive operations

by units from small independent patrols to full division-size operations.

Among the offensive operations are the following :

Target acquisition--Sensor ’s real-time detection capability

leads to immediate reaction .

Landing (drop) zone--Sensors monitor enemy activity for future airrnobile assault.

Combat sweep—-Sensors monitor enei~y withdrawal or attack activity .

Ambush-—Sensors establish enemy habits; are employed with a

remote firing device and command—detonated mines.

Among the defensive operations are the following :

Base camp defense——Sensors provide warning of enemy presence ;

extend listening post/observation post detection range.

Convoy security—-Sensors provide ambush detection and warning .

Border surveillance-—Sensors provide warning of enemy presence

and fire control information for real-time reaction .

Beach defense——Sensors provide warning of counterattack in beachhead situations .

Whether for offensive or defensive tactical purposes , UGS can be employed in any of three ways : string, grid , or alerting. In string

employment , UGS are employed along a potential transportation route

(land or water). Whether UGS are hand—emplaced or air—delivered , the

sensors should be emplanted accurately so that their location wit

h1
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respect to the route and their separation distances are known. This

enhances manual readout by permitting the derivation of relatively

accurate direction , speed , and length of column information from the

sensor activation patterns . If hand—emplaced , the sensor locations can

be accurately located on a map and “seated” properly in the ground.

In 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 (see Figure 1). A grid

would normally be used in defensive operations such as for base camp

security or early warning near the forward edge of the battlefield.

Again , the objective is to emplant the sensors accurately although this

is more difficult than in string employment. The grid is designed to

maximize the probability of detecting and acquiring enemy forces intruding into any portion and from any direction within a large area

(several square kilometers). Because the path of the target is estimated , the operator can only grossly estimate speed. Even with special

operator training , procedures , and job aids , the accuracy of estimates

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

usually obtained with the string employment of UGS.

In the alerting employment , UGS are employed to “cover ” a given

route or ground area where , for various reasons , the exact locations

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

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

be the case when sensors are delivered by mortar or artillery in areas

heavily dominated by enemy forces and in areas inaccessible to friendly

units. Also, the situation can occur when sensors have been air—delivered

under poor visibility conditions. Whatever the cause , the operator knows

only the approximate location of the sensors . Reliable detections of

the presence of activity can be made , but additional information such as

speed , number of targets , and direction cannot be computed accurately.

The U.S. Army Int’

~

tlligence Center and School (USAICS) teaches the

string employment concept and the alerting employment concept . Other

than a brief overview , however , the school does not train students on

monitoring and interpretation procedures for grid-type employment. Although it has received little attention by military or civilian researchers , grid employment can be used in almost all the offensive and defensive operations discussed previously. In the past, tJGS operators

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

conflict to worldwide usage , the potential applications of grid employment in area intrusion situations have increased.

The effects of this lack of formal training in grid interpretation

have been apparent in previous experiments (Edwards , Rochford , &

Shvern , 1977; Pilette et al., 1978; and Edwards et al., 1978) . Opera—

tor performance in such areas as target detection , direction determina—

tion , and speed estimation has been lower than performance using the

string employment of sensors.

2

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Figure 1. ~xample of a 9-sen sor grid used

at a natural chokepoint.

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An error analysis was performed on data collected in the above

experiments to determine specifically where errors were being made and

what were their causes. All three projects involved seismic sensor ’

activation data collected during field exercises conducted at Fort

Bragg, N.C. During these exercises, different combinations of military

vehicles traversed a grid sensor field 1 kilometer square . Within this

kilometer square , nine seismic sensors had been hand—emplaced 500 meters

apart in the pattern shown in Figure 1.

In these three projects, as in the present one , the task of the

UGS operator was to detect and analyze as many targets as he could within the time allotted. The operator was given a “Target Log ” in which to

report his target analyses. The information he was required to report

was consistent with that needed by a field commander , i.e., target detection , direction , and speed. Additional information collected for

scoring and error analysis purposes included the distance that the target traveled through the grid and the time that the target took to t x - ~

iverse the grid (called midpoint time difference).

The details of the analyses of operator errors made in the above

research are in Edwards , Pilette , and Martinek (1977). A summary of

the errors , causes , and circumstances surrounding the errors is presented below .

1. Errors of omission increased:

a. During conditions of high target activity or high target

loads ;

b. For targets occurring near the end of the activation records during high-target-load conditions;

c. For targets traveling direct (straight) trails during low

target activity ;

d. For targets with noncontinuous sensor patterns;

e. For targets having other targets in close proximity ( adjacent activations on the activation records) ;

f. For targets with small sensor patterns ;

g. For targets whose sensor pattern s overlapped w ith the

sensor patte±~ns of other targets during high activity; and

h. When operators mismanaged target logs.

2. False alarms increased (but were infrequent) during low target

activity.

4
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3. Direction deviation error increased:

a. For vehicle convoys during high activity (as compared to

single—vehicle targets);

b. For targets traveling indirect routes during high activity;

c . For targets traveling direct routes during low activity ;

arid

d. Because of operator procedural errors in reporting.

4. Speed deviation error increased:

a. For single-vehicle targets in high-target-activity

condi tions ;

b. Due to underestimates of the distance that the target

traveled through the grid ; and

c. Because of procedural errors in reporting .

OBJECTIVES

1. To develop a training package for grid employment , based upon

an error analysis of operator performance using sensors employed in a grid.

2. To determine the improvement in operator performance resulting

from use of the training package .

3. To provide estimates of the completeness of target detection

arid accuracy of direction and speed estimates of operators when

using the grid employment of UGS.

METHOD

Development of the Training Content and Instructional Approach

The operator monitoring errors and their apparent causes and circumstances formed the basis for developing the subject ma tter con ten t

and presentation . The content was organized into two major topic areas

or units , one dealing with solitary targets and the other with target

clusters. Solitary targets are those that pass through the grid area

alone--i.e., their sensor patternS are easily distinguishable because

they are far removed from the sensor patterns of other targets. A solitary target can be either a single vehicle (e.g., one tank) or a convoy5
of vehicles (e.g., five tanks) , but with no other targets in the grid.

Deve loping a unit of instruction dealing with only solitary targets

permitted a sound development of those problem areas that deal with the

basic principles of monitoring .

A target cluster occurs on the activation records (hereafter called

the X-T plot) when more than one vehicle or convoy passes through the

grid at the same time , or about the same time , causing their sensor patterns on the X—T plot to overlap or to be very close. In this experiment , target clusters occurred only during the high-target-activity condition (seven targets appearing within a 30—minute period) . As with

solitary targets, the vehicles within a target cluster can be composed

of single vehicles and/or convoys of vehicles.

These two major content areas were divided into the following lesson units:

Unit I — Solitary Target Analysis

Lesson 1. The Grid Employment Pattern

Lesson 2. Target Pattern and Path Analysis for the Grid

Employment

Lesson 3. Speed and Direction Determination

Lesson 4. Small Sensor and Target Patterns

Unit II — Target Cluster and I~Iulti-readout Analysis

Lesson 1. Target Cluster Analysis

Lesson 2. Multidisplay Readout

With the training content determined , a training system was designed to allow each individual to proceed at his own pace , test his

own knowledge after short segments (lessons) of instruction, obtain

immediate feedback on his mastery of the content , and require only minimal instructor control and assistance . Each lesson contained the fol—

lowing six steps1:

Step 1. The Lesson Sheet was divided into three sections: Objective , Purpose , and Concept. The Objective section stated what the operator (student) should be able to do at the end of the lesson , the Purpose sec tion stated the importance of the lesson , and the Concept section

contained the instructional materials with examples.

L

Training text is available from the U.S. Army Research Institute for

the Behavioral and Social Sciences, 5001 Eisenhower Avenue , Alexandria ,

Va. 22333.

6
Step 2. The Practical Exercise Questions contained questions on

the material discussed in the lessons. These questions were intended

to allow the operator to test his mastery of the concepts in the lesson.

Step 3. The Practical Exercise Answer Key provided the operator

w ith the correc t answers to the questions and allowed him to check his

own knowledge. The operator could restudy the lesson if necessary .

Step 4. The Criterion Exercise Questions contained questions relating to information in the lesson. These questions tested the same

thing as the practical exercise questions and we re the final check for

mastery before going on to the next lesson.

Step 5. The Criterion Exercise Answer Key enabled the instructor

to evaluate the operator ’s performance on the Criterion Exercise. If

the operator performed within pre—established criterion levels , he was

given the next lesson. If he did not, he was sent back for additional

review or to an instructor for individualized help .

All the lessons except Lesson 1 of Unit II are self-administerable

wi th instructor checking the operators ’ criterion exercise answers.

Lesson 2 of Unit II presents target cluster ana lysis , which is a more

complex topic. It is anticipated that the instructor will have to assist the operator with this lesson , espec ially the inexperienced, in

understanding the concepts presented.

Population and Sample

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

l7M2O), school-trained at the U.S. Army Intelligence Center and School

at Fort Huachuca , Arizona. Sixteen enlisted personnel of the UGS platoon , 502nd MI Battalion , 2nd Armored Division stationed at Fort Hood ,

Tex ., participated in the research as UGS operators .

Apparatus

Ten R0376 Tactical Data Recorder simulators were used to display

the sensor activation records (X—T plots). The simulators were me—

chanically adjusted to drive the records at 12 inches/hour , a speed

equal to that of the operational RO376 (30-pen) Tactical Data Recorder

and a BASS III (60-pen) Tactical Data Recorder. Display housings were

placed on each simulator to reduce its viewing area size to that of the

R0376 or BASS III. The X-T plots were therefore presented at the same

speed and format as in the field situation.

7

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Research Design

The following independent variables and combinations of variables

were studied:

Pretest/Posttest (Training Effect) . The comparison of posttest

results in relation to pretest results provides the measure of the overall effectiveness of the training.

Workload. Four different workloads were analyzed:

Target Total Targets in a

Workload Activity Display 30-Minute Period

I Low 30 pen 2-3

II Low 60 pen 4

III High 30 pen 7—8

IV High 60 pen 13-14

These workloads are similar to those used in the research which provided

the data for the error analysis (ref. 3). They span an operationally

relevant range from a low—target-load condition (only 5 targets per

hour) to a high-target—load condition (27 targets per hour).

Period (Practice Effect). An analysis was performed comparing performance during each of the 30-minute periods of both the pretest and

the posttest to determine whether the operators’ performance varied over

time (period ) during the test. ]f they improved as time progressed ,

practice effects would be assumed which would confound the pretest/

posttest analysis.

Pretest/Posttest Workload Interaction. This interaction shows

whether the training affected performance differently in each of the

four workload conditions.

Pretest/Posttest Period. This interaction shows whether period

effects could have occurred differentially in the posttest and the pretest conditions. If an improvement over time was found (practice effects), an appropriate adjustment could be made to the pretest/posttest

comparisons.

