U.S. Army Research Institute 

for the Behavioral and Social Sciences 

Research Report 1830 

Battle Command Visualization 101: 

Prototype Embedded Training on Networked Sensors 

Carl W. Lickteig 

U.S. Army Research Institute 

Charles G. Heiden and William T. Holden, Jr. 

Human Resources Research Organization 

 

 

December 2004 

Approved for public release; distribution is unlimited.
U.S. Army Research Institute 

for the Behavioral and Social Sciences 

A Directorate of the Department of the Army 

Deputy Chief of Staff, G1 

ZITA M. SIMUTIS 

 Director 

Research accomplished under contract 

for the Department of the Army 

Human Resources Research Organization 

Technical review by 

May H. Throne, U.S. Army Research Institute 

Robert A. Rasch, Jr., U.S. Army Research Institute 

 

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i 

REPORT DOCUMENTATION PAGE

1. REPORT DATE (dd-mm-yy) 

 December 2004 

2. REPORT TYPE 

 Final 

3. DATES COVERED (from. . . to) 

 August 2003 – May 2004 

5a. CONTRACT OR GRANT NUMBER 

 DASW01-99-D-0012 DO 0024 

4. TITLE AND SUBTITLE 

Battle Command Visualization 101: Prototype Embedded 

Training on Networked Sensors 5b. PROGRAM ELEMENT NUMBER 

 0602785A

5c. PROJECT NUMBER 

 A790

5d. TASK NUMBER 

 211

6. AUTHOR(S) 

Carl W. Lickteig (U.S. Army Research Institute), Charles G. 

Heiden and William T. Holden, Jr. (Human Resources 

Research Organization)

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Human Resources Research U.S. Army Research Institute for the 

 Organization (HumRRO) Behavioral and Social Sciences 

66 Canal Center Plaza, Suite 400 ATTN: DAPE-ARI-IK 

Alexandria, VA 22314 2423 Morande Street 

 Fort Knox, KY 40121-5620

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Research Report 1830 

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14. ABSTRACT (Maximum 200 words): 

The anticipated ability of the Future Force to See First requires proactive research to transform the emerging 

concepts of embedded training and networked sensors into assets. This report documents initial research on an 

innovative training program to provide small unit commanders the tactical and technical skills needed to exploit 

networked sensors to meet the See First objective. The research addressed three areas: design of a prototype 

embedded training program on networked sensors, design of a subset of training exercises for the training program, 

and development of those exercises in a prototype command and control (C2

) system compatible with virtual 

simulation. Design and development stressed a structured training approach with progressive simulation-based 

exercises. A representative subset of 20 exercises were developed that focused on exploiting an array of networked 

sensors on unmanned air vehicles (UAVs) to support the commander’s critical information requirements. Results 

provide lessons learned on technology dependent training that will be used to refine and extend the prototype 

training on employing networked sensors, and that may apply to the Army’s ongoing effort to develop embedded 

training for the Future Force.

15. SUBJECT TERMS 

Training Perception Human Performance Sensors Networked 

Sensors 

Command and Control See First Battle Command Visualization Future Force Future

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ii 
Research Report 1830 

Battle Command Visualization 101: 

Prototype Embedded Training on Networked Sensors 

Carl W. Lickteig 

U.S. Army Research Institute 

Charles G. Heiden and William T. Holden, Jr. 

Human Resources Research Organization 

Armored Forces Research Unit 

Barbara A. Black, Chief 

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

2511 Jefferson Davis Highway, Arlington, Virginia 22202-3926

December 2004 

Army Project Number Personnel Systems and 

2O262785A790 Performance Technology

Approved for public release; distribution is unlimited 

iii 
iv 
FOREWORD 

Innovative training solutions to help the Army transform to the Future Force and Future 

Combat Systems (FCS) are a key concern of the U.S. Army Research Institute for the Behavioral

and Social Sciences (ARI). The Future Battlefield Conditions Team of ARI at Fort Knox is 

developing a prototype embedded training program to provide small unit commanders the 

technical and tactical skills to exploit networked sensors. This research supports work package 

(211) FUTURETRAIN: Techniques and Tools for Command, Control, Communications, 

Computers, Intelligence, Surveillance, and Reconnaissance Training of Future Brigade Combat 

Team Commanders and Staffs. This research also supports the Science & Technology Objective 

(STO) “Methods and Measures of Commander-Centric Training.” 

