IMPRINT Analysis of an Unmanned Air System 

Geospatial Information Process 

by Bruce P. Hunn, Kristin M. Schweitzer, John A. Cahir, and Mary M. Finch 

ARL-TR-4513 July 2008 

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Army Research Laboratory 

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ARL-TR-4513 July 2008 

IMPRINT Analysis of an Unmanned Air System 

Geospatial Information Process 

Bruce P. Hunn and Kristin M. Schweitzer 

Human Research and Engineering Directorate, ARL 

John A. Cahir and Mary M. Finch 

Northrop Grumman 

Approved for public release; distribution is unlimited. 
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IMPRINT Analysis of an Unmanned Air System Geospatial Information Process 

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14. ABSTRACT 

This study evaluated the streaming video analysis portion of the geospatial intelligence process associated with an unmanned 

aircraft system, which provides information to a four-person, military intelligence, geospatial analysis cell. The Improved 

Performance Research Integration Tool (IMPRINT) modeling program was used to understand this process and to assess crew 

workload during several test scenarios. Based on the use of IMPRINT, recommendations are made regarding the level of 

staffing for this type of system, based on crew workload characteristics discovered. This initial model was the first segment of 

a more comprehensive model to be developed to look at full mission conditions for a 12-hour shift.

15. SUBJECT TERMS

ERMP, geospatial, IMPRINT, manpower, workload 

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Bruce P. Hunn 

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iii

Contents 

List of Figures v

Acknowledgments vi

1. Introduction 1

2. Background 1

2.1 Challenges .......................................................................................................................2 

2.2 Purpose ............................................................................................................................2 

2.3 Impact..............................................................................................................................3 

3. Method 3

3.1 Participants ......................................................................................................................4 

3.2 Workload.........................................................................................................................5 

3.3 Scenario...........................................................................................................................5 

3.4 Assumptions ....................................................................................................................5 

4. Results 6

4.1 Crew Member 1: Primary Imagery Analyst...................................................................6 

4.2 Crew Member 2: Production Analyst.............................................................................7 

4.3 Crew Member 3: Production Analyst 2..........................................................................8 

4.4 Crew Member 4: Non-Commissioned Officer in Charge (NCOIC) ..............................9 

5. Discussion 10

6. Conclusions and Recommendations 11

7. References 12

Appendix A. 96D Questionnaire Explanation 13

Appendix B. Scenario 17
iv

Appendix C. IMPRINT Data – Task Flows by Crew Member 23

List of Symbols, Abbreviations, and Acronyms 31

Distribution List 32 
v

List of Figures 

Figure 1. Primary Imagery Analyst workload. ...............................................................................7 

Figure 2. Production Analyst 1 workload.......................................................................................8 

Figure 3. Production Analyst 2 workload.......................................................................................9 

Figure 4. NCOIC workload...........................................................................................................10 

Figure 5. Overall workload for all four crew members. ...............................................................11 

Figure C-1. Crew member 1 – Primary Imagery Analyst.............................................................23 

Figure C-2. Crew member 2 – Production Analyst 1. ..................................................................23 

Figure C-3. Crew member 3 – Writer...........................................................................................24 

Figure C-4. Crew member 4 – NCOIC.........................................................................................24 
vi

Acknowledgments 

The authors wish to acknowledge Woodson Tucker III for his contribution to the IMPRINT 

model and Stuart E. Deakin for creating the scenario. 
1

1. Introduction 

“Geospatial intelligence (GEOINT) is intelligence derived from the exploitation and analysis of 

imagery with geospatial information to describe, access, and visually depict physical features and 

geographically referenced activities in the operational environment. GEOINT consists of 

imagery, imagery intelligence (IMINT) and geospatial information” (Department of the Army, 

draft). 

A technical definition of geospatial information consists of, “geodetic,*

 geomagnetic, imagery, 

gravimetric,† aeronautical, topographic, hydrographic, littoral,‡ cultural, and toponymic§ data that 

are accurately referenced to a precise location on the surface of the earth” (Kabinier, 2001). 

Note: all definitions were taken from http://dictionary.reference.com on 23 and 24 January 2008. 

For the purposes of this report, the focus is on creating a model of the military intelligence (MI) 

image analyst, when s/he is tasked with using geospatial imagery information as provided by an 

extended range multipurpose (ERMP) type of unmanned aircraft system (UAS). The role of this 

imagery analyst is to create a product that MI organizations can present to commanders in order 

that they may better understand the implications that GEOINT has on the battlefield. In short, 

MI transforms geospatial information into data with context and meaning: intelligence. Overall, 

this process draws from sources such as governmental or non-governmental agencies, UAS, or 

Soldiers in the field to create a product that may contain a written report, presentation, imagery, 

or all these things combined. On the whole, GEOINT is the combination of the engineer-based 

geospatial information and the MI-based intelligence products, but that modeling effort is 

planned to follow this preliminary report on one element of that complex task. 