Groups (and Order Effects). The 16 operators were divided randomly

into four groups of 4 each for control purposes. Each group received

the workload conditions in a different order to control on order effects.

Within each group , two operators receive i one set of 30-minute segments

of the scenario for the pretest and the second set for the posttest. The

other two operators received the sets in reverse order , thereby preventing scenario effects from confounding the pretest/posttest comparison .

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The following dependent variables were used:

Detection Completeness. If an operator reported a target when the

scenario indicated that a target could have been causing activation on

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

times 100.

False Alarms. If an operator reported a target when the scenario

indicated that no target could cause activations on the designated pens ,

the response was classified as a false alarm. In addition , if an operator reported two or more targets on the sane pens when in actuality

there was only one , the additional response(s) was classified as a false

alarm(s).

Target Direction Deviation. If the direction and speed of a target

as it leaves the grid area are known , its location at a later time can

be estimated. To compute direction deviation , the actual direction (in

degrees) that the target traveled after leaving the grid was subtracted

from direction the operator reported. The operators were trained to use

a 360 ° protractor for this purpose.

Results on target direction should be generalized with caution.

Due to space restraints in the area assigned for the collection of sen—

sor activation data at Fort Bragg , most targets after passing through

the grid were required to turn and travel parallel to the last row and

approximately 100 meters from it. Thus most of the targets activated

the entire last row of sensors ; this situation made ~1irection estimates

more difficult than if the target had kept going straight as would normally be expected.

Target Speed Deviation. As with target direction , deviation scores

were computed for target speed by comparing the operator ’s responses

with school solutions based on time and distance data obtained during

the exercise. If a correct speed (in meters/minute) was given , a score

of zero deviation would result. For an incorrect score , the leviation

in meters/minute from the school solution would be determined .

~~~~~~~~~~~~~~~~~~~

The experimental design used for the target detection completeness

analysis is a three—factor Latin Square with repeated measures on the

pretest/posttest factor (Winer , 1962). The Latin Square factors are

workload , period , and groups (see Table 1). Certain interactions were

not analyzed but were summed into a “residual” interaction term because

they were not of value to the objectives of the research except for control purposes . Scenario effects were counterbalanced between the pretes t and posttest periods but were confounded with wor kload .

9

~~~IiL.~~L ~~~~~~~~~~~~~~~~~~~ -

~~~~~~~~~~~~~~
Table 1

Experimental Design Showing Workload Order by Groups and Periods

Pretest Posttest

periods periods

Subjects 1 2 3 4 1 2 3 4

Group A (n=4) IV II I III IV II I III

Group B (n=4) II IV III - 

I II IV III I

Group C- (n=4) III I IV II III I IV II

Group D (n=4) I III II IV I III II IV

Development of the Test Scenarios

To obj ectively assess the performance of the operators , R0376, X-T

(event versus time) plots containing operationally collected target activation patterns were developed for presentation to the operators. The

target activation patterns (target patterns) were transcribed from magnetic tapes recorded at Fort Bragg during a field exercise specifically

designed to test sensors and collect activation patterns. Included in

these exercises were armored and wheeled vehicles traveling singly or

in convoys of up to 10 vehicles. The presence of artillery fire added

operational “noise” to the scenarios. Since these were controlled exercises , target location and time were known and could be related to

target patterns in developing school solutions.

The X-T plots were developed into 30-minute segments and then were

combined into 2—hour scenarios to include the correct levels and presentation order of target activity to satisfy the specific experimental

design requirements discussed earlier. No changes were made to the

original target pa tterns , although the relative positions of some target patterns were changed. Each segment portrayed three 9—sensor grids ,

using 27 of the 30 column s available on the R0376 plot. Two 30-minute ,

3—grid segments could be combined in a side—by—side presentation to represent 30 minutes of viewing on a 60-pen recorder (or 6 grids—-workload

conditions II and IV).

In accordance with the experimental design , twelve 30—minute segments (3 grids each) were selected from previous research material (Edwards et al., 1978) and numbered 1, 2 , 3, 5, 6, 7, 8, 9, 10, 12 , 13, and

14. A 2—hour scenario was constructed for each subject by combining six

30—minute segments for the pretest and six 30—minute segments for the

posttest (see Table 2). Two of the six 3—grid segments were combined to

produce a low—target—activity , 6—grid (or 60-pen recorder) workload

10
- 

~~~~~~~~~~~~

Table 2

Order of Administration of 30-Minute Segments

Pretest Posttest

Oper- Period Period Period Period Period Period Period Period

ators 1 2 3 4 1 2 3 4

Subject Group A

1 1—3 8—12 9 2 6—7 13—14 10 5

2 6—7 13—14 9 5 1—3 8—12 10 2

3 1—3 8—12 10 2 6—7 13—14 9 5

4 6—7 13—14 10 5 1—3 8—12 9 2

Subject Group B

5 8—12 6—7 2 10 13—14 1—3 5 9

6 13—14 1—3 5 10 8—12 6—7 2 9

7 13—14 1—3 2 9 8—12 6—7 5 10

8 8—12 6—7 5 9 13—14 1—3 2 10

Subject Group C

9 2 10 1—3 13—14 5 9 6—7 8—12

10 5 10 6—7 8—12 2 9 1—3 13—14

11 2 9 6—7 13—14 5 10 1— 3 8—12

12 5 9 1—3 8—12 2 10 6—7 13—14

Subject Group D

13 9 2 8—12 1—3 10 5 13—1 4 6—7

14 9 5 13—14 6—7 10 2 8—12 1—3

15 10 2 8—12 1—3 9 5 13—14 6—7

16 10 5 13—14 6—7 9 2 8—12 1—3

11
- - 

~~~~UI~~~

I

condition (eight targets/hour). Two other 3-grid segments were used

for the high—target—activity, 6-grid workload conditions (27 targets/

hour). The remaining two 3—grid segments (low and high target activity)

were used for the other two workload segments (each 3-grid). The 30-

minute segments were counterbalanced across the four groups of subjects

periods , and pretest/posttest conditions in the order shown in Table 2,

to prevent confounding of these variables by scenario differences. The

workload variable and scenario effects are confounded.

Tes t Procedure

Prior to taking the pretest , the operators participated in a fourpart grid orientation and test procedure training . These involved the

following: Orientation Briefing (Appendix A) , Introduction to the Grid

Employment Pattern (Appendix B), Test Procedure Training (Appendix C),

and Row Patching Technique Training (Appendix D).

Three job aids were provided to the operators for their use in this

training-—a speed table, an UGS ruler , and a protractor . The speed table

circumvents the need to perform arithmetic division , which is a frequent

error source for some operators. The UGS ruler assists the operator in

measuring ground distance on the grid and time on the X-T plots. Both

job aids were used in previous studies. They are discussed in Appendix D.

The protractor is a new job aid introduced in this study . The operators were taught how to use the protractor to determine the direction

(in degrees) in which the target was heading when it left the grid area.

The protractor helps the operator provide a more precise target direction

to the Army commander——which of course is not needed when strings are employed along a roadway . Use of the protractor is discussed in Appendix D.

The trainino and testing schedule by day is presented in Table 3.

Because only 11 Tactical Data Recorder simulators were available , two

groups of eight operators each participated in the 3—day program in succession. The first group of operators participated for 3 work days for

the f irst week and the second group of operators for 3 work days the

second week.

RESULTS AND DISCUSSION

The results are discussed in terms of the four dependent variables:

detection completeness , false alarms, target direction deviation, and

target speed deviation . The independent variables are discussed separately under each of these as appropriate ; not all independent variables

were analyzed for all dependent variables, because of restrictions in

the data obtained. Thus, the complete analysis of variance was computed

on only the detection completeness variable.

12
Table 3

Schedule of Administration at Fort Hood

Day 1 8:00 a.m. - Orientation Briefing

8:15 — Introduction tn the Grid Employment Pattern

8:30 - Test Procedure Training (average 1 hour)

Patching Technique Training (average 2 hours )

11:30 - Lunch

1:00 p.m. - Finish Pretraining - Question Period

2:00 — Pretest Scenario Administration

4:00 — Finish

Day 2 8:00 a.m. — Unit I — Lesson 1 (average 45 minutes)

9:00 — Unit I — Lesson 2 (average 1 hour)

10:00 — Unit I — Lesson 3 (average 1 hour 15 minutes)

11:00 — Unit I — Lesson 4 (average 1 hour)

11:30 — Lunch

1:00 p.m. — Finish Unit I materials

Start Unit II — Lesson 1

5:00 — Finish

Day 3 8:00 a.m. - Finish Unit I - Lesson 1

10:30 — Unit II - Lesson 1, Practical Exercise Vu-graph

11:00 - Unit II - Lesson 1, Final Lesson Exercise Vu-graph

11:45 — Lunch

1:00 p.m. - Unit II - Lesson 2

2:00 - Posttest Scenario Administration

4:00 — Finish

Detection Completeness

Table 4 presents the analysis of variance results for target detection completeness expressed as the percentage of targets detected.

The significant difference between pretest and posttest performance

indicates that the training package was effective in increasing operator

performance . The operator average detection completeness increased from

57.5% on the pretest to 79.4% on the posttest. This increase represents

a 38% improvement over pretest performance .

13
——------- 

~~~~

- -— ~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Table 4

Analysis of Variance for Detection Completeness

Signif—

Source df SS MS F icance

Between subjects 15 21 , 900

Groups 3 4,554 1,518 1.05 NS

Subjects w . groups 12 17, 346 1,446

Within subjects 112 115 , 034

Workload (A) 3 58 ,693 19,564 65.83 .01

Periods (B) 3 6 ,581 2 , 194 7 . 38 .01

Pretest/posttest (C) 1 15, 312 15, 312 51.52 .01

A x C 3 4,664 1,555 5.23 .01

B x C 3 1,063 354 1.19 NS

Residual interactions 15 3 ,756 250 <1 NS

Pooled error 84 24 ,964 297 . 

-

Total 127 136,934

Significant differences were found (as shown in Table 4) for both

workload and its interaction with pretest/posttest. The workload differences in detection completeness are generally similar to those of

previous research but are lower and do not indicate the same linear relationship between number of targets (workload) and detection completeness (Edwards et al., 1978). A possible explanation of these dissimilarities may be in the difficulty levels of the target patterns used

in the specific 30—minute segments : i.e., the sce nario effe cts were

confounded with workload.

The significant workload x pretest/posttest interaction indicates

that the training had a differential effect on the operators ’ performance under the various workloads . Table S shows that significant improvements occurred in the case of Workloads I, III , and IV but not for

Workload II. Although Workload II presented more targets than Workload

I, detection completeness was 88% for Workload II and only 81% for Work—

load I in the pretest condition .