To transition from the Current Force to the Future Force, concepts about the future 

including embedded training and networked sensors must be investigated efficiently and 

effectively for utility and development. Small unit commanders in the Current Force have little 

experience with networked sensors on a regular basis; with the right training, their Future Force 

counterparts will routinely exploit them. 

The research reported here designed a prototype embedded training program for 

employing networked sensors called Battle Command Visualization (BCV) 101 and developed a 

representative subset of supporting training exercises. Findings from this initial phase of training 

development underscore the problems and potential of combining theory and technology for 

embedded training to support the Future Force, particularly in exploiting networked sensors to 

See First. An ongoing follow-on effort will extend and refine the BCV 101 training program to 

provide an exemplar of embedded training for the Future Force. 

Results of this ongoing effort were presented to representatives from the Defense 

Advanced Research Projects Agency and the Program Executive Office for Simulation, Training 

and Instrumentation for refinement and transition of the BCV 101 training program. 

 MICHELLE SAMS

 Technical Director 

v 
vi 
BATTLE COMMAND VISUALIZATION 101: PROTOTYPE EMBEDDED TRAINING ON 

NETWORKED SENSORS 

EXECUTIVE SUMMARY 

Research Requirement: 

The Army has identified four essential Qualities of Firsts for the Future Force: See First, 

Understand First, Act First, and Finish Decisively. The Quality of See First forms the foundation 

for all other Qualities. The requirement to See First rests heavily on proactive research to 

transform two concepts – networked sensors and embedded training – into assets. Small unit 

commanders in the Current Force have little experience with networked sensors on a regular 

basis; with the right training, their Future Force counterparts will routinely exploit them. 

This report documents initial research to develop a prototype embedded training program

to provide small unit commanders the tactical and technical skills to exploit networked sensors. 

Because the visualization skills targeted by this research are basic or “101” introductory level 

skills for battle command visualization (BCV), the training program is referred to as BCV 101. 

Procedure: 

The BCV 101 research addressed three areas: design of an embedded training program 

on employing networked sensors, design of a subset of training exercises for the training 

program, and development of those exercises in a prototype command and control (C2

) system

compatible with virtual simulation. Program design stressed a structured set of simulation-based 

exercises nested in a training matrix to systematically control skill progression and assess 

proficiency through a series of criterion or “gate” exercises. 

Exercise design and development focused on a representative subset of exercises to 

convey an understanding of the capabilities and limitations of networked sensors. A subset of 

twenty (20) exercises was developed that focused on exploiting an array of networked sensors on 

unmanned air vehicles (UAVs) to support the commander’s critical information requirements. 

All exercises were initially developed as short-duration (5-10 minute) exercises to maintain a 

close link between tasks, conditions, and standards, and to provide repeated opportunity for 

practice and feedback. 

During development, the individual exercises were temporarily sorted into exercise 

bundles to overcome current technical challenges to technology dependent training. All 

exercises provided simulation-based feedback, including visual depiction of dynamic sensor 

footprints or coverage areas, automated alerts, and data on sensor detections and sensor images 

received. Formative evaluations of the exercises with supporting research personnel identified 

shortcomings in training design and exercise development that will be addressed in follow-on 

research efforts for BCV 101, and potentially the Army’s efforts to develop embedded training in 

Future Combat Systems. 

vii 
Findings: 

Overall, the BCV 101 effort provides a promising start on the extended process of 

developing a working example of embedded training to help meet the See First requirement. The 

design of the training program and exercises combines theory and technology to address an 

important and unmet training objective of the Future Force. A follow-on effort is underway to 

extend and refine BCV 101 training based on lessons learned and challenges encountered. In the 

interim, this progress report documents current status and provides a set of training implications 

to further the process of developing embedded training for FCS and the Future Force. 

A key finding is that the technical challenges to technology-based training are imposing. 

This finding may seem obvious, but it is often ignored when future training concepts such as 

embedded training and simulation-based training are envisioned. In particular, the prototype C2

system linked to virtual simulation did not readily support the training design requirement for 

minimal turn-around time between exercises. The use of exercise bundles versus discrete 

exercises provided a near-term workaround to training development, but weakened the training 

design’s links between tasks, conditions, and standards. Technical challenges also limited the 

ability of the training developers to manipulate and augment feedback on the process and 

outcomes of sensor employment. 

A related finding is that proactive collaboration and research between the developers of 

training and technology is essential to achieving simulation-based embedded training. A burden 

on training developers is to provide identified training requirements and proposed training 

designs to system developers during the earliest stages of system design. A burden on system

developers is to modularize the C2 system to permit modifications by training developers. 