2. Background 

Geospatial information is normally gathered through ground-based means such as surveys and 

through remote means such as aircraft or satellite images. Technical enhancements have enabled 

greater accuracy with lower variability and provided a much larger perspective of the world than 

has ever been available before. UASs provide multiple image feeds (e.g., electro-optical, 

infrared), each providing a valuable and unique perspective but each requiring analysis in order 

 *

 Of or relating to or determined by geodesy, which is the scientific study of the size and shape of the earth, its field of 

gravity, and such varying phenomena as the motion of the magnetic poles and the tides. 

†

 Of or relating to measurement by weight or variations in a gravitational field. 

‡

 Of or pertaining to the shore of a lake, sea, or ocean; the region or zone between the limits of high and low tides. 

§

 Of or pertaining to a name derived from a place or region. 
2

to create an intelligence product. Manned aircraft and satellites capture images and information 

with great precision but also require analysis in order to provide useful intelligence. 

However, the continuous and rapid evolution of technology is creating an increasing gap 

between equipment output and the military's ability to provide sufficient analytical capability, 

including appropriate personnel and training. Personnel and training shortfalls in the geospatial 

and GEOINT areas are currently being addressed through a restructuring of several MI military 

occupational specialties (MOSs) to ensure that personnel obtain enough knowledge and 

familiarization with the critical tasks to successfully perform their job. However, it is critical 

that the proposed staffing and training levels match the workload level that new technology will 

impose. The manpower-requirement issue for geospatial analysis requires consideration of the 

number of video feeds, size and complexity of the area of interest, detail required by 

commanders, operational tempo, and a slew of other variables that are a function of the situation. 

2.1 Challenges 

Information gathering and the intelligence-analysis process function hand in hand but require 

distinct skill sets. Separate from information gathering, the intelligence, analysis process has 

little to do with staffing equipment in the traditional sense. It has everything to do with 

recognizing items of interest, establishing connections, knowing where best to look for useful 

information, and communicating the results in a format that is understandable to a highly diverse 

audience with respect to background and experience. How long does it take to find a useful 

image or to identify a feature near a building? It depends upon what is being sought, the 

neighborhood of the building, or the quality of the image. How many tasks can an analyst 

perform at the same time and for how long until she/he begins to miss pieces of important 

information? It depends upon the person’s cognitive ability, physical environment (including the 

system displays), and training. To return to the initial question, how does one determine 

manpower requirements for a cognitively intense job? 

One way to determine manpower requirements is to set up a high-fidelity mock mission and 

experiment with how people with various experience, training, and cognitive abilities perform 

different tasks. While perhaps the more traditional way to determine manpower, it is expensive, 

time consuming, and resource and personnel intensive and may be limited by participant availability, the numbers and kinds of missions and conditions that can be run, etc. An alternative is 

to model the process. 

2.2 Purpose 

This report addresses the results from an improved performance research integration tool 

(IMPRINT) analysis of the manpower requirements for a narrowly defined GEOINT scenario. 

The scenario uses an ERMP type UAS in a task force (TF) observe, detect, identify, neutralize 

(ODIN) mission to illustrate the tasks of integrating geospatial information from the UAS 

streaming video portion of the GEOINT mission. To accomplish this analysis, the IMPRINT 
3

program was used to assess the image analyst’s workload levels for several scenarios. The 

IMPRINT program uses stochastic task-network modeling to predict human cognitive workload 

(IMPRINT, 2007). It can easily analyze alternate crew-system function-task allocation schemes 

and run repeated missions in order to examine variations in task performance, accuracy, and 

multitasking. 

The objective of this analysis and report are to establish staffing levels congruent with the image 

analyst’s tasking, while assuring that whatever those staffing levels are, the crew of this image 

analysis cell is not overloaded or underloaded in their work. The proper level of workload for 

individual team members as well as the team overall is where neither any individual nor the team 

is subject to extreme levels of overload nor extreme underload but can operate at a fully engaged, 

productive, moderate workload level. IMPRINT modeling can assess the workload of 

individuals as well as the team and project a proper balance, based on the input of subject matter 

experts (SMEs), as well as operators experienced in image-analysis positions. The proper level 

of staffing is critical for the effective performance of the cognitive type tasks that image analysts 

perform, and IMPRINT was created to evaluate this type of work scenario. The goal of this 

study would be to make staffing recommendations for imagery analysts, which best suit the goal 

of accomplishing the GEOINT type of mission for a single, continual-feed UAS, independent of 

echelon and platform. 

2.3 Impact 

Modeling the imagery-analysis process is a cost-efficient way to estimate the manpower 

requirements to perform a cognitively intensive mission. The results of the IMPRINT 

assessment will allow commanders to see where their people will potentially have trouble 

performing their assigned tasks and, as a result of that trouble, what types of information might 

be neglected. 

3. Method 

Tasking guidance was to model personnel from MOS 35G (Imagery Analyst,* formerly MOS 

96D). The critical task list for 35Gs (in use in July 2007) was obtained from the 305th MI 

Battalion and refined to list only the tasks relevant to a TF-ODIN scenario. The critical tasks 

were entered into IMPRINT as functions, and the performance measures of those critical tasks 

were entered as tasks. 