14

~~ ________________________________________________ 

- - -
Table 5

Means and Analysis of Workload

Pretest/Posttest Interaction for Detection Completeness

Signif—

Workload Pretest Posttest SS df F icance

I (5 targets/hr , 30—pen ) 81% 97% 1,953.125 1 6.57 .05

II (8 targets/hr . 60—pen) 88% 94% 312.500 1 1.05 NS

III (15 targets/hr, 30—pen) 28% 66% 11,742.78 1 39.51 .01

IV (27 targets/hr , 60—pen) 34% 61% 5,967.78 1 20.08 .01

Error (within) 24 ,964.375 84

Note : Analysis of simple effects , from Winer , 1962, pp. 571—574.

A similar result in the pretest condition for Workloads III and IV is

shown in Table 5, where detection was 28% for Workload III (15 targets/hr )

and higher (34%) for Workload IV (27 targets) in spite of the greater nunber of targets presented. A possible hypothesis to explain these results

is that a 60-pen display (6 grids) was used for Workloads II and IV. However , this effect was not found in a previous study by Edwards et al.

(1978), which used some of the same workload conditions (30- and 60-pen)

and similar scenarios.

The detection rates for the heavier workloads (III and IV) were about

100% higher in the posttest as compared to the pretest. The corresponding

increase for Workload I (which also showed a significant increase) was much

lower (20%). The increase of Workload III (28% to 66%) was significantly

higher (t = 2.17, df = 15) than the increase of Workload I (81% to 97%);

other compar isons of increases were not significantlj different. A greater

increase at high workloads was expected , as the greatest emphasis in the

training was placed on eliminating errors of omiss ion occur ring during

high—workload conditions and because of the limited increase possible for

Workload I--at most 19%.

The differences found between workloads might be attributed to differences in the individual target difficulties , although it is assumed

that target difficulty was randomly distributed over workload conditions .

However , the increases in performance between pretes t and posttes t could

not have occurred because of target difficulty differences , because the

same targets appeared equally of ten in the pretes t and posttest.

15
—--~~~--- ~~ —-——- — -‘ -- -----------

The nonsignif icant period x pretest/posttes t interaction indicates

that the relationship between the four periods (30-minute scenario segments) was similar for both the pretest and posttest. For both the pretest arid posttest , performance decreased as the periods progressed from

1 to 4 (see Table 6). This significant (see Table 4) decrease in performance over periods (or time) is probably due to fatigue , loss of

motivation , etc. It is important to note that an increase in performance (practice effect) did not occur. Thus, the difference from pretest to posttest is attributable to the training given and not to practice effects.

Table 6

Pretest/Posttest x Period Interaction for Detection Completeness

Period 1 Period 2 Period 3 Period 4

Pretest 66% 62% 60% 42%

PoSttest 88% 84% 74% 73%

The group ’s effect, which is confounded with workload order , is nonsignificant (Table 4). This indicates that the four subgroups of operators (each having a different workload order) did not perform significantly differently from one another. ]

False Alarms

The number of false alarms reported by operators was small , only

six i

~ the- pretest and 12 in the posttest (see Table 7). The increase

between the pretest and posttest was not significant (t 1.19, df = 15).

The number of false alarms was at a minimal level and was not affected

by training.

Target Direction Deviation

Target directions were scored as deviations (in degrees) above and - ]

belo .i school solutions based upon ground truth. One method of analyzing

this score is to calculate the arithmetic sum of the direction deviation

for each operator and divide it by the total number of targets for which

the operator reported a direction to obtain an average. The average for

the pretest was -6°, while that for the poettest was -9°. Using this

method , these figures appear to indicate that the operators were very

accurate , whereas in fact the opposite is true because of two conditions.

16
The first was the atypical target paths used: The targets turned 70°

after passing through the grid and traveled parallel to the last row of

sensors. The second was that half the target activation patterns were

generated by taking mirror images of the other half (e.g., if a target

traveled down one side of the grid and then turned 70° to the right,

its mirror image would travel down the other side and turn 70° to the

left) . In general , the operators missed the turning of the targets .

Thus, for one-half of the target activation patterns , the average

deviation for both pretest and posttest was -46°. For the mirror image

of these patterns, the average deviation was +35°. In summing these,

an average of — 8.5° results , which does not reflect the actual accuracy

of estimates of direction.

Table 7

Pretest/Posttest False Alarms

Operator Pretest Posttest

a 2 2

b 1 0

c 1 0

d 1 0

e 1 3

f 0 3

g 0 3

h 0 1

Total 6 12

Based on the above considerations, a different method of scoring

was used. The analysis was conducted using the absolute value of the

direction deviation , that is, ignoring the sign of the deviation. This

method cancels the effect of the second condition above (mirror image

target paths).

Because data were missing in 12 cells in the design matrix , the

planned analysis of variance (see research design section) was not conducted. The missing data occurred when a particular operator did not

report any target detections within a 30-minute segment or did not give

a direction for a reported target. By combining Workloads I and II into

a low—target-activity condition and III and IV into a high-target-activity

condition , an analysis could be performed using a 2 (groups) x 2 (activity) x 2 (pre— 9osttest) design .

17

___i_i__ ____ - -

~~~~~ ---~~
- -

Although training improved target detection , there was a loss of

intelligence information associated with training and these variables.

Direction information was omitted on 19% of the targets in the post~ est

and on only 3% in the pretest. This caused too many missing cells and

prevented the planned analysis. However , training still resulted in

more targets being reported , both with and without complete information

on direction estimation.

Table 8 presents the analysis of variance for the absolute value

of the operators ’ direction deviations using the modified workload variable of low and high target activity (6-7 targets/hr and 20-22 targets/

hr , respectively) . The only significant difference found was between

pre-est and posttest . The training improved direction estimation by

20%, or from 56° to 45°. Thus the training used not only increased the

detection of targets as indicated in the previous section , but also

improved the estimates of the direction of target movement.

Table 8

Analysis of Var iance for Direction Deviations (Absolute )

Source df SS MS F Significance

Between subjects 15 5,279

Groups (A) 1 150 150 <1 NS

Subjects w. groups

[error (a)] 14 5,129 366

Within subjects 48 14 , 473

Pretest/posttest (B) 1 2,025 2,025 5.76 .05

AxB 1 105 105 <1 NS

B x subjects w. groups

[error (b )] 14 4,923 352

Activity , low/high (C) 1 689 689 2.86 NS

AxC 1 100 100 ‘~

l NS

C x subjects w. groups

[error (c)] 14 3,374 241

BC 1 333 333 1.62 NS

ABC 1 42 42 <1 NS

BC x subjects w. groups

[error (bc)J 14 2,882 206

- — 

18
- - - - --- ~~~~~~~~~~~~~~~~~

The usefulness for intelligence purposes of the operator ’ s report

on target direction as estimated in this experiment is difficult to determine because of the atypical target path used. The relatively useless value of 45° reported above is based largely on targets which’

turned 70° after passing through the grid. However , six straight target paths were available on which 21 targets were reported. Using these

sparse data , the operator’s average direction deviation was ±26°. This

accuracy corresponds roughly with the accuracy of a report using such

terms as north, northeast, east, southeast, etc.

No practice effect was indicated by the average direction deviations by period. Analysis of Table 9 does not suggest any systematic

decrease in average deviation over time (over periods) for either the

pretest or posttest.

Table 9

Means of Absolute Direction Deviations by Periods

F retest periods Posttest periods

1 2 3 4 1 2 3 4

Average

deviation 61° 52° 52 ° 62° 45° 46 ° 46° 45 °

n 16 14 16 12 16 15 15 13

Target Speed Deviation

Target speed was scored as deviations (in meters/minute or mpm)

above and below ground truth speeds . As with direction deviation , 12

cells with missing data precluded the planned analysis of variance .

The data were tabulated to conduct a matched group t test. An average

deviation was computed for each operator on the pretest and posttest.

The average deviation was computed by dividing the arithmetic sum of

the deviations by the total number of target speeds reported by the

operator.

The average arithmetic pretest deviation is -82 meters per minute

(mpm) (3.1 mph) , and the average posttest deviation is -67 mpm (2.4 mph) .

The difference of .7 mph represents an 18% improvement over the pretest

deviation error and is statistically significant at the .05 level (t =

1.80, df = 15). Based on these results , it is concluded that the training reduced the size of the average deviation.

19
-- -:

Practice effects (learning) were minimal or nonexistent during the

administration of the pretest and posttest. The pretest average deviations for the four periods (in mpm) are 59 , 70, 121, and 98. The

posttest average deviations for the four periods (in mpm) are 70, 64,

85, and 67.

As these results show , operators tend to underestimate the ground

truth speeds. This was noted in previous research and was attributed

to the problem of underestimating the ac tual distance that a target

traveled through the grid (Edwards et al., 1977; Pilette et al., 1978;

and Edwards et al., 1978). When drawing a target ’s path through the

grid , it is normal to draw a smoother and straighter path than the one

that it actually traveled. Sensor patterns do not usually indicate if

vehicles turn to avoid hills, trees , etc. The operators ’ estimates of

speed may be more useful than the true target speed , since they are

estimates of how fast that target will get from one point to another

using cross-country (not straight) traveling procedures. Generally ,

this is the information the commander needs to know . However, if true

speeds are required (e.g., to be used to differentiate between vehicles

and personnel) , then a correction factor would need to be added to the

operators ’ estimates.

CONCLUSIONS AND RECOMMENDATIONS

Conclusions

Overall , the operator ’s detection completeness significantly increased from a pretest average of 57.5% of the targets detected to a

posttest average of 79.4% of the targets detected. This represents a

38% improvement over pretest performance . The self-paced training material with minimal instructor participation is considered successful.

Workloads I , III, and IV posttest results indicate significant

improvements in detection completeness. Workload I (5 targets/hr , 30—

pen) improved by 20%, Workload III (15 targets/hr , 30-pen) improved by

136%, and Workload IV ( 27 targets/hr , 60-pen) improved by 79%. The

substantial improvements for Workloads III and IV represent success in

training target cluster analysis for high—target—activity situations.

Workload II (8 targets/hr, 60-pen display) did not result in a

significant pretest—posttest improvement in detection completeness.

Performance was relatively high for both the pretest and posttest (88%

and 94%)

The false alarm rate was negligible with or without the training.

While this is contrary to the reports on the uses of the sensors in

Vietnam , newer sensors , better training, and different environments and

employment could easily account for this improvement.

20
- - - 