Embedded training guidelines stress the need for a “Training Mode” or module in the C2

 system; 

however, the findings stress that a “Training Development Mode” is also needed. 

Utilization of Findings: 

The findings will guide the ongoing follow-on effort to extend and refine the BCV 101 

training program. To work around technical challenges, the training design will extend to multimedia and multi-method training approaches. To overcome technical challenges, current and 

future findings will be provided to the developers of C2

 systems for FCS and to the Army’s 

training developers for embedded training. To meet the See First requirement, the BCV 101 

training development effort will endeavor to provide the Future Force the right training to exploit 

networked sensors routinely. 

viii 
BATTLE COMMAND VISUALIZATION 101: PROTOTYPE EMBEDDED TRAINING ON 

NETWORKED SENSORS 

CONTENTS

Page 

Introduction................................................................................................................................ 1 

 Organization of the Report................................................................................................... 1 

Background................................................................................................................................ 1 

 The Quality of “See First” ................................................................................................... 2 

 Training for See First........................................................................................................... 2 

 Sensors in Support of See First............................................................................................ 3 

 Preliminary Research with Sensors and See First Training................................................. 5 

 

Research Requirement ............................................................................................................... 6 

Research Method ....................................................................................................................... 6 

 Training Program Design..................................................................................................... 7 

 Exercise Design ................................................................................................................... 11 

 Exercise Development ......................................................................................................... 13 

 Description of the Command and Control (C2

) Interface .............................................. 14 

 Construction of the Sample Exercises ........................................................................... 18

 Train-the-Trainer............................................................................................................ 19 

 Major Technical Limitation ........................................................................................... 19 

 Exercise Bundles............................................................................................................ 20 

 Exercise Overview......................................................................................................... 20 

 Development Process..................................................................................................... 21 

 

Training Implications................................................................................................................. 24 

 FCS Training Challenge ...................................................................................................... 24 

 Training Design: Structured Training................................................................................. 25 

 Training Development: Technical Collaboration and Authoring Tools............................. 25 

 Training Delivery: Simulation-Based Embedded Training ................................................ 27 

 Training Feedback: Performance Measurement and Perceptual Augmentation................. 28 

Conclusions................................................................................................................................ 29 

ix 
CONTENTS (continued)

Page 

References.................................................................................................................................. 31 

Appendix A Acronyms............................................................................................................. A-1 

Appendix B Prototype Program of Instruction ......................................................................... B-1 

Appendix C Training Matrix .................................................................................................... C-1 

List of Tables 

Table 1 Overview of Purpose and Approach for Four Modules in the Training 

 Design for BCV 101 ................................................................................................. 9 

Table 2 Basic Specifications for 20 Exercises in the Sensor Training Matrix ......................... 12 

Table 3 Summary of BCV 101 Training Implications.............................................................. 24 

List of Figures 

Figure 1 Sample of current operational unmanned air vehicles (UAVs).................................. 4 

Figure 2 Systematic variations in task conditions for training exercise matrix........................ 8 

Figure 3 Initial sensor training progression matrix................................................................... 8 

Figure 4 Command group participants in a surrogate command and control (C2

) vehicle....... 14 

Figure 5 Sample view of the command and control (C2

) interface........................................... 15 

Figure 6 Command and control (C2

) interface with sensor tasking information...................... 17 

Figure 7 Animation snapshot with microUAVs en-route or on-station awaiting next tasking 

from Shadow 200 ................................................................................................... 18 

Figure 8 Sample of synthetic aperture radar (SAR) detections ................................................ 22 

Figure 9 Command and control (C2

) interface with microUAV sensor image......................... 23 

Figure 10 Visual enhancements of sensor coverage and footprint areas................................... 29 

x 
BATTLE COMMAND VISUALIZATION 101: PROTOTYPE EMBEDDED TRAINING ON 

NETWORKED SENSORS 

Introduction 

This report documents initial work for an ongoing research effort by the U.S. Army

Research Institute for the Behavioral and Social Sciences (ARI)1

 titled Battle Command 

Visualization 101. The research focuses on providing a prototype example of embedded training 

for individuals in company-level command groups employing networked sensors in the Future 

Force. Because the visualization skills targeted by this research are basic or “101” introductory 

level skills for battle command visualization (BCV), the training program is referred to as BCV 

101. Based on the research reported here, a follow-on BCV 101 research effort is underway and 

will be documented in a subsequent report. 