 * The Imagery Analyst is an enlisted Soldier who is primarily responsible for supervising and analyzing aerial and ground 

permanent record imagery developed by photographic and electronic means (http://www.goarmy.com/JobDetail.do?id=153, 23 

Jan 2008). 
4

Since the tasked mission of deriving geospatial information from ERMP* feeds is currently 

nonexistent, initial staffing numbers could not be based on existing teams. The initial model 

depicted four crew members at the suggestion of SMEs and instructors with the MI 35G Basic 

and Advanced Non-Commissioned Officers’ Courses (BNOC, ANOC) at Fort Huachuca, 

Arizona. The suggestion was based on real-world TF-ODIN missions in Iraq and Macedonia 

that were conducted with aerial vehicles other than ERMP. The first crew member’s task was 

imagery exploitation (level 1 only): to monitor the ERMP video feeds, take screen captures of 

items that required further investigation, and transfer them to the second crew member. The 

second crew member’s task was to investigate the captures that the first crew member collected, 

research and collect relevant information from other sources such as the National GeospatialIntelligence Agency (NGA), and transfer the annotated information to the third crew member. 

The third crew member’s task was to write reports, develop presentations, or prepare other 

imagery products that might be required for submission to the commander. The fourth crew 

member’s task was to supervise the first three crew members, substitute when needed, and 

review the products before they were delivered to command, as well as coordinate for Multi-Int 

information, and all source intelligence support, in addition to participating in required briefings 

or meetings. Most of the work was done in parallel since background mission information and 

general annotations could be done without specific video captures from the first crew member. 

However, specifics that were unique to each capture were done serially. 

3.1 Participants 

After the basic crew member tasks were determined, the authors developed a questionnaire (see 

appendix A for the questionnaire instructions and excerpts) that was given to instructors of the 

35G BNOC and the 35G ANOC and to students of the 35G BNOC and of the 350G (Imagery 

Intelligence Technician,†

 formerly MOS 350D) BNOC. All instructors were previous graduates 

of 35G ANOC. 

The instructors and students who completed questionnaires ranged in rank from E6 to E7 and 

CW1 to CW4. All Soldiers had at least one year of experience as an imagery analyst, and most 

had 35G or 350G experience from multiple deployments in theater. Soldiers’ experience ranged 

from the brigade level to the national level. The questionnaire asked for average expected times, 

accuracy, and visual, auditory, cognitive, and psychomotor (VACP) workloads for the tasks 

required in an analyst’s duties; 41 questionnaires were completed with task time data; 20 of the 

41 questionnaires included VACP data. 

 * ERMP is a multimission aircraft based on the Predator that will provide the U.S. Army with a long-endurance, persistent 

ISR (intelligence, surveillance, and reconnaissance) and tactical strike capability featuring a heavy-fuel engine for increased

supportability in the field (http://www.ga-asi.com/products/er-mp-uas.php, 24 Jan 2008). 

†

 The Imagery Intelligence Technician is a warrant officer who provides technical expertise and manages activities engaged in 

imagery analysis (http://www.usarec.army.mil/hq/warrant/prerequ/WO350G.html, 23 Jan 2008). 
5

3.2 Workload 

Overall workload (Ow) in IMPRINT was defined as Ow = V + A + C + P. Overload occurred 

when total visual workload was greater than 7 (V > 7), when total auditory workload was greater 

than 7 (A > 7), when total cognitive workload was greater than 7 (C > 7), or when overall 

workload was greater than 40 (Ow > 40) for any one crew member. While the addition of the 

four VACP domains numerically equals a workload level of 28, a modeling precedent from 

studies has developed the numeric value of 40 as a more representative value than simply adding 

the domain qualities serially. The values used to define overload are supported by limited 

research and precedent (Pomranky and Wojciechowski, 2007; Mitchell et al., 2003; Mitchell, 

2005). It is expected that a crew member with a workload of 30 is fully engaged. 

This approach of setting 40 as a workload overload point makes allowance for human variability 

of performance at high workload levels and the possibility of some parallel processing of the 

VACP values, even at high workload levels. This distribution of effort across domains is both 

intuitive and commonly recognized in cognitive tasking of all types (e.g., talking on the 

telephone while watching a TV screen) and recognizes that both actions may be accomplished at 

the same time, with some individual performance decrement for each task being present but the 

overall tasking being performed at a reasonable level of accuracy. It is very important to 

consider that the VACP scale as well as IMPRINT results are relative measures rather than 

absolute values and that relative comparisons are the purpose of the modeling effort and not 

establishing absolute workload levels. For example, the workload rating values are not ratio 

scale (i.e., engineering quality) data but are subjective and more useful for comparing one 

scenario with another or one crew member’s effort with another’s on a common scale metric. 

The IMPRINT output is therefore useful for determining order of magnitude types of effects 

rather than fine nuances in workload levels. 

3.3 Scenario 

The scenario was a 12-hr TF-ODIN scenario based on actual missions and operated only from 

the division level and below. The model was built with 2+-hr segments for each of the four crew 

members. This report details only the first 2+-hr segment of the mission since the modeling and 

analysis of the entire 12 hr are ongoing. See appendix B for the detailed scenario. 