~~~~~~~~~~

- 

~~~~~~~~~~~~~~~~~~~~~~~~ ~~~

r ~~~~~~~~

The accuracy in target direction determination improved significantly as a result of the training. Due to the atypical target paths

used , a good estimate of the operators ’ ability to determine target

direction cannot be given. The best estimate of the operators ’ mean

performance , based on the results of only 21 reported targets , is ±26

from the true direction.

The average accurac~ in target speed estimations improved significantly as a result of the training, from a 3.1 mph underestimation to

a 2.4 mph underestimation . The underestimation is of the actual speed

of the target and not necessarily of the cross—country speed which takes

into account minor changes in direction to avoid obstacles. The operators ’ estimates could be more useful to the commander than the actual

speed.

Performance estimates of what the commander can expect of operators in the field interpreting activations of sensors employed in a

grid are shown in Table 10.

Table 10

Field DE tection Performance Expectations

Existinga Operator with 13 addiWorkload operator tional hours of training

S tgts/hr 81% (4.0 tgts) 97% (4.8 tgts)

8 tgts/hr 88% (7.0 tgts) 94% (7.5 tgts)

15 tgts/hr 28% (4.2 tgts) 66% (10.0 tgts)

27 tgts/hr 34% (9.2 tgts) 61% (16.5 tgts)

a

school_trained and with some basic introduction to the grid employment .

Recommendations

The training materials should be integrated into the UGS school

curriculum as part of training required for award of the MOS l7M.2 0 .

Unattended ground sensor personnel already trained and in the field

should be given special grid training. Training should be administered

periodically as a refresher course to maintain proficiency for operators

in the field.

21 

-
- 

~~~IIWL --~~~-_ —

— - - —~~~-- -.-~

,- -—- -— - — .-r-

~

-

During low—target—activity conditions , the grid employment pattern

of the type tested is recommended as a highly effective early warning

and target acquisition system (95% detection rate) . During high-activity

conditions , the early warning and target acquisition capability of the

grid is still recommended , but intelligence personne l should base their

estimates of threat on its effectiveness of 67% detection rate . The

problems of false alarms are negligible for this type of employment.

In terms of target acquisition information , the commander can expect more accurate target speed determinations as a result of the training , but the estimates will still be below the actual target speed . However , speed is used by the commanders ’ staff to estimate when a target

will reach a certain location . The operators ’ estimate is of crosscountry speed and may provide a more useful estimate in the sense of

how far the target will travel in a certain amount of time , assuming

that the terrain does not change. Actual speed can be estimated from

the cross—country speed estimate , for such purposes as target identification (e.g., personnel versus vehicles) .

Although training significantly improved direction estimation accuracy , and the results on the few “normal” target paths indicated that

the reports are sufficiently accurate , the data are insufficient for

any recommendation as to the usefulness of direction estimation using

grid employment. It is recommended that operator estimates of direction

using additional target activation data from a grid be obtained to determine the system ’s accuracy for direction estimation .

22

— ----- ---- - --
- -

=

~~~~

- ,-

~~~~~~

- .- -- - —=

~~

REFERENCES

Edwards , L., Rochford , D. & Shvern , U. Comparison of Four Unattended

Ground Sensor Displays. ARI Technical Paper 281, April 1977.

Pilette , S., Biggs , B., and Edwards , L. & Martinek , H. Optimum Patching Technique for Seismic Sensors Employed in a Grid. ARI Technical Paper 320, 1978.

Edwards , L., Pilette , S., Biggs , B. & Martinek , H. The Effects of Workload on Performance of Operators Monitoring Unattended Ground

Sensors. ARI Technical Paper 321, 1978.

Edwards , L., Pilette, S. & Nartinek , H. Error Analysis of Unattended

Ground Sensor Oper ators ’ Reports . ARI Research Memorandum 77-1 ,

November 1977.

Operator and Organization Maintenance Manual for Recorder , Signal Data

R0376/UGS Resdel Model No. 91662-1 , DA.ABO7-69-O195, June 1969.

Wi ner , B. Statistical Principles in Experimental Design. New York :

McGraw—Hill , 1962.

23

- - ___
::

~~~~~~~~~~~T~~~~1 - --

~~~~~~~Z

APPENDIX A

ORIENTA TION BRIE FING

Monitor: Paraphras e the followi ng :

I want to welcome everyone here today and thank your 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 worthwhile. You will be participating in a fi ve—day orogram and we will be spending the next several

hours briefing you and givi ng you an orientation as to what it is all

about. Before going any further I want to introduce myself and my associ ate; and fi nd out who you are.

- Introductions -

Our purpose in coini ng here is to evaluate , with your assistance , several di fferent displa y and target activi ty conditions using seismi c

sensors employed in a grid. We have been asked by the Arm y Research

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

Army to administer this exercise to you . The A rmy 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. Many of the skills you have acquired in school and on the

job will apply to these tasks , however , the patching technieue

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 duri ng 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 aski ng might prevent your having to repeat some of your work. We will

be using simulated RO-376 dri ve mechanisms . If any of the equipment appears

to be malfunctioning , info rm 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

app lyi ng 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 crosscountry and not along the existing road network . This type of sensor field

would be used to help detect and i denti fy different tactical maneuvers such

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

gated array , gri d and gri d employment.

25

:

~~~~~~~~~~~~~~~k
I

For our experiment , we have taped actual sensor acti vati ons from a

gri d employment during field exercises using various types of targets .

The target acti vati ons were collected under s imulated battlefield conditions complete wi th noise acti vations produced by artillery fi re, helicopters , and wi nd . These tapes will not be played back to you in real time , but

in the form of pre-prepared X-T p lots. You will interpret these X-T plots

and extract information us i ng our procedures and forms. Since we know where

and when target acti vations actually occurred , we can score your reports for

accuracy .

Each of you will participate three days this week . Your NCOIC will

post the schedule each day. During that time , you will be given training

on the row patching techniaue and multi —display training. Duri ng the program you will be given appropriate breaks , lunch , etc. You mu st be here

for all scheduled times or we cannot use your results .

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

good an ope rator you are. The purpose of this study is to determine what

are the effects of di fferent display and target acti vi ty conditi ons. We

are not interested 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 on these practica l exercises . Still , these activations are actual activations recorded in the field and

your accuracy in interpreffl~

g is one indicati on or example of what you can

do. You will be able to compare ~

Fiit 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

offi cer. 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 interpre t the X-T plots to the best of your

abilit y 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 realisti c as we could.

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

specialists that have graduated and will graduate from the IJGS school for a

long time to come. Army emp l oyment plans for UGS equipment and personnel

will be partl y infl uenced based upon what you can do .

Monitor : Begin the briefing on gri d empl oyment pattern .

26
1

~

RiS PAG’E IS BE~

ST QTJA.Ltry PRLCI’ICAJU Thou OOPY F1J2?J

~

IS}f~D To DDC ~~~~~~~~~~~~ 

- -

APPENDIX B

INTRODUCT IOU TO THE GRID DEPLOYMENT PATTERN

(LECTURE/DISCUSSION)

The Grid array consists of unattended ground sensors (UGS) depl oyed

in a matrix wi thin a designated field area as opposed to the string

pattern in which UGS are deployed in sequential alignment along a roadway . The gri d array can be used for area intrusion surveillance problems

encompassing entire border areas or smaller gate (gap) areas where coverage by radar or other m eans is lim i ted or not feasible. It is desi gned 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 deployed in a systematic way with pre-planned

distances between the sensors so that information extraction is enhanced.

For this exercise ~ie are utilizing a sensor grid , consisting of 9

sen sors , each sensor is hOO 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 tergets , track their path throug h the sensor grid , and provide further infoniation about them. You have already received

some information concern~ng this task. At this time we would like to

provide you with further information which should aid you in monitoring

sensors in the sensor griu .

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 acti vate all the sensors in order. However , in a

grid formation , the targ ts may come from any directio ’~ 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 SOHO examples of targets entering the sensor gri d

from different angles and taking different paths through the grid. We

also have copies of the sensor acti vations caused by these targets .

Examp le 1 — Here is a tai-get entering a gri d , and crossing the fi rst line

of sensors , I as sincj di rect ly over one of them.

1’ 2

~~

Activations would f r - ~t appea r on the middle sensor. As the target

proceeds the sensors to Ilie right and left woul d activate for a shorter

— 

27 

--
peri od of time . The sensors to the ri ght and left woul d cease activating

before the middle sensor thus the acti vations woul d appear as below :

1 2 3

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

4• 5• 6

~

appear like this on the X-T plot.

6

5

4

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

a stair— step pattern . All sensors wpuld activate for approximately the

same l ength of time .

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

t

~~~.

Here the left hand sensor woul d activate fi rst fol l owed by the middl e

sensor and the right -hand sensor , however , the middl e sensor woul d be

activated for a longer tirr ~ because the target woul d come closest to it.

- 9

8

7

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

gri d woul d, in the sanie way , indicate the path of the target as it traveled

the grid. . A good genera l ~

‘u1e to remember when moni toring a sensor grid

is to look at the over all ~

ittern of the sensors being activated , and then

make a deterni nation from tJ il~ overall pattern , where the target is trave li ng .

28
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

overal l pattern of activations

Example: 10 11 12 ~

l3~ 14~ 15 V

In this example , with sensors activating on the left and ri ght but

not in the midd le , it mist be assumed that two targets are present. In

these situations it is inportant to take note of sensors that are not

activating as well as sinsors that are activating.

13 14 15

10 11 12

29
APPENDIX C

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 out of this booklet and lay it on your desk where you

can see it clearly. As you can see, Figure 1 shows 9 sensors employed

in a grid which is 1000 meters on a side. The sensor identification

numbers are shown . In an operational situation , a grid this size could

be a small section out of a long sensor network or it could be placed

between natura l barriers . For our purposes , assume that each grid is

l ocated in a flat partially wooded field between natural tarriers such

as rugged terrain and marshy terrain.

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

you can see, target 1 has come from the expected direction and has

passed through the center of the sensor field. The target has passed

over sensors 11 , 15 , and 14.

The bottom half of Figure 1 shows blank spaces and a six step

procedure which you will use to report on targets.

YOUR JOB DURING THIS EXERCISE WILL BE TO FIND TARGETS

ON X-T PAPER AND FOR EACH ONE THAT YOU FIND , FILL IN

BLANKS FOR THE SIX STEPS.