Organization of the Report 

The Background section of this report discusses the major research and development 

underpinnings for the work. The Research Method section then addresses three primary research 

areas: design of an embedded training program for employing networked sensors, design of a 

subset of training exercises for the program, and development of 20 exercises in a prototype 

command and control (C2

) system. The final sections of the report address training implications 

for follow-on BCV 101 research, and potentially for embedded training development. 

Background 

The BCV 101 training program stems from the operational expectations for Soldiers 

equipped with Future Combat Systems (FCS) in the U.S. Army’s Future Force. The Army’s 

ongoing transformation to FCS poses an unprecedented alliance of humans and machines. 

Currently, FCS is essentially a conceptual design featuring an interdependent system-of-systems 

(Department of the Army [DA], 2003b). Interdependence is reflected in the concept of a 

network-centric force composed of modular manned and progressively autonomous platforms 

with networked communication, sensor, and fire capabilities. 

The Future Force equipped with FCS will be more strategically employable, with the

requirement for rapid deployment and seamless operations across the full spectrum of military 

operations. The Army has identified four Qualities of Firsts that the Future Force must possess 

to operate effectively in that future environment: See First, Understand First, Act First, and 

Finish Decisively. Training that supports these Qualities must be embedded in every Future 

Force operational system, and in supporting systems, including laptop computers and personal 

digital assistants (DA, 2003b). 

The BCV 101 training program is focused on sensor exploitation to support the See First 

Quality. This Background section describes what that Quality means, how training and system

developments are evolving to support the Quality, and what initial research efforts are revealing 

about skills and skill acquisition for See First. 

 

1

 Appendix A contains a list of all acronyms used in this report. 

1 
The Quality of “See First” 

The Qualities of Firsts are critical for transformation to the Future Force. To promote 

transformation, these Qualities are already appearing in the latest generation of U.S. Army

manuals. By relating familiar processes to the Qualities, the doctrinal discussions give direction 

and definition to superior information awareness. The Army’s Field Manual (FM) 6-0, Mission 

Command (DA, 2003a), states that the See First Quality “...equates to the ‘observe’ activity but 

emphasizes accomplishing it before the enemy does” (p. A-3). The See First Quality is related 

directly to familiar reconnaissance and force protection concepts in that it emphasizes the 

processes of discovering information about the enemy and shielding friendly information from

the enemy while developing the situation out of contact. 

The ability to integrate the raw data flowing into the C2

 system with already-processed 

information to construct a coherent common operating picture has long been a critical 

requirement for commanders. For the Future Force, the commander is still the critical link, but 

commanders at all levels down to the company will have greater access to all collection sources 

on the battlefield. As commanders enter Future Force organizational structures, they will adapt 

familiar methods and procedures to exploit the new capabilities. The start-point of the 

operational planning-execution cycle for Future Forces is described in FM 6-0 (DA, 2003a) in 

this way: 

During operations, commanders first observe the situation – that is, they collect

information. They learn about the status of their own forces, the environment, and 

the enemy through intelligence, surveillance, reconnaissance, information systems 

(INFOSYS), and reports from other headquarters. Sometimes they actively seek 

information; sometimes the command and control (C2

) system disseminates it to 

them (p. A-1). 

The U.S. Army Training and Doctrine Command (TRADOC) pamphlet Military

Operations, Objective Force Maneuver Units of Action (DA, 2002) also discusses the Quality of 

See First for Future Forces. Future Force units, using the networked, cooperative sensors within 

the joint operational environment, will see the enemy as a whole complex and adaptive 

organization. Commanders will have the ability to examine and follow up on any particular 

input into the system for their own purposes in order to gain key and critical information 

affecting an immediate decision. Parallel analyses will be conducted by higher, lower, adjacent, 

and supporting commanders working to identify critical enemy decisions or actions that indicate 

a particular enemy course of action. The data and conclusions will be mutually informing and 

supporting, allowing for a rich and accurate common operational picture that assists commanders 

in understanding what the enemy is currently doing, and what he may do next. 

Training for See First 

Soldiers face formidable tasks at every level to provide relevant contributions to the 

requirements of the See First Quality. One assumption under the See First Quality requires that 

every Soldier, not just sensor operators, understand the capabilities and limitations of the sensor 

network (ARI, 2003). This basic understanding will enable the use of the sensor network to gain 

2 
a common operating picture within the unit effectively and efficiently. The requisite technical 

and tactical skills of the Future Force command group will have to reside at every echelon, 

allowing full and immediate exploitation of every advantage on the battlefield. Commanders 

will force cross training in critical skills across the command group in order to exploit 

technological strengths and shield technological limitations. 