3.4 Assumptions 

Certain assumptions were made to limit the number of variables and to focus the initial model. 

Those made for the model described in this report are as follows: 

• ERMP feed is from a single platform and 274 mbps of electro-optical/infrared (EO/IR), 

synthetic aperture radar (SAR), and moving target indicator (MTI). The analysts receive 

direct (live) feed from ERMP 24 hr per day, 7 days per week. 
6

• The pilot and the payload operator for the ERMP are not co-located with the analysts. The 

analysts will communicate with and direct the ERMP pilot and payload operator via voice 

through various communication channels. 

• Imagery analysis is required “on demand” and is performed by a 35G. Only level 1 

exploitation is performed. (Level 1 analysis consists of generic classification of military 

objects, e.g., truck vs. car vs. tank, as opposed to Dodge vs. Volkswagen vs. M-1A.) 

• Weather, equipment failure, and communications loss will not be a factor in this model. 

Equipment is capable of meeting all data storage requirements. 

• No automated fusion of imagery products occurs. 

• Injury, illness, absence from station, rotations, prolonged duration of duty, or personnel 

substitutions are not factored into this model. 

4. Results 

Total time for the first segment of the 12-hr model averaged 2 hr 45 min across 20 iterations. 

The variable of interest was workload, so time and accuracy data are only referenced when they 

clarify a point. The data excerpts are in appendix C. 

4.1 Crew Member 1: Primary Imagery Analyst 

The Primary Imagery Analyst’s duty was to monitor direct imagery feeds from ERMP and 

capture relevant snapshots of imagery and video for further analysis. Tasks included maintaining 

voice contact with the ERMP pilot and payload operator, directing the ERMP sensor 

employment, plotting coordinates on images and maps, and identifying objects or events in the 

imagery. 

The number of concurrently performed tasks ranged from none to four, with overall workload 

reaching overload when three or four tasks were required at the same time. Figure 1 illustrates 

the Primary Imagery Analyst’s workload and number of concurrent tasks for the first 2+-hr 

segment of the TF-ODIN mission. The Primary Imagery Analyst’s overall workload exceeded 

40 8% of the time in three distinct workload spikes. The first occurred at the start of the mission 

(Mission Time = 00:00:00 [hh:mm:ss]) and lasted about 2 min while the Primary Imagery 

Analyst was maintaining voice contact with the ERMP operators, exploiting full-motion (video) 

imagery, and conducting aerial route reconnaissance, all at the same time. 

The second overload in overall workload lasted about 6.5 min and consisted of two related spikes 

with a brief (~2-min) respite between them. During both spikes and the respite, the Primary 

Imagery Analyst was concurrently exploiting full-motion imagery and conducting aerial route 

reconnaissance. These two tasks by themselves did not cause overload, as demonstrated by the 
7

0

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140 160

Time (minutes)

Workload

Overload

Overall Workload

Number of Tasks

Overload Spike 1: 

Three Concurrent Tasks Required 

Overload Spike 2: 

Three Concurrent Tasks Required 

Overload Spike 3:

Three Concurrent Tasks Required 

Figure 1. Primary Imagery Analyst workload. 

respite. However, the additional task of directing the ERMP sensor employment induced 

overload for the first spike, and the addition of maintaining voice contact with the ERMP 

operators induced overload for the second spike. 

The third incident of overload occurred ~90 min into the mission and lasted about 4.5 min. 

Three combinations of tasks caused the overload, all of which included concurrently exploiting 

full-motion imagery and directing the ERMP sensor employment. The third concurrent task for 

each combination was identifying roadways on imagery, plotting coordinates on an image or 

map, and identifying unconventional acts on the imagery, respectively. 

4.2 Crew Member 2: Production Analyst 

The Production Analyst 1’s duty was the level 1 exploitation of imagery captures received from 

the Primary Imagery Analyst and to retrieve relevant information from other sources. Tasks 

included exploiting full-motion imagery, retrieving information from databases and other sources 

(governmental and nongovernmental), preparing imagery-derived products, and analyzing 

activities in support of various missions. 

The number of concurrently performed tasks ranged from none to four, with overall overload 

occurring when three or four tasks were required at the same time. Figure 2 illustrates the 

Production Analyst 1’s workload and number of concurrent tasks for the first 2+-hr segment of 

the TF-ODIN mission. The Production Analyst 1’s overall workload exceeded 40 ~55% of the 

time. At all times during the overload condition, the Production Analyst 1 was performing at 
8

0

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140 160

Time (minutes)

Workload

Overload

Overall Workload

Number of Tasks

Increased Overload Spike:

Four Concurrent Tasks Required 

Significant Workload Drop:

Two Concurrent Tasks Required 

Constant Overload: 

Three Concurrent Tasks Required 

Figure 2. Production Analyst 1 workload. 

least three of the following tasks concurrently: retrieving information from other sources, 

analyzing activity in support of mission, preparing imagery derived products, managing 

electronic maps, managing data files, identifying vehicle types on imagery, determining 

geospatial position data from imagery, determining the dimensions of an object on imagery, 

identifying man-made obstacles on imagery, and exploiting full-motion imagery. The workload 

spiked about 80 min into the mission when four tasks required the Production Analyst 1’s 

attention at the same time: analyzing activity in support of mission, identifying man-made 

obstacles on imagery, exploiting full-motion imagery, and preparing imagery-derived products. 