31
- - - - - ~~~~~~~~~~~~~~~~~~~~~ -

~ —--- ~~~ -

irget 1

- 0— -._ < 1 ,

-

~

1 1 4

.zZ

(11 (7)

.zZ

9

“49 f)

5 10

b

-~~

II I I I 1 ’ 1 i 

I ‘

~1

0 00 200 300 400 600 700 800 900

MET ERS

V

STEP 1 STEP 2 STEP 3 STEP 4 STEP S STEP 6

TARGET DISTANCE CONFIDENCE DIRECTiON MID-POINT SPEED

NUMBER (METERS) 25% , 5O% (DEGREES) TIME ( mm ) (rn/mm )

__________ ___________ 

75% , 100% _______— TEE RENC’ ___________

_____ ,c

~

sp so% ~ f

~5~~ ,

~~~ J~~~~~13I

_ ii

-

Figure 1. Nine-sensor target log .

32

-~
-. ------- ~~~~~~~~~~~~ --------- -- 

~~~~~~~~~~~~~~~~~~ “— 

~~~~~~~~~~~ ~‘

- - 

- 

Sensor

Pen ____j p

~

- 2 2 ~~~ . 3 3 ;

~~~~~~~~~~~~~~- 4 4

— 5 5 .—

-

- - - 6 6 - 

- 7 7 -

- 8 8 — 9 9

- - 10 10

- 11 11

- - 

- - 13 13 - 

I 14 14

- - : ~

15 15

I - S 16 16 -I - 17 17 - 

- - Y -~ensor Gri qI 18 18 - t

~~g~t2 - 19 19 - - - 

- 

- - 20 20

2 4 6 8 10 12 4 1~~ 2i 22 YI 2~ 2~ ~

I) 21 21

_______ _______ - - -— _______ 

22 22

23 23

FIGURE 3

~~~rnindtes

I1

~

1

~~

-J

~~~~~

- - 9-S nsor Grid

T~jrget 1 - 2 4 6 0 10 12 I-I 10 ~Y 2 1 2 1

FIGURE 2

33
- — ---— ----- .- -

~~~~~ ~~~~~~~~~T1—’

~~

-

~~~~~~~

— - -

~~~

--- —-- --,---- --- 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ “

~

You will be working with X-T chart paper and targets similar to tha t

shown in Figure 2. Study target 1 and target 2 of Figure 2. Notice

the pen/ID chart at the side showing that the X-T ~ numbers correspond

to the same sensor ~~j4 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 tanks 50 meters apart travel i ng in a convoy formation would be one

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

different sections of the grid simu l taneously or at different times .

Because their activation patterns can be separated from one another , they

are classified as separate targets .

Step 1 on the Target Log - TA RGET NUMBER

In Figure 2 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 .

Step 2 on the Target Log - 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. The estimated

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

blanks.

34

——-~~

.,-
.

~~~

p 3 on the Target Log - CONFIDENCE

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:

1 00% - This means you are positive or certain.

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

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 shou ld

be recorded and checked out. You have low confidence that

this is a target.

A 50% confidence has already been placed in this column on the

Target Log .

St~p 4 on the Target Log - DIRECTION (Degrees)

For this step, record the direction in which the target is heading

after it leaves the grid area. The direction will be reported in degrees.

A protractor is provided for this purpose. Assume that the grid is

oriented exactly along east-west and north-south lines . The row lines

connecti ng the sensors, therefore , are oriented in an east-west direction .

To determine target direction , lay the protractor on the target

path , line up the east-west and north-south lines , and read the targets ’

direction in degrees from zero. As you can see, the direction has already

been determined and recorded in the Step 4 blank. Tha t direction is (fill in

the blank) ____________

Step 5 on the Target Log - MID-POINT TIME (mm ) DIFFERENCE

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

the first and last sensors . As shown , the first sensor is 11 and the

35
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. Now determine the time difference between the two mid-points.

This is done directly off the X-T chart paper as shown in Figure 3.

Remember , there are 2-minutes 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. If you missed it , reread this section and/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 tha t 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 col umn and row converge gives you the speed . In the case

of target 1 the speed is _______________ 

meters per minute. Check your

answer with the one already provided in the Target Log . If you missed

it , reread the instructions and/or see the Training Monitor.

Now you will receive practice on what you have just learned concerning

the SIX STEP target reporting procedure . Study target 2 presented on the

X-T plot in Figure 3. The sensor ID and pen number combinations are the

same as those for Fig ure 2.

36

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

—‘--- ---- —.--- .--.---- .

~~

- 

-- :‘

~~~~~T~~

-- -- ~

---

When you are finished , take your booklet to the Training Monitor.

If you feel you need to review the test procedure before wor king the

practice targets , do so If you have any questions , ask the Training

Monitor.

37
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~~~~~~~~~~~~~~~ ..,

~~~~~ ~~~~~~~~~~~~ 

- 

APP ENDIX 0

THE GRID DEPLOYMENT OF SEISMIC SENSORS USING ROW PATCH IUG

OBJECTIVE

To familiarize you with how the row patching technique is used with

a grid deployment pattern and to train you on how to use it to detect and

report on ta rgets using the -six-step procedure . Part I of this workbook

deals with training and Part II deals with practice in target reporting.

PART I - TRAINING

WHAT IS THE ROW PATCHING TECHNIQUE?

The row patching technique is defined as patching the sensors which

have been deployed in a symmetri cal grid pattern into horizontal rows on

the X-T plot. Look at the next page of this workbook. The top half of

Figure A presents a symetri cal 9-sensor gri d which shows the sensors

grouped into three horizontal rows : Row I, Row II , and Row III. All the

sensors are seismi c and are set at the same medium gain setting. The

bottom half of Figure A contains the six-step reporting procedure that

you are already familiar wi th . 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 corner and place it on your desk where it is clearly

visible.

Examine the 9-sensor gri d of Fi gure A more closely. Study again

which sensors have been assigned to the various rows . Fill in the

following bl anks as you cone to them .

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

Row II is composed of sensors 4, , and 6.

Row III is conposed of sensors —

, , and —.

HOt’! 00 YOU DETECT 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 observi ng what activation activity

39
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~~~~T ’”’~~ ‘ T -- - ~i 

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~~~ 

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1! ~ / J

\

~ - - - — - . - - —o / o

~ 4 5 6 /

MDUNTAIN OUS ‘ SWANP

AREA /

/

‘

I

/ —

Row III - — 

H— 500 meters f1 ~ ‘ 500 meters “

~~ 

I

I

1 N

T~~ TEP 1 S’rE P2 ;S T E P 3 STEP O 1 STEP 5 STEP 6

TARGET DIS1 ,\NLE Ct )N i-IDLN C G DIRECTION MID—POINT SPEED

NUMBER (METERS) 25~~. 5U (DEGRGE S )~ TIME ( mm ) (rn/m m )

I _____ _____

2100 1 2100 50% ; 180° 10 “T

~ ’ ~~ 1450

2 1 450 50% 130° 8 —r’ =181

I ;1050 3 1050 50% 1910 ________ 

‘

~~~~~~~ 

=1 50

Figure A. Target Log .

40

-- —

,,, 

-~~~ 

~~~~~~~~~~~~~~~
‘.‘ 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

is taking pl ace in each row . In other words , any tarqet enter i ng or leaving the gri d area will have to activate one or more 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 gri d are each patched to a pen of the sane n umber on this

X -T plot (one-for-one). Pen 1 on the X-T plot , therefore , refers to sensor

1 on the gri d , etc . Throug hout this lesson the terms “sensor” and “pen”

wil l be used interchangeably.

Find your job aid (UGS Ruler). One side of the ruler has a 24-pen

(sensor grid) scale and a 9-pen (sensor grid) scale as shown below .

4 5 6 9-SENSOR GR~O 9NIHO,LVd M0~~

0W~ dOSN]S-P~ ~~~~~ I Z O~j 61~

81 L1

~

91 01 6 8 L 9

fl_Pt . 1 -________ 

For this exercise you will be concerned only with the 9-pen scale. You

will notice that your 9-pen scale is broken down into three (3) colors .

Each of these colors represents a row of sensors wi thin a 9-sensor gri d.

BY PLACING THIS SCALE ON THE X-T PLOT YOU WILL

BE ABLE TO QUICKLY D ETERMINE WHICH SENSORS IN

EACH ROW HAVE ACTIVATED.

flow lay this scale on the X-T plot and line it up properly. Not i ce

how quickly you can tell which sensor in each row is activating . In

many situations , th is job aid may help you to: 1) detect a target,

2) determine where the target entered and left the gri d area and , 3) determine if more than one tar~et is wi thin the gri d area at the same time.

Go back to Fi gure A. Figure A shows the paths of three ta rgets which

we will analyze. As you can see these targets passed through or around

various rows and activated sensors in these rows . Place your job ai d on

the X-T plot for each of these targets and briefly note which pens of

each row have activated.

a . Exam ple 1 - Target 1

For target 1 , the pattern of activations provides good examples of

interpretat ion principles . All three sensors in Row I have activated in

41 

- - J
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ‘

~~~~‘‘ “~~~~

FIGURE B: X-T Plot for Row Patching

Using 9—Sensor Grid

(Learning Targets)

- 

- Sensor

H~1icopter 

-- 

Row Pen ID

- 

~p

4z:

~

:—i i 

~ 

1 —‘ 2 4 6 10 17 II 16 I I ~

’ - - - -- 2 2

A~tillery 

___ H 

II 4 4~

- 

~~ - III 7 7

3 

—

7 m m . _

~~~~~

_

= l 

- - 

p

Target 3 _________ ______ _________ -

8 m m . 1 7 9 11 13 15 17 19 21 23 2

~ 27 29

- z

Target 2

10 mm

Target 1 - 2 4 6 8 10 12 14 16 2-) ‘) ‘I -

42 

-- --- - - ~~1~~~ -—-- —----- -
—-•——- - ---.-‘---- -_

-,-_•- -‘------- --------~ 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~ _- ---- ‘—

~~~~~~~

—

a noticeable sta irstep pattern . This indicates that the target traveled

somewhere along Row I itself as though the sensors were deployed along a

road as in a trail/road monitoring situation . In this case the target

was traveling perpendicular to what we consider the primary watch di rection

of the gri d . Because sensor 5 (the internal sensor) did not activate ,

you should be able to conclude that the target probably did not penetrate

the grid , b ut merely traveled along the top as shown in the target 1 path

of Fi gure A. The last two sensors to acti vate (sensors 6 and 9) are the

last sensors on the right-hand side of Rows II and III respectively . !kgain ,

since only outer sensors activated , you would probably be right in concludi~~ that the target did not penetrate the inside of the gri d. Also ,

because of the regular stairstep pattern formed by sensors 1 , 2, and 3,

it can be concluded that the target passed these sensors one right after

the other at a relatively constant speed.

It is i mportant also to note that each sensor activated for about the

same peri od 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 , this condition implies that the target traveled

the same distance away from each sensor. If the activation lengths had

differed , this would imply that the target traveled closest to the sensor

with the longest activation pattern . We can say this because gain setting,

wh i ch is i mportant in determining detection range , is in the medi um ranae

for all the sensors . Keep ‘in mind , however, that other factors can also

infl uence detection range such as the seismi c response characteristi cs

of the ground , the environmental/weather factors , and the condition of

the equipment.

Review Peri od - Take a few minutes now and study target 1 and the row

patching technique . Start with the X-T plot and retrace the path of

target I oi the grid and try to visualize the relationships that we

have just discussed.

b. Example 2 - Tarqet 2

Turn your attention now to target 2. Firs t look at the X-T plot ,

then the grid. The activations are in wh i ch row(s)? _____ 

Sensor 1 o f

43

______
,1

Row I shows the fi rst activations. The sensor which shows the firs t

activations will usually tell you the closest point in the grid where

a target first made contact with the gri d by entering or going around

the grid. The word usually is used here because in the field another

sens or 

~~~ 

acti vate fi rst even thoug h it is further away from the

target because of detection range di fferences. Next , sensor 5 of Row

II activated and was followed by sensor 9 of Row III. This indicates

that the target moved out of Row II and into Row III.