The assumption that only technical experts will examine and process raw data from the 

sensor network is untenable in the time-constrained future battlefield. A more viable assumption 

is that multi-skilled warfighters, including small unit command groups, will need to gather and 

analyze disparate sensor data into information, into a coherent and immediately comprehensible 

operational picture. The challenge is exacerbated by the fact that every combat organization in 

the Future Force will have organic sensors, including unmanned air vehicles (UAVs), from

platoon level up. The lower echelons of command do not have staff or trained intelligence 

specialists to help analyze the information. 

Early prototyping for testing key concepts – such as networked sensor employment and 

embedded training – provides a method of shortening the cycle from concept, to development, to 

operational asset. Early prototyping, however, should not be limited to work on the operational 

use of technologies; parallel efforts should be ongoing for training as well. By introducing 

visualization2

 training along with the operational system itself, developers ensure that the Future 

Force Soldier will be able to learn to use the operational system to its fullest advantage. This 

binding of operations and training offers more immediately a proficient starting performance for 

Soldiers as soon as the systems are fielded. Additionally, the embedded training paradigm

(Gossman, Flynn, & Breidenbach, 2004) posits that training systems will accompany Soldiers 

during the training-alert-strategic movement-operations cycle of deployment. This capability 

will increase the ability of the unit to achieve a high level of mission-specific proficiency. 

Training that capitalizes on the ability of the Future Force to receive direct sensor feeds at any 

point before and during deployment will help commanders See First. 

Training on networked sensors sooner rather than later will help Soldiers adapt from

current to future methods for observing the battlefield. With an advanced array of individual 

networked sensors capable of cross cueing (passing information and commands to each other and

acting together to gather additional information), the Future Force will see the enemy in realtime. Exploitation of the robust and numerous echeloned sensor systems from tactical to 

strategic levels will be enabled by human-computer capabilities to quickly categorize, store, and 

broadcast the data as useful information. 

Sensors in Support of See First 

The Future Force will gain information through a system of networked sensors. The use 

of air sensor platforms, particularly unmanned platforms, represents a significant departure from

 

2

 In common language, “visualization” is “...the act or power of forming visual images of objects not present to the 

eye” (G. & C. Merriam Company, 1961). When used in the context of battle command, it takes on greater meaning,

as the core mental process that commanders use in decision-making to determine how to get forces from their 

current state or position to the end state that represents mission accomplishment (DA, 2003a). The first step in the 

visualization process is to collect information, hence the “101” in BCV 101.

3 
the Current Force operation that is vital to the Quality of See First. Unmanned systems will 

increasingly provide small unit commanders with reports, snapshots, and live video outputs of 

battlefield conditions. Pushing sensors and their capabilities downward to the platoon level 

represents a key shift in decision-making and operation. 

The sensor capabilities that enable Future Force units to receive continual feeds of critical 

data include (a) rapid movement around the battlefield, (b) comparatively long standoff while 

observing due to altitude and sensing systems, (c) survivability through size and speed, and 

(d) communication of real-time images across networks. Once trained, Soldiers will be able to 

use the sensor networks to detect above surface, surface, and subsurface targets through electrooptical, thermal, acoustic, magnetic, chemical, and seismic activity. Sensors will be equipped 

with both passive and active systems to provide target acquisition, real-time tracking, and 

identification of entities in the area of operations for the unit. Sensor data and other information 

form the basis of commander’s ability to See First, make decisions, and control manned and 

robotic units on the battlefield. 

Some, but not all, of the Army’s thinking and planning for UAV sensors is based on real 

systems currently being used, some of which are shown in Figure 1. Certain systems, including 

the Hunter and Shadow 200 UAVs, have received intensive workouts during Operation Iraqi 

Freedom (Odierno, 2004). 

Figure 1. Sample of current operational unmanned air vehicles (UAVs). 

At the center of Figure 1, the Shadow 200 is a brigade-level UAV equipped with an 

electro-optical/infrared (EO/IR) sensor turret. The use of EO/IR technology enables 

transmission of real-time video in both daylight (visible) and obscured (thermal) conditions. At 

4 
11 feet long with a 13-foot wingspan, the Shadow 200 can cruise at 70 knots for up to 6 hours at 

an altitude of 14,000 feet. The larger “Hunter” is a division-corps UAV and has increased 

operating characteristics in terms of speed, altitude, and flight time. At the other end of the 

spectrum, small hand-launched UAVs such as the “Raven” are also being used in Iraq and in 

Afghanistan (Operation Enduring Freedom). The Raven weighs in at about 6 pounds and has a 

4-1/2 foot wingspan. It carries a 2-pound EO/IR camera, and can fly for 60 to 80 minutes out to 

a range of about 10 kilometers. In theory, the Raven would be used at the discretion of the 

tactical or small unit commander without the need for permission from brigade or higher 

echelons. 