The spike ended with the completion of the fourth task, leaving the Production Analyst 1 

performing three concurrent tasks. Workload fell below 40 near 90 min into the mission after 

the third task was dropped, and only two tasks were required concurrently. 

4.3 Crew Member 3: Production Analyst 2 

The Production Analyst 2’s duty was to prepare products and reports for delivery to command. 

Tasks included responding to intelligence taskings, preparing overlays, translating information 

into military symbols, manipulating computer files, and managing electronic maps and data files. 

The number of concurrently performed tasks ranged from none to three, with overall overload 

occurring when two or three tasks were required at the same time. Figure 3 illustrates 

Production Analyst 2’s workload and number of concurrent tasks for the first 2+-hr segment of 

the TF-ODIN mission. Production Analyst 2’s overall workload exceeded 40 nearly 14% of the 
9

0

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140 160

Time (minutes)

Workload

Overload

Overall Workload

Number of Tasks

Constant Overload: 

Three Concurrent Tasks Required 

Figure 3. Production Analyst 2 workload. 

time. During overload, Production Analyst 2 was performing three of the following tasks in 

varying combinations concurrently: retrieving information from other sources, preparing 

situation overlays, managing data files, and manipulating computer files. 

4.4 Crew Member 4: Non-Commissioned Officer in Charge (NCOIC) 

The NCOIC’s duty was to respond to intelligence taskings, manage shift operations, advise 

command, and provide quality control on imagery-derived products derived by the team. The 

number of concurrently performed tasks ranged from none to four, with overall overload 

occurring when three or four tasks were required at the same time. Figure 4 illustrates the 

NCOIC’s workload and number of concurrent tasks for the first 2+-hr segment of the TF-ODIN 

mission. The NCOIC was overloaded 37% of the time. During the overloaded period, three to 

four of the following tasks were performed concurrently: manage shift operations, select 

appropriate sensors, analyze activity in support of the mission, determine the available GEOINT 

products, request information, retrieve information from other sources, respond to intelligence 

taskings, advise command, and perform quality control on products. 
10

Figure 4. NCOIC workload. 

5. Discussion 

Given the nature of the tasks involved with a 35G Imagery Analyst’s duty, the model indicates 

that as a general rule, no more than two tasks may be performed concurrently to maintain an 

acceptable overall workload level. For many of the 35G’s tasks, to maintain VACP workloads 

within acceptable levels generally requires only one task be performed at a time. 

The number of tasks indicated on the graphs can be somewhat deceiving. Within the model, 

breaks and lulls with no workload were added as spacers to enable the appropriate tasks to begin 

at the times required by the scenario. IMPRINT counted these breaks and lulls as tasks. For 

example, of four tasks only three may have workload values, meaning only three tasks are 

contributing to the overall workload. One should use caution in taking the number of tasks on the 

graph at face value. 

Overall workload, especially for the NCOIC, drops at predictable points, i.e., when one of the 

workload-intensive tasks ends (see figure 5). After the full 12-hr shift model is completed and 

the task integration is refined, no significant drop in overall or VACP channel workload is 

expected near the 2-hr mark. 

0

10

20

30

40

50

60

70

0 20 40 60 80 100 120 140 160

Time (minutes)

Workload

Overload

Overall Workload

Number of Tasks

Constant Overload: 

Four Concurrent Tasks

Significant Workload Drop:

Two Concurrent Tasks

Constant Overload:

Three Concurrent Tasks
11

0.0 10.2 17.0 35.1 44.5 48.6 58.4 65.3 80.7 87.1 116. 119. 143.

Production Analyst 1

NCOIC

Primary

Imagery

Analyst Production Analyst 2

0

10

20

30

40

50

60

70

Workload

Time (minutes)

Figure 5. Overall workload for all four crew members. 

6. Conclusions and Recommendations 

The model of the 2+-hr mission segment indicates that all four imagery analysts will experience 

overload with their overall workload at some point during the first part of their mission. While 

any values over 40 are considered overload for this model, the consistent values near 32 indicate 

significant workload on all four team members for large portions of the mission segment. Spikes 

in overall workload clearly show where multiple tasks are required of the analyst at the same 

time. 

The authors recommend continuation of the current model to account for the remaining 10 hr of 

the analysts’ shift. To this point, the model indicates that analysts will have a challenging time 

completing the required tasks well because of multitasking and mental processing capacity. 
12

7. References 

Department of the Army. Imagery Intelligence; FM 2-22.5; U.S. Army Intelligence Center: Fort 

Huachuca, AZ, draft. 

IMPRINT. http://www.dtic.mil/dticasd/ddsm/srch//ddsm0177.html (accessed 2007). 