YOU MUST USE JUDGMENT IN TRACING A TARGETS ’ PATH A ND BE ABLE TO USE CLUES FROM THE LENGTH

OF ACTIVATIONS .

It is important to undet’stand the concept of solitary targets versUs a cluster of targets travelin g through the grid. Try the following exercise

whi le still looking at the X-T plot. Imag ine that target 1 and target 2

are starting at the same time and progressing through the gri d 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 penci l or pen . Be careful as you fill in

the activations to reproduce the same time relationships of target 2.

Now look at the comb ined activations of both targets carefully.

:f you had just now seen these activations for the firs t time would you

be able to tell that two targets were involved? Would you have been

able to separate the one long activation pattern on pen 9 into two

targets? Remember , several targets can travel through the gri d at the

same time or close to the same time especially during a battle situation. If an intruder tried this tacti c do you think that you would be

able to distinguish and report on the separate targets?

Revi ew Period - Take a few minutes and study the combined activation

patterns in relation to the paths of these separate targets on the

grid.

c. Examp le 3 - Target 3

Look at target 3 on the X-T plot. Line up your UGS ruler on the

X-T plot di rectly under target 3. Using your IJGS ruler to help you

44

— —_

~

-

--—

~~~~~~

- _~
with your answer , whic h pens ha ve activated? _______________

. It i s

easy to see that sensors in all three rows have activated. Again it

would be safe to assume that the target probably passed through the

entire grid.

Noti ce the differences in the lengths of the various acti vation

patterns on the X-T plot. Generally, you can use this as a guide in

giving you an idea as to how cl ose the target came to the various

sensors . Compare the lengths of the activations on pens 3 and 8. The

acti vation length on pen 8 is about one minute longer than the one on

pen 3. Since the gain setting of all the sensors is the same ,

YOU CAN SAFELY CONCLUDE THAT THE TARGET PASSED

CLOSER TO THE SENSOR WITH THE LONGEST ACTIVATION

PATTERN .

In the example discussed , the target probably passed closer to

sensor _____ 

than it did to sensor 3. Look at the target path in

Figure A and check where the target did pass.

Sensors 5 and 6 act ivated and it wo ul d be a reasonable assumpt ion

that they are valid activations associated with target 3. Do you think

you can conclude the same about the four activations on pen 1? Probably

not. Chances are the acti vations on pen 1 are unrelated to target 3

and probably do not even involve a target. Sensor 1 may be starting

to mal-function and if it becomes a “talker ” it will run down its power

supply.

Look at the X-T plot and answer the following question for the

pairs of sensors listed below . The target passed closer to which sensor?

Target 3: Sensor 5 or Sensor 6? _____

Target 3: Sensor 3 or sensor 5? _____

Now superimpose target 2 which you studied previously onto target 3

so that they sta rt in the same time frame . Wi th your penci l or pen , fill

in the target 2 act ivations in the same manner that you did previously

wit h 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 fo r the fi rs t time , would you be able to tell that two

45
targets were invo l ved? Lay your job aid on the X-T plot. Does the

job aid help you in distinguishing these targets?

Review Period - Take a few minutes and stu dy the combi ned ac tivation

patterns in re lation to the paths of those separate targets as shown

on the grid.

d. Example 5 - Artillery and Helicopter Activity

Look in the upper portion of the X-T plot and you will see a

typical activation pattern fur arti llery and helicopter activity .

Could you have recognized them if they had not been annotated? An

artillery shellburs t usua lly shows as a thin band of one or two activations per sensor with the activations occurin g at the same time .

Hel i co pter acti vity is di fferent i n that there are group ings of two

or more activations per sensor occuring at the same time . There are

more acti vations because the helicopter is in the area longer and

therefore the disturbance created by the helicopter has an effect on j

the sensors for a longer period of time .

For th is exercise circ le and annotate on your X -T p lot but do

not report artillery or helicopter activity as targets . It is important for you to know what it looks like on an X-T plot so you do not

report this activity as targets . Study the examples shown .

HOW DO YOU ESTIMATE DISTANCE?

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 of

the six-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

l abeled “Distance in Meters” as shown below .

I I I I 1 I I

~~~ ~ I

100 200 300 400 500 800 700 800 900 1000 1100 1200 1300 1400 1500

DISTANCE IN METERS

UGS RULlI Iuini son

SIIONIM ~31fl~ SOn NI 3MII

1 2 3 4 5 6 1 8 9 10 11 12 13 14 15 16 11 18 19 20 21 22 23 24 25 26 21 28 29 30 1 L J J ~~i~,L~~L

46 - 

- -

‘ Ii;!

-- -
-- ~~-~ ---- 

~~~~~~~~~~ 

~~~~~~

. , -

~~~~~~

To use this scale , place it along a target path that you have 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 horizonta l 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 LJGS RULER.

- 

- Using your UGS ruler, measure the target paths drawn in Figure A

and check your estimate wi th the answers already orovided . If y

~~~ answers differ from the given answers by over 100 meters , consult the

Training Monitor.

HOW 00 YOU DETERMINE THE MID-POINT TIME DIFFEREN CE? ~

I_. As you learned previously, an estimate of target speed ~~~~~

can be made only by knowing the: 1) distance that the

target traveled through or around the grid and 2) the amount

of time that the target spent in the grid. Step 3 asks for

your ______________ as to whether you feel you in fact have —~ 2 —.

detected a real target. Step 4 requires you to record the 

direction in which the target is heading by reading the angle ~

from your protractor. See if you can get the same directions —

as in Fi gure A. ~ Step 5 requires you to find and mark (on the X-T plot) 

~ 

j 

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 mid - ~~~~~ &

points probably gives you the best estimate of how long the

target was in the grid then any other method. Any easy way to

estimate this midpoint time difference is to use a scale. -

Check the scale on your UGS ruler which is labeled

“Time in UGS Ruler Minutes ” . An example is shown on the —

right. The scale extends from 0 to 30 minutes and should ~

- ~

be adequate for measuring most activation patterns that

you will be working with. To use this scale si3lply measure —

p_. —

47 

~~ - -
__

the di s tance between the two mi dpoints as though it were a ruler and you

were measurin g inches. Read the time to the nearest ‘-

~ minute . This

answer woul d be recorded in the Step _____ 

blank.

Take a few minutes now and check the mi dpoints of the fi rst and last

sensors of targets 1 , 2, and 3 on the X-T plot. Measure the midpoint time

diffe rences wi th your UGS ruler and see how cl ose you come to the school

solutions provided in Figure A. You ,uay feel that using the ‘JGS ruler

for this measurement is not needed because the answers can be sight-rea d ,

but remember that these learning targets were intentionall y simplified

for training purposes and field-collected targets wi ll be more difficult.

Also , you will make fewer mistakes if you use the ruler. In the event

that your answers differ by over 1⁄2 minute f ’om the given answers , consul t

the Training Monitor. If your an swers di ffer by over _____ 

minutes from

the given answers consu lt the Training Monitor.

HOW DO YOU CALCU LATE SPEED?

Step 6 requires an estimated average speed concerning the type of

target whether vehicle or personnel . In order to save time and avoid

ari thmetic errors , you should use the Speed Table which you have already

been taugh t to use.

MON ITOR CHECK

Before you beg in Part II below take your materials to the monitor

and take a short break.

4

~

_ .

__ i - __~ — -
- — - ~~~~~~~-~~~- -

~~~

------

~~

PART II - PRACTICE TARGETS

Fi gure C presents an X-T plot of operationally collected targets

for you to practice on using the patching technique and principl es

that you have just learned. After you are finished reading this booklet ,

study this X-T pl ot for targets starting at the bottom and working upwards in the order you would see them on a field recorder. For each

target that you detect report on it in Fi gure D.

Fi gure D presents a blank 9-sensor gri d Target Log . Pul l it out

of your booklet , wri te your name in the upper right -hand corner and

place it in 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 . As you report on each target , remember to ci rcle all the acti vations

associated wi th that target by row, number all your circles , and fill

in the six -step procedure in the Figure 0 Target Log .

USE ALL THREE FUNCTIONS OF YOUR UGS RULER:

SENSOR ROW GROUPS , DISTANCE MEASUREMENT , AND TIME MEASUREMENT .

As you work through these operationally -collected practi ce targets ,

remember that they are not the sterile , ideal examples which you have

just worked with . THE PRACTICE TARGETS CONTAIN VARIOUS SOURCES OF BACKGROUND NOI SE AND THE EFFE CTS OF MALF UNCTIONI NG SENSORS AND VARIATI O NS IN

SENSOR DETECTION RANGE DUE TO~ ROUND/TERRAIN CONDITION S

AND WEATHER . To be able to do a good UGS reporting job , you must learn

how to detect and extract target info rmation from X-T plots collected in

the field.

Consult the Training Monito r when you feel the need. When you are

finished with your practice targets , take your work to the Trainin g

Monitor. He will determine whether you need additional practice and/or

review.

There is one additional point which must be made because it is an

important part of your response. Each time tha t you draw the path of a

target through the grid , at the end of the target path draw an arrow in the

direction that the target is traveling. Assume that your target direction

and speed information will be used by your CO for fire control purposes.

49

~~~~1 ~~~~~ ~~~~~~~ - ~~- - - - I
Figure C: X-T Plot for Row Patch i ng

Using the 9-Sensor Grid (Practice Targets)

3 5 7 9 U ~

3 15 1? 19 21 23 25 ~

7 29

2 4 6 8 10 12 14 16 18 20 22 24 26~ 28 ~

0

— ~~~~~~~~ -

= - 

- -‘ - -

—

- ~~~~‘- - ~~- ‘ - - - - — -

4 — -

13579 11 113 15 17 921 23 25 27 29

5Q —
FIGURE 0 UL’LNA11)1~_________________

Target Log (Practice Targets)

-

~

1---

\ WBITE

L~~

, ~~~ ~~~~ - - ---<>- -- - - - --- - - ~ o

MOUNTAINOUS - 

SWAMP

AREA —

—

~~~~~~~ ~~~ 

- ‘: 

‘

- 

-

~~~

-- . .- --- - --

~~~~~~

-

~~~~~~~~

--

~~~~

- --- - -----

~~~~~

STEP 1 STEP 2 STEP 3 STEP 4 STEP 5 STEP 6

TARGET DISTANCE CONFIDENCE - DIRECTION MID—POINT SPEED

NUMBER (METERS) 25%.50% (DEGREES) TIME (mm ) (ru/miri )

__________ __________ 75% , 100% DIFFERENCE __________

51 

-I_ __ _

hII~A - 

- 

-~ — -

~~~~~~ :~~~~ ~ 

— 
______

Fi gure E: X-T Plot for Row Patching

Using the 9-Sensor Gri d (School Solution )

1; 5 9 ,11 15 ~/ 

1

7 21 23 5 27 29 I-

- I

4 m m . 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

<~

ç

. Arti li~r~ 

-

— ‘. - - U m i .