Other UAVs are under development at the Defense Advanced Research Projects Agency 

(DARPA), such as the A160 Hummingbird rotorcraft. The A160 UAV uses a hinge-less, rigid 

rotor to achieve a high-endurance (over 24 hours), high-altitude (30,000 feet) capability. With 

its synthetic aperture radar (SAR), snapshots can be transmitted back to the unit, and moving 

target indicator (MTI) technology supports detection and reporting of movement in the area. 

Designs for microUAVs, which are miniature aircraft, less than 6 inches in any dimension, are 

being explored by DARPA. The DARPA microUAVs are designed to aid individual Soldiers 

and Marines fighting in urban areas and close terrain. The program is pushing technologies such 

as small, lightweight propulsion, small sensors, and communication technologies (Tiron, 2001). 

Thus, there is still a great deal of conceptualizing, developing, and decision-making to be 

done in the area of UAV planning, design, and procurement. Proactive research and 

development efforts such as those described below often portray system capabilities that do not 

yet exist – to advance experimentation on human performance, to explore and refine operational 

concepts, and to develop prototype training methods. 

Preliminary Research with Sensors and See First Training 

The U.S. Army’s Communications-Electronics Command (CECOM) at Fort Monmouth 

worked with DARPA on the FCS C2

 research program to conduct a series of exploratory 

experiments with Future Force organizations and equipment (Lickteig, Sanders, Durlach, 

Lussier, & Carnahan, 2003). The virtual simulation for this work represented a Future Force 

environment for a notional FCS small unit under a company-level command group. 

Training requirements for a small command group with highly automated and 

interdependent network-based systems may entail unprecedented levels of tactical and technical 

expertise (Lickteig et al., 2003). For new participants in the FCS C2

 program, introductory 

training and practice required two weeks followed by a one-week experimentation period. In the 

main, this introductory training began with a lecture-based non-interactive slide show primer to 

show concepts and graphic depictions of weapon and sensor systems, including individual sensor 

coverage and outcomes. Depictions of this type showing idealized results offer excellent 

information, but little opportunity for participants to assimilate the skills and interactions 

associated with the range of networked, organic, and external sensors required for their missions. 

Providing information is not necessarily a bad approach to training, but it is usually 

incomplete; the training ought not to stop there. Observations of the FCS C2

 training suggested 

5 
that the C2 interface could be adapted to introduce, familiarize, and train proficiency with 

multiple aspects of sensor employment and exploitation. Once Soldiers were trained on use of 

the C2 interface itself and had practiced with overlays, files, and various menu-driven features, 

they would be able to engage in high-fidelity interactive multimedia training using the system

features. Whether or not the interface could in fact be adapted was unknown, but the notion of 

using a simulated operational C2

 system to support training on that system closely parallels 

expectations for Future Force embedded training (DA, 2003b; Throne & Burnside, 2002). 

Lessons learned from the FCS C2

 experiments centered on the difficulties that even 

experienced participants had in mastering the technical and tactical aspects of individual and 

networked sensors. For example, participants were frequently unable to recognize and identify 

individual elements in the enemy array. This array included a large number of vehicle types 

along with dismounted Infantry Soldiers. Even when the participants could conduct the basics of 

See First initially, they soon became overwhelmed by the mass of data from the sensors. They 

sometimes lost track of individual threat entities and were unable to assemble and visualize an 

accurate common operating picture. Similar shortcomings in battlefield visualization and 

associated skills were identified previously, particularly at the commander and staff levels 

(Reilly, 1997; Solick, Spiegel, Lussier, & Keene, 1997). Effective training in basic visualization 

skills is needed now more than before to meet the See First objective. 

Research Requirement 

Realization of the increased demands that the future operating environment will place on 

Soldiers, coupled with understanding of the performance shortcomings already identified in 

current environments, led to the research requirement on which this project was based. 

Specifically, research was needed that would result in a prototype design for embedded training 

(DA, 2003b) with a focus on employing networked sensors. 