Kabinier, D. L. Geospatial Information Requirements of the Objective Force; AD-A390-563; 

Strategy Research Project. Carlisle Barracks; U.S. Army War College, March 2001. 

Mitchell, D. K.; Samms, C. L.; Henthorn, T.; Wojciechowski, J. Q. Trade Study: A Two- Versus 

Three-Soldier Crew for the Mounted Combat System (MCS) and Other Future Combat 

System Platforms; ARL-TR-3026; U.S. Army Research Laboratory: Aberdeen Proving 

Ground, MD, September 2003. 

Mitchell, D. K. Soldier Workload Analysis of the Mounted Combat System (MCS) Platoon’s Use 

of Unmanned Assets; ARL-TR-3476; U.S. Army Research Laboratory: Aberdeen Proving 

Ground, MD, April 2005. 

Pomranky, R. A.; Wojciechowski, J. Q. Determination of Mental Workload During Operation 

of Multiple Unmanned Systems; ARL-TR-4309; U.S. Army Research Laboratory: Aberdeen 

Proving Ground, MD, November 2007. 
13

Appendix A. 96D Questionnaire Explanation 

You are being asked to fill out a lengthy questionnaire in order to assist us in answering a 

question asked by the HQ Department of the Army to MI, “How many 96D analysts are required 

to support the Extended Range Multi-Purpose (ERMP) UAV.” 

The purpose of this questionnaire is to provide data (times, accuracy, etc) for a human 

performance model which will provide information on how many 96Ds are required to perform 

imagery analysis of the data/information expected from ERMP. 

This questionnaire asks for a great deal of detail, which is required by the type of model (the 

IMPRINT model) being used to determine 96D requirements for the Army. 

The Improved Performance Research Integration Tool (IMPRINT) is a model developed by the 

Army Research Laboratory (ARL) Human Research and Engineering Directorate (HRED). 

IMPRINT is consists of a set of automated aids used to assist analysts in conducting human 

performance analyses. IMPRINT provides the means for estimating manpower, personnel, and 

training requirements for new systems (In this case ERMP) or processes. 

We are trying to: 

Project future manpower levels and personnel characteristics through a task-based analysis 

of critical tasks 

Predict the effects of environmental stressors and sustainment training frequency on 

performance 

Estimate individual and collective section workload 

Although some of the questions may appear to be “repeats” of previous questions and are 

highlighted in gray. You do not have to answer them again unless you believe that under this 

function your answer would be different than you previously gave under another function. 

If you have served in a number of 96D positions doing these tasks, select one of your 

experiences to draw from to answer the questionnaire. List that echelon where you worked at 

the beginning of the questionnaire. 

In BOLD typeface are the overall functions, and below these functions there will be several 

separate tasks an analyst must perform to complete each overall function. Fill in the values for 

each separate task as explained below. 

TIME: Please use seconds, minutes, etc. to estimate the average time it took you or your 

comrades to perform that task in an “average” situation. We understand that there is no real 

“average” situation, but we must ask that you create one in your head when answering these 

questions. 

(EX: 30s, 40m, 2hr) 

% (ACCURACY): Record what the minimum accuracy standard should be required (as 

percentage correct) when accomplishing the task listed. (EX: 90, 85, 98) Or, what is the 

expected accuracy for a soldier performing this particular task. 

VACP: Refer to the separate sheet which has the scales for the Visual, Auditory, Cognitive, and 

Psychomotor inputs. Do not be put off if you find that many functions have the same VACP 

values. This is common. We still need this data from you. To accomplish each listed task, 

some combination of Visual (looking), Auditory (listening), Cognitive (thinking) and Psychomotor 

(moving) is required. Some tasks will not require the use of all four components to accomplish. 
14

If this is the case, then put a “0” in the box. (EX: V-4, A-0, C-5, P-2) 

Methodology: Look at the critical task (In bold, generally begins with a critical task number, in 

this example 301-96D-1050 PLOT COORDINATES ON A MAP, IMAGE OR GEOSPATIAL 

DATA). 

Then review the performance steps associated with each critical task. For each performance 

step we want you to provide the average length of time you believe it would take a qualified, 

trained soldier to complete in seconds, minutes or hours. The next column (%) represents the 

expected accuracy standard for this performance measure. We want your opinion on what 

accuracy standard is expected on this sub task. What we are asking for is an average accuracy 

standard required for this task. We are not asking for a performance evaluation on what you 

think a soldier can/will do, but the required level of accuracy needed to complete this sub task to 

standard. 

The final 4 columns (VACP) represent the estimated workload, in each category, on a soldier 

doing this sub task. V stand for Visual (seeing, looking), A for Auditory (listening/hearing), C for 

Cognitive (mental workload / thinking) and P for Psychomotor (non-reflexive muscle movement) 

Please read the sub task and then refer to the provided VACP scale to determine the 

appropriate level based on your experience. 