- -

I - -

i I

~~I I,! -

—‘ 2 4 6 8 10 12 1 14 16 18 20 22 24 26 28 ~0

- -~~~~~~

-

~~~~

I 

-

- 

-

— - - -4f------~~ Heli opter

¶

~~~~~~~7m ~n 

1

_ ~~ 11 l

~ 

15 17 21 

_

~3 5 ~7 2~

52 
— — -

~~~~ - -

~~

---

~~~~

---

~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

FIGURE F U1’EL<AkUL (_________________

Taryet ~

‘j .. ,Target 6 (School Sol ution )

~ 

path

I - - --( s- - - -- - -o OAK

MOUNTAINOUS 

-

. 

I 

/ SWAMP

0) 

----- .

-

-

~~~~~ -~~~~~~~~~ ---~~~~~~ -- --~~~~

U 4

STEP 1 STEP 2~~~~~ STEP 3 - 

STEP 4 STEP S STEP 6

TARCET DISTANCE I 

CONFIDENCE - DIRECTION MID-POINT SPEED

NUMBER (METERS ) I 25% ,5O~ (DEGR1~

ES) TIME (mm ) (rn /m m )

_____ 

___ 

0 ___

4 1/50 I s-o% 

~~~~ 

I

5 / 9 ~

00 J5o

~~ p77° ___

6 _____ 

5O~~ J77 ’ II ___

1 ‘ J O~

g

~ - 13o° 44~ I _____

53

- - ----

~~

-- ------ --~~~~- -~~~~~~~~ - - -

.

--

,- 

~~~~~~---~-- ----- - -.- -
F” :

~~~~~ - -- -

APPENDIX E

PART IV MULTI-DISPLAY PROCEDURE TRAINING (LECTURE/DISCUSSION)

Monitor: Paraphrase the following:

You will be monitori ng one 30-pen and two 30-pen displays .

To familiarize you with the procedures needed to monito r one 30-pen and

two 30-pen displays using the six-step reporting procedure . Each person

will have an opportunity to interpret two targets at each display condition . Each 30-pen display contains three 9-sensor grids . The following diagram

clari fies the grid arrangement.

Monito r: Draw this picture on the blackboard and paraphrase the following .

Di splay A Display B Display C

Gri d 11 111 I II iii~~ 1 i i II

Pens 11-19 21-29 1-9 11-19 2l-29 J 1-9 11-19 21-29 —

As you can see in the diagram there are three 9-sensor grids in each display . For each display : - 

-

Grid 1 is composed of sensors 1-9 and uses the green Target Log .

Grid 2 is composed of sensors 11-19 and uses the yel l ow Target Log.

Grid 3 is composed of sensors 21-29 and uses the blue Target Log .

Take the target logs located on your desk and study them in relation to

the three grids . Notice how the grids are geographically related to each

other.

When you detect a target you will report on it using the six-step

procedure that you have al ready learned. However , there is one important

exception.

For each target that you report, you must include a display letter

(A, B , or C). At this time , check to see which display or displays you

are working wi th . For each target that you report, always include the

display letter in which the target is located.

55

I

~~~ECLI~ING PAUL bi.AMC NOT F1ui.~~
____ 

~~~~~~~~~~~~~~~~~~~~~~~~~~ —- — ---- - 

The place to report the display letter is with the target number in

Step 1. Suppose the fi fth target that you have detected is located in

Display A , Gri d 2. In Step 1 with the Target Number headi ng on the b lue

Target Log , you would wri te A-5 . Suppose that the tenth target that you

detect falls in Display B , Gri d 1 . For this example , in Step 1 under the

Target Number heading of the (green/yellow/blue ) Target Log you would

wri te B-i.

Monito r: Go through several more examples to be sure everyone unders tands .

Paraphrase the following:

Fi nd your job aid (UGS ruler) . Turn to the side that has the 9-pen

scale. You will be able to use this ruler effectively if you desire . The

ruler , as you have already learned , is divided into three sections. Each

section is a different color and represents a different row of sensors . This ruler can be used for each of the three grids that you will be working

wi th in each display as shown .

1 2 3 4 5 6 7 8 9 Grid l

11 12 13 14 15 16 17 18 19 Grid II

21 22 23 24 25 26 27 28 29 Gri d III

Of course , when you are working with Grid I , you will be working with

sensor 1-9. When you are working with Gri d II , simply add 10 to each

sensor number and use the ruler as you have learned. When you are working

with Gri d III simply add 20 to each sensor number and use the ruler as you

have learned.

Monito r: Be sure that pract ice ta rgets are in positon on all of the

disp lays for a practice session . Paraphrase the following:

You will now be given practice scenarios to work wi th . Detect and

report on two targets usi ng the procedure that you have learned. After

you have comp leted report ing on these pract ice targets , take your target

log sheet or sheets to the training monitor. He will check you work and

determine if you need more practice targets .

Af ter you ha ve comp leted the pract ice targets you wi ll be rotated to

a different display condition and you will report on two more targets .

Since under some di s p lay cond itions you will be monitoring more grids and

sensors there is a good chance that you will be detecting and reporting

on more targets . As you work through the practi ce targets , think about

how you would successfully handle heavy ta rget activity situations. In
____

these situations , your time would be at a premi um and you must know your

procedures wel l . For exam pl e, if you ha ve detected three targets

simultaneously you would want to share your time evenly with all three

rather than with just one of them. Time/task sharing is therefore

important.