For the initial BCV 101 effort, the design would specify a set of exercises to provide a 

graduated approach to skill acquisition and include a representative sample of simulation-based 

exercises developed on a prototype C2

 system. Assessment of the exercises for the initial effort 

was limited to pilot tryouts and demonstrations for formative evaluation, with more rigorous 

external evaluations anticipated in follow-on efforts. A more immediate goal of the BCV 101 

effort was to support participant training for future experiments by the FCS C2

 research program, 

currently called the Multi-Cell and Dismounted Command and Control (M&D C2

) research 

program. A more extended goal was to develop a working example of embedded training on 

sensor exploitation required by FCS and the Future Force. 

Research Method 

The research methods for BCV 101 comprised three distinct but related areas. “Training 

Program Design” examines the basic design parameters and training requirements and then 

describes the training program designed to address those requirements. “Exercise Design” 

describes a representative subset of 20 exercises prepared to convey an understanding of the 

capabilities and limitations of networked sensors. “Exercise Development” documents the 

process and results of an attempt to develop the 20 exercises on a futuristic C2

 system. 

6 
Training Program Design 

The BCV 101 training concept for basic visualization skills acquisition includes several 

key design parameters (ARI, 2003). Overall, the training should comprise a large set of 

relatively short exercises (5-10 minutes), giving individual participants multiple practice and 

feedback opportunities to facilitate skill acquisition and reinforcement (Sanders, 2003; West, 

Farmer, & Wolff, 1991). Together, the set of exercises should systematically vary the range of 

task and/or battlefield conditions under which networked sensors would be used and convey the 

capabilities and limitations of individual and networked sensors for small unit command groups. 

The training should vary task conditions in a progressive and systematic fashion to 

provide skill reinforcement while simultaneously increasing performance difficulty. The 

prescribed progression through the exercises should be computer-assisted, so that each 

participant’s progress would be optimal in terms of performance difficulty and skill 

reinforcement. 

A series of gates (criterion performance tests) should be presented at strategic locations 

or times in the progression, both to ensure that training participants would demonstrate lower 

level skills prior to beginning work on more difficult skills, and also to permit fast learners to 

proceed through the exercises efficiently. Gated performance also provides the data required to 

manage training in embedded and related training delivery systems referred to as learning 

management systems (Appendix B, p. B-5). Computer-assisted training should also reduce the 

demand for expert trainers, and allow progression and feedback to be standardized. 

Training in BCV 101 should concentrate primarily on the conceptual skills required to 

exploit sensors rather than procedural skills or “switchology” required simply to control sensor 

activity. The specific sensor systems for this initial training design were limited to UAVmounted sensors, and included electro-optical and thermal video, SAR, and MTI radar. The 

design is scalable, however, allowing later expansion to include additional sensor systems or 

network capabilities. 

In effect, the design parameters are similar to the familiar and successful Conduct-of-Fire 

Trainer (COFT) model (COFT, 2003). Like COFT, the plan was to have exercises of graduated 

difficulty and short duration, structured and ordered to concentrate on specific sensors and their 

outputs. Figure 2 illustrates the broad concept for systematic variations in task conditions that 

would enable progressive difficulty. 

In Figure 2, the variations in task conditions that will define the training matrix include 

considerations of sensor type, weather/visibility, terrain types, enemy/target status (including 

type, number, activity, and countermeasures such as cover and concealment), and cue type – 

detection or prediction. 

7 
Weather

Terrain

Target

Cue

Sensor Type

Training

Module

Weather

Terrain

Target

Cue

Sensor Type

Training

Module

Figure 2. Systematic variations in task conditions for training exercise matrix. 

Figure 3 provides a more detailed example that shows a progressive matrix model that 

varies type and number of sensors, visibility (day, night), and a small representative selection of

targets. If all task conditions and combinations of conditions for the variables shown in the 

matrix in Figure 2 were developed into exercises, there would be upwards of 1500 exercises. 

The array shown in Figure 3 represents a systematic sample of the domain of conditions – 

preserving the progressive difficulty feature while avoiding an exhaustive (and exhausting, and 

unnecessary) inclusion of all possible exercises. 