Sample from questionnaire 

 ERMP Survey 

1 301-96D-1050 PLOT COORDINATES ON A MAP, 

IMAGE OR GEOSPATIAL DATA 

Time % V A C P 

1.1 Determine the scale of the map sheet in use. 30S 100 4 0 4 2 

1.2 Plot given geographic coordinates. 1M 100 5 0 3 1 

1.3 Create lines of latitude and longitude by connecting 

the grid tick marks on the neat lines 

2 H 80 6 7 7 7 

Note: The times, accuracy % and VACP figures are for example only) 

Sample VACP scale 

Value Visual Scale Descriptor - Vision: related to, or used in vision an action done or 

executed by sight 

0 No Visual Activity 

1 Visually Register/Detect (detect occurrence of image) 

2 Visually Discriminate (detect visual differences) 

3 Visually Inspect/Check (discrete inspection/static condition) 

4 Visually Locate/Align (selective orientation) 

5 Visually Track/Follow (maintain orientation) 

6 Visually Read (symbol) 

7 Visually Scan/Search/Monitor (continuous/serial inspection, multiple conditions) 

Please keep all answers UNCLASSIFIED. 
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#REF!

3 301-96D-1101 DETERMINE DIMENSIONS OF AN OBJECT ON IMAGERY Time % V A C P

3.1 Determine the scale of the vertical imagery, if unknown.

3.2 Identify an object with a known ground distance.

3.3 Measure the dimensions of the known object.

3.4 Determine the dimensions of the known object.

3.5 Convert all measurements into the same units of measurement.

3.6 Determine the scale of imagery 

4 301-96D-1152 PREPARE A ROUTE OVERLAY Time % V A C P

4.1 ID the routes that are of greatest significance to the CMD

4.2 Retrieve the appropriate map image or product

4.3 Determine if the imagery quality is sufficient to accurately analyze the roadway and satisfy the ER.

4.4 Import the proposed route

4.5 Locate the route on the imagery.

4.6 Determine the route classification formula 

4.7 Determine the route width based on the narrowest width of the traveled way.

4.8 Determine the route type based on its ability to withstand weather.

4.9 Estimate the military load capacity (MLC) of the route.

4.10 Analyze any route obstructions/chokepoints by location and type.

4.11 Analyze any bridges.

4.12 Determine the geographic positioning data

4.13 Determine the traveled way width.

4.14 Analyze any underpasses.

4.15 Analyze any tunnels.

4.16 Analyze any sharp curves.

4.17 Analyze any areas where the roadway is constricted to less than 4 meters by craters, erosion, 

minefields, or other reasons.

4.18 Annotate overlay with appropriate classification markings, as required.

4.19 Determine the appropriate classification marking to be applied.

4.20 Satisfy the ER.

9 301-96D-1204 ID ROADWAYS ON IMAGERY Time % V A C P

9.1 Determine the requirement by examining the exploitation requirement(s) (ER).

9.2 Locate the roadway on the imagery.

9.3 Determine if the imagery quality is sufficient to accurately ID the roadway.

9.4 ID the status of the roadway.

9.5 ID the road classification.

9.6 ID any route obstructions/chokepoints by location and type.

9.7 ID any bridges by type.

9.8 ID any underpasses.

9.9 ID any tunnels.

9.10 ID any causeways or fills.

9.11 ID any sharp curves.

9.12 ID any areas with slopes/gradients over 7 percent.

9.13 ID any through or side hill cuts.

9.14 ID any areas with low overhead clearance under 4.3 meters.

9.15 ID any areas where the roadway is constricted by craters, erosion, minefields, or other reasons.

9.16 ID the roadway by functional classification code IAW DIAM 65-3-1.
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18 301-96D-1232 EXPLOIT FULL MOTION (VIDEO) IMAGERY Time % V A C P

18.1 Determine the requirement by examining the exploitation requirement(s) (ER).

18.2 Obtain any supporting data or references.

18.3 Maps, charts, or other geospatial intelligence (GEOINT) products

18.4 Review Target folders

18.5 Review Historical reports

18.6 Signatures developed through the analysis of FMV.

18.7 Obtain imagery and geospatial data.

18.8 Streaming video downlinked from the aerial vehicle.

18.9 Conduct analysis and manipulation of data.

18.10 Conduct analysis of data at various speeds.

18.11 Conduct analysis of data frame by frame.

18.12 Perform any audio/video capture.

18.13 Perform any geographic positioning.

18.14 Perform any object recognition and identification.

18.15 Perform any mensuration functions.

18.16 Perform any manipulation functions (zoom, rotate, overlaying of nonsequential frames, etc.).

18.17 Perform any change detection.

18.20 Perform any mosaicing functions.

18.21 Prepare any SPOT or SALUTE reports.

18.22 Prepare any imagery derived products (IDP) (301-96D-1159) and reports IAW unit SOP.

56 CONDUCT AREA RECONNAISSANCE Time % VACP

56.1 Establish and maintain communications with supported / friendly units 

56.2 Monitor control measures

56.3 Reconnoiter key and adjacent terrain within the assigned area

56.4 Locate all obstacles and barriers, 

56.5 Locate a bypass around built-up areas, obstacles, and contaminated areas.

56.6 Inspect and classify all bridges, overpasses, underpasses, and culverts.

56.7 Locate fords and crossing sites near all bridges.

56.8 Locate enemy elements

56.9 Report the situation based on PIR, IR

Additional tasks Time % V A C P

67 ID object, area or activity of interest on an image or video

68 Provide chip, image or video clip for additional analysis

69 Provide direction/guidance to UAV sensor operator

70 respond to request for imagery

71 Provide direction/guidance to UAV pilot
17

Appendix B. Scenario 
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Appendix C. IMPRINT Data – Task Flows by Crew Member 

Figure C-1. Crew member 1 – Primary Imagery Analyst. 