Mon itor: Begin the practice phase to complete the fi rst cycle of display

training and practi ce . Rotate the students and complete cycl e

2 in the same manner.

~~~~~~ J
- --- .

--—- ---- ----- —----- ---- -

DISTRIBUTION

ARt Distribution List

4 OASD IM&RAj 2 HQUSACDEC , Ft Ord , ATTN: Library

2 HQDA IDAMI-CSZI 1 HOUSACOEC , Ft Ord , ATTN: ATEC — EX—E—Hum Factors

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1 HQDA (DAMA -AR ) 1 USAPACDC , Ft Benj amin Harri s on , ATTN: ATCP—HR

1 HODA IDAPE-HRE-POI 1 USA Comm—Elect Sch , Ft Monmouth , ATTN~ ATSN — EA

1 HODA SGRD -ID) 1 USAEC , Ft Monmou tfr , ATTN: AMSEL—CT —HDP

1 HQDA (DAMI-DOT-C) I USAEC , Ft Monm o ut h , ATTN : AMSEL— PA— P

1 HQDA (OAPC-PMZ-Al I USAEC, Ft Mon mouth , ATTN: AMSEL—S l—CB

I HQOA ‘ DACH-PPZ-A ) 1 USAEC , Ft Monmouth , ATTN: C, Fact Dcv Br

1 HODA )DA PE-HREj 1 USA Materials Sys Anal Agcy , Aberdeen , ATTN: AMXSY — P

1 HQDA (DAPE-MPO-CI 1 Edgew ood Arsenal , Aberdeen , ATTN: SAREA— BL—H

I HODA IDAPE-DWI 1 USA Ord Ctr & Sch , A berdeen , ATTN. ATSL—TEM—C

1 HODA IDAPE -HRL ) 2 USA Hum Engr Lab , Aberdeen , ATTN: Library/Dir

1 HODA IDAPE-CPSI 1 USA Combat Arms Tog Bd , Ft Benniny, ATTN: Ad Supervisor

1 HODA IDAFD -MFA) I USA Infantry Hum Rs ch Unit , Ft Becr rr ing, ATT N: Chief

1 HQDA IDARD-ARS -P) 1 USA Infantry Sd , Ft Benni rig, ATTN: STEBC—TE-- T

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1 HODA (DUSA-ORI 1 USA Air Def Sch , Ft Bliss , ATTN: AT SA—CTD- M E

1 HQDA IDAMO-RORI 1 USA Air Def Sch , Ft Bliss , ATTN: Tech Lib

1 HODA (DASG) 1 USA Air Oct Bd , Ft Bliss , ATTN: FILES

1 HODA IDA1O-PIl 1 USA Air Del Bd , Ft Bliss , ATT N: STEB O—PO

1 Chief , Consult Di~ (DA-OTSG). Ade lphi , MD 1 USA Cmd & General Stf College , Ft Leavenworth , ATTN: Lib

1 Md Asst , H um Ret , 000R&E , OAD (E&LS) 1 USA Cmd & General Stf College , Ft Leavenworth , ATTN: ATSW—SE—L

1 HQ USARAL , APO Seattle , ATTN: ARAGP .R I USA Cmd & General Stf College , Ft Leavenworth . ATTN: Ed Advisor

1 HO Firs t Ar my, ATTN: AFKA -O I-TI 1 USA Combined Arms Cmbt Dev Act , Ft Leavenworth , ATTN: DepCdr

2 110 Fifth Army, Ft Sam Houston 1 USA Combined Arms Cmbt Des Act . Ft Leavenworth , AT’TN: CCS

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1 Ofc Chief of Stf , Studier Ofc 1 USA Combined Arms Cmbt Dcv Act , Ft Leavenworth . ATTN: ATCACO—E

1 DCSPER , ATTN: CPS/OCP 1 USA Combined Arms Cmbt Des Act , Ft Leavenworth , ATTN: ATCACC —C I

1 The Army Lib , Pentagon , ATTN: RSB Chief 1 USAECOM , Nig ht Visio n Lab . Ft Belvoir , ATTN: AMSEL—NV—SD

1 The Army Lb . Pentagon , ATTN: ANRAL 3 USA Computer Sys Cmd , Ft Belvoir , ATTN : Tech Library

1 Ofc , Ass s Sect of the Army (R&D ) 1 LJSAMERDC , Ft Belvoir . ATTN: STSFB—DO

1 Tech Support Ofc , OJCS 1 USA Eng Sch , Ft Belvoir , ATTN: Library

1 USASA , Arlington , ATTN: IARD -T 1 USA Topographic Lab , Ft Belvoir , ATTN: ETL -TO—S

1 USA Rsch Ofc , Durham . ATTN: Life Sciences Dir I USA Topographic Lab , Ft Belvoir , A1TN: STINFO Center

2 USARIEM , Nati c lc , ATTN: SGRD-UE-CA 1 USA Topographic Lab , Ft Belvoir , ATTN: ETL—GSL

1 USATTC . Fr Clayton . ATTN: STETC-MO-A 1 USA Intelli gence Ct r & Sch , Ft Huachuca . ATTN : CTD—MS

1 USAIMA . Ft Brgeg . ATTN ATSU -CTD-OM 1 USA Inte lli gence Ctr & Sch , Ft Huachuca , ATT i

~: ATS—CTD—MS

1 SJSAIMA, Fr Bragg, ATTN: Marqu at Lib 1 USA Intelligence Ctr & Sch . Ft Huachuca , ATTN: ATS I—TE

1 US WAC Ct r & Sch . Ft McClellan , ATTN: Lib 1 USA Intelli gence C’r & Sch , Ft Huachuca , ATTN: ATS I—TEX—GS

1 US WAC Ctr & Sch , Ft McClellan , ATTN : Tng Dir 1 USA Intelligence Ctr & Sch . Ft Huachuca , ATTN: ATS I—CTS—OR

1 USA Quartermaster Sch , Ft Lee , ATTN: ATSM-TE 1 USA Intellig ence Ctr & Sch , Ft 1Iuachuca . ATTN : ATS I—CTD — DT

1 Intellig ence Material Dcv Oft , EWL . Ft Holabird 1 USA Intellig ence Ctr & Sch , Ft 1-luachuca , ATTN: ATSI—CTD—CS

I USA SE Signal Sch . Ft Gord on , ATTN : ATSO.EA I USA Intelli gence Ctr & Sch , Ft Huachuca , ATTN: DAS/SRD

1 USA Chaplain Ct r & Sch , Ft Hamilton . ATTN: ATSC-TE-RD I USA Intellig ence Ctr & Sch . Ft Huachuca , ATTN: ATSI—TEM

I ‘JSATSCH . Ft Eut ti s , ATTN : Educ Advi sor 1 USA Intelligence Ctr & Sch , Ft Huachuca , ATTN: Library

1 USA War College , Carl isle Barracks , ATTN: Lib 1 CDR . HO Ft Huachu ca , ATTN: Tech Ref Div

2 WRA I R , Neurops ychiatry Div 2 CDR , USA Electronic Prvg Grd , ATTN: STEEP—MT—S

I DLI , SDA , Monterey 1 CDR . Project MASSTER , ATTN : Tech Info Center

1 USA Concept Anal Agcy Bethesda , ATTN: MOCA.WGC 1 Hg MASSTER , USATRADOC , LNO

1 USA Con cept Anal Agcy, Beth esda , ATTN: MOCA-MR I Researc h Institute , HO MASSTER , Ft Hood

1 USA Concept Anal Agcy, Bethesda , ATTN: MOCA.JF 1 USA Rer.r uit ing Cmd , FtS h erdian , ATTN: USARCPM— P

I USA Artic Test Ctr . APO Seattle , ATTN: STEAC-MO-ASL 1 Senior Army Adv . , USAFAGOD/TAC , Elg in AF Au rs FId No. 9 - -

1 USA Ar t ic Test Ctr , APO Seattle , ATTN~ AMSTE-PL-T5 1 HO USARPAC , DCSPER , APO SF 96558 , ATTN: GPPE—SE

I USA Armame nt Cmd , Redstone Arsenal . ATTN: ATSK .TEM 1 St imso n Lub , Acade m~ of Health Sciences , Ft Sam Houston

1 USA Armamen t Cmd , Rock Islan d . ATTN : AMSAR .TDC 1 Marine Corps Inst . , ATTN: Dean—MCI

1 FAA NAFEC . At lantic City, ATTN~ Library I HOUSMC. Commandant . ATTN~ Code MTMT 51

1 FAA - NAFEC , Atlantic City. ATTN. Hum Engr Br 1 HOUSMC . Commandant . ATTI’J : Code MPI—20

1 FAA Aeronautical Ctr , Okl ahoma City, ATTN: AAC -44D 2 USCG Academy, New London . ATTN : Admission

2 USA Fld Arty Sch . Ft Sill , ATTN: Library 2 USCG Academy, New London , ATTN: Library

I USA Armor Sch , Ft Knox , ATTN: Library 1 USCG Training Ctr . NY . ATTN~ CO

I USA Armor Scfr , Ft (flo e , ATTN: ATSB -O l-E I USCG Training Ctr , NY , ATTN: Educ Svc Ofc

1 USA Armor Sch , Ft Knox . ATTN : ATS B-DT -T P 1 USCG, Psychol Ret Br . DC , ATTN: GP 1/62

I USA Armor Sch Ft Knox , ATTN: ATS B-CD-A D I HO Mid—Rang e Br , MC Oct . Ouantico , ATTN: P&S Div

59

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1 US Manor Cor ps Li aisiori Ofc , AMC . Alexandria , ATTN: AMCGS—F 1 Dat & Civ il Inst of Envuro Medicrne . Canada

I USATRA DO C, Ft Monro e , ATTN: ATRO--ED 1 AIR CRESS . Kensington. ATTN: Info Sys Be

6 USATRADOC . Ft Monro e . ATTN: AT PR-AO 1 Milisaerpsykofogi* Tjensate, Cco.hagen

1 IJSAT RADOC , Ft Monroe , ATTN : ATTS—EA 1 Military Att ache , French Embassy, ATTN: Doe Sec

1 USA Fo rces Cmd , Ft McPherson , ATTN: Library 1 Medecin Chef , C.E.R .P.A.—Ars enal . Toulon /Navat France

2 USA Aviation Test Sd, Ft Rucker , ATTN: STEBG—PO 1 Prin Scientific Off. Ap~ I Hiatt ~~~ Rscls Due . Miruusery

I USA Agisy foi ‘i,v rat ion Safet y, Ft Rucker , ATTN: Library of Defense , New Delhi

1 USA Age’.’ for Av iati on Safety, Ft Rucker , ATTN: Educ Advisor I Pars Rsch Ofc Library, AKA , Israel Defense Forces

1 USA Av iation Sch , F’ Rucker , ATTN: PC Drawer o 1 Ministe ris van Defensie , DOOP/KL Afd Sociaal

I HOUSA Aviation Sys Cmd , St Louis , ATTN: AMSAV—ZDR Psyth olog ische Zaken, The Hague , Netherlands

2 USA Aviation 9,/a Tett Act., Edwards AFB , ATTN: SAVTE—T

1 USA Air Def Sob , Ft Bliss , ATTN: ATSA TEM

1 USA Air Mobility Rsch & Des Lab . Moffett Fid , ATTN: SAVDL—A S

1 USA Av iation 5th , Res Trig Mgt , Ft Rucker . ATTN: ATST—T—RTM

1 USA Aviation Sch , CO. Ft Rucker , ATTN: ATST—D—A

1 HO , DARCOM , Alexandria , ATTN: AMXC D—TL

1 1-0, DARCOM , Al exandria , ATTN: CUR

I US Mili tary Academy, West Point , ATTN: Serials Unit

1 US Military Academy, West Point , .ATTN: Ofc of Milt Ldrshp

I US Military Acaden sv, West Point , ATTN: MAOR

1 USA Standardization Gp, UK , FF0 NY , AITH: MASE—GC

1 Oft of Naval Rsch , Arlington , ATTN : Code 452

3 Ofc of Nasal Rsch . Arl ington . ATTN : Code 458

1 Ofc of Naval Rsch, Arlington , ATTN: Code 450

1 Of c of Navel Rs ch, A rl: ngt on . ATTN: Code 441

1 Naval Aero spc Med Rex Lab , Pensacola . ATTN: Acou s Sch Div

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.jval Aern spc Me-i Rev Lab , Pensacola , ATTI4: Code 151

1 Naval Aerorpc Med P’s Lab , Pensacola , ATTN: Code L5

1 Chief nf N~-aPer~, ATTN : Pert~

OA

1 NAV’ ,IRSTA . Nio folk . ATTM : Safety Ctr

1 Nyu Oceaii o ir .

~~ iic . DC, ATTN: Code 6251 , Charts & Tech

1 Cc-n- te n of I-i aani Asial. .11TH: Dot Ctr

1 / isr vA ir SysCssrr r , ATT N. A I R— 5313C

I Nay BuMed . Ai’TN: 713

1 Navfuel icop terSvi bSoua 2, FF0 SF 96601

1 AFHRL (FTI Wi lliam AFB

1 A FI

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1 AFI-IR L (AS) WPAFB , OH

2 AFHPL (DQJZ i Brooks AFB

1 AFHRL iDOJN) Lackland AFB

I HOUSAI

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1 HQUS, ’cF (DPXXA )

I AFVTG IRD( Randolph AFB

3 AMRL (IIE( WPAFB:O l-1

2 AF Inst ‘

.

~f Tech , WPAF B , OH, ATTN: ENE/S L

1 ATC (X I’i L )) Rarril oip h AFO

1 JSAF A rrr sSi:ri Lii: , Broo k t-AFB (SUL—4), ATTN: DOC SEC

I AFOSR INL1 . Arlington

1 AF Log Cmd , McClellan AFB , ATTN: ALC/ DPCRB

1 An Force Academ y , CO. ATTN: Dept ot Bel Scn

5 NaxPers & Dcv Ctr , San Diego

2 Navy Med Meunosus yt’.hiatric Rsth Unit , Sat’s Diego

1 Nay Electron ic Lab , San Diego , ATTN: Ret Lab

I Nay Irri t( ’ en , San Diem . AITH: Code 9000— Lib

I Nav Post GraSch . Montere y . ATT N: Code 55Aa

1 NavP c .stGr aSc r , Montere y, ATT N: Code 2124

I NavTrn gE q~ ip Ctr . O:iando . ATTN: Tech Lib

1 US Dept of Labor , DC, AT”N: Macpower Adr ei n

1 US Dept ~f Just icC , DC, ATTN . Drug Enforce Admin

I Nat Rur of Standards , DC, ATTN: Computer Irfo Section

I Nat Clearing Nous,’ for FIH—Info , Rock eitl e

1 Deirv er Federal Ctr . Lakeuv ood . ATTN: ELM

12 Defens e Docu mtrnt,atior , Cente r

4 Dir Psyth , Army Hg, Russ ell Ofcx , Canberra

I Scientific Arlvs r , Mil Bd , ‘

Army Hg, Russell Ofcs , Canberra

1 Mit and Air Attache, Austrian Embassy

1 Cr~’ t nc -’ ‘fe R~chet che Des Fatte urs . Humairr e da ta Detente

Natr onale . Brussels

2 Canadian .Ioint Staff Washin gton

I C/Air Staff , Royal Canadian AF , ATTN: Pers Std Anal Br

3 Chief , Canadian Del Rscl’ Staff . ATTN: C/CRDS)W)

4 British Del Staff . British Embassy, Washin gton