Figure C-2. Crew member 2 – Production Analyst 1. 
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Figure C-3. Crew member 3 – Writer. 

Figure C-4. Crew member 4 – NCOIC. 
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28
29
30
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List of Symbols, Abbreviations, and Acronyms 

ANOC Advanced Non-Commissioned Officers Course 

BNOC Basic Non-Commissioned Officers Course 

EO electro-optical 

ERMP extended range multipurpose 

GEOINT geospatial intelligence 

IMINT imagery, imagery intelligence 

IMPRINT improved performance research integration tool 

IR infrared 

MI military intelligence 

MOS military occupational specialty 

MTI moving target indicator 

NGA National Geospatial-Intelligence Agency 

ODIN observe, detect, identify, neutralize 

SAR synthetic aperture radar 

SME subject matter expert 

TF task force 

UAS unmanned aircraft system (formerly called an unmanned aerial vehicle [UAV]) 

VACP visual, auditory, cognitive, and psychomotor 
NO. OF 

COPIES ORGANIZATION 

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 1 DEFENSE TECHNICAL 

 (PDF INFORMATION CTR 

 only) DTIC OCA 

 8725 JOHN J KINGMAN RD 

 STE 0944 

 FORT BELVOIR VA 22060-6218 

 1 US ARMY RSRCH DEV & 

 ENGRG CMD 

 SYSTEMS OF SYSTEMS 

 INTEGRATION 

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 FORT BELVOIR VA 22060-5608 

 1 DIRECTOR 

 US ARMY RESEARCH LAB 

 IMNE ALC IMS 

 2800 POWDER MILL RD 

 ADELPHI MD 20783-1197 

 1 DIRECTOR 

 US ARMY RESEARCH LAB 

 AMSRD ARL CI OK TL 

 2800 POWDER MILL RD 

 ADELPHI MD 20783-1197 

 1 DIRECTOR 

 US ARMY RESEARCH LAB 

 AMSRD ARL CI OK T 

 2800 POWDER MILL RD 

 ADELPHI MD 20783-1197 

ABERDEEN PROVING GROUND

 1 DIR USARL 

 AMSRD ARL CI OK TP (BLDG 4600) 
NO. OF NO. OF 

COPIES ORGANIZATION COPIES ORGANIZATION

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 1 ARMY RSCH LABORATORY - HRED 

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 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MD T COOK 

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 1 ARMY RSCH LABORATORY - HRED 

 AWC FIELD ELEMENT 

 AMSRD ARL HR MJ D DURBIN 

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 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MK MR J REINHART 

 10125 KINGMAN RD 

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 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MV HQ USAOTC 

 S MIDDLEBROOKS 

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 1 ARMY RSCH LABORATORY - HRED 

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 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MQ M R FLETCHER 

 US ARMY SBCCOM 

 NATICK SOLDIER CTR 

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 NATICK MA 01760-5020 

 1 ARMY RSCH LABORATORY-HRED 

 AMSRD ARL HR MT J CHEN 

 12423 RESEARCH PKWY 

 ORLANDO FL 32826 

 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MT C KORTENHAUS 

 12350 RESEARCH PKWY 

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 1 ARMY RSCH LABORATORY - HRED 

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 SIGNAL TOWERS 

 BLDG 29808A RM 303 

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 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MU M SINGAPORE 

 6501 E 11 MILE RD MS 284 

 BLDG 200A 2ND FL RM 2104 

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 FT SILL OK 73503-9042 
NO. OF 

COPIES ORGANIZATION 

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 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MW E REDDEN 

 BLDG 4 ROOM 332 

 FT BENNING GA 31905-5400 

 1 ARMY RSCH LABORATORY - HRED 

 AMSRD ARL HR MN R SPENCER 

 DCSFDI HF 

 HQ USASOC BLDG E2929 

 FT BRAGG NC 28310-5000 

 1 ARMY G1 

 DAPE MR B KNAPP 

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 WASHINGTON DC 20310-0300 

ABERDEEN PROVING GROUND

 2 DIR USARL 

 AMSRD ARL CI OK TP 

 S FOPPIANO 

 AMSRD ARL HR MR 

 F PARAGALLO 
NO. OF 

COPIES ORGANIZATION 

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 5 ARMY RSCH LAB – HRED 

 AMSRD ARL HR MY 

 B HUNN 

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 STE 1172 BLDG 51005 

 FT HUACHUCA AZ 85613-7069 

 5 MARINE CORPS SYSTEMS CMD 

 PM-ICE 

 A SCHWEIZER 

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ABERDEEN PROVING GROUND

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