Single

Sensor

Multi 

Sensor

Day

Night

Stationary Moving

Single

Target

Tracked

Multiple

Target

Camo 

Target

Single 

Target

Multiple

Target

Formation

Camo

Target

No Target

(Boresight Ex)

Infantry

dug-in

Single

Target

Wheeled

Multiple

Target

Convoy

UAV

MT

UA

I

V

SAR

Ground

Magnetic

Ground

Thermal

UAV

Thermal

UAV

SAR

UAV

EO

Ground

Acoustic

Ground

Seismic

Ground

EO

UAV

SA

U

R

AV

Thermal

Sensor

Footprint

Sensor

Footprint

Ground

Acoustic

UAV

Thermal

UAV

Thermal UAV

EO

UAV

EO

UAV

EO

Single

Sensor

Multi 

Sensor

Day

Night

Stationary Moving

Single

Target

Tracked

Multiple

Target

Camo 

Target

Single 

Target

Multiple

Target

Formation

Camo

Target

No Target

(Boresight Ex)

Infantry

dug-in

Single

Target

Wheeled

Multiple

Target

Convoy

UAV

MT

UA

I

V

SAR

Ground

Magnetic

Ground

Thermal

UAV

Thermal

UAV

SAR

UAV

EO

Ground

Acoustic

Ground

Seismic

Ground

EO

UAV

SA

U

R

AV

Thermal

Sensor

Footprint

Sensor

Footprint

Ground

Acoustic

UAV

Thermal

UAV

Thermal UAV

EO

UAV

EO

UAV

EO

Target Type

Figure 3. Initial sensor training progression matrix. 

The resulting BCV 101 training program design is documented in Appendix B. The 

design provides a general framework for exercise design and development and addresses each of 

the key design parameters previously discussed. Notably, the design is organized into four 

modules that reflect an individual training sequence based on performance difficulty and 

8 
progression from enabling skill to terminal skill, as described in Appendix B. The four training 

modules demonstrate the building block approach concerning sensor capabilities and limitations 

for a See First training objective. Table 1 summarizes the purpose and approach for each of the 

four training module. 

Table 1 

Overview of Purpose and Approach for Four Modules in the Training Design for BCV 101 

Module 1: 

Sensor 

Capabilities, 

Limitations,

and 

Vulnerabilities 

Purpose: To train the participants on their unit’s organic sensor capabilities, limitations, and 

vulnerabilities, as well as the external sensors that may be made available to them. Participants will 

also be trained on the methods that enemy forces can be expected to use to destroy sensors, spoof 

them, or negate their capabilities. 

Approach: Participants will be shown a series of informational pages, pictures, animations, and 

simulations that will cover the capabilities, limitations, and vulnerabilities for each system. Where

appropriate they will be provided tactics, techniques, and procedures (TTPs) on the employment of 

various systems. Specific information covered during this training will include the effects of weather 

and terrain on the sensor. 

Participants will be presented tactical situations with various weather, terrain, and enemy conditions 

and asked to select the appropriate sensor. The participant could also be asked to identify raw sensor 

images and determine which sensor provided the image. The participant could also be provided 

degraded sensor images and be asked to identify changes to the sensor payload or mission tasking 

(such as altitude, speed, sensor payload orientation) that could improve the quality of the image. 

Module 2: 

Sensor Tasking 

Purpose: To train participants to task organic unit sensors to gain information to answer the 

commander’s critical information requirements (CCIR) and Priority Intelligence Requests (PIR). The 

participants will also be trained to identify CCIR and PIR that are beyond the capabilities of their 

unit’s organic capabilities and to initiate requests for information to higher headquarters to obtain 

support from external sensors to provide the needed support. 

Approach: Participants will be provided a tactical framework, including the higher unit’s 

reconnaissance and surveillance (R&S) plan and overlay, CCIRs and PIRs and intelligence 

summaries. A visual projection of the location of all friendly forces based on the common operational 

picture will be available. They will also be provided their unit’s R&S tasking matrix that has been 

completed except for the designation of the sensor that will be employed to answer the CCIR/PIR. 

The matrix is designed so that each successive task becomes more complex through manipulation of 

weather, terrain, the target type, the availability of sensors, and the ambiguity of the enemy situation. 

To answer each PIR, the participant will be required to select from a list of possible sensor taskings 

that provide the sensor’s mission profile (speed, altitude, sensor payload, sensor payload orientation, 

and other pertinent information). Once the participant selects a sensor, the interactive courseware will 

provide a simulation of the sensor tasking and provide the appropriate sensor output for that selection. 

If the participant has made the adequate selection, the desired information will be displayed. If the 

participant has made an inadequate selection, the participant will be provided additional feedback as 

to why that selection was incorrect and then be required to make another selection. This process will 

be repeated until all of the pre-designated PIR elements have been covered. 

Initially, the participant will be selecting taskings for individual sensors. Later in the module, the 

participant will be required to select among multiple combinations of sensors to answer the 

CCIR/PIR. As the participant progresses through the training, the amount of feedback or coaching 

that is being presented for an incorrect selection will be reduced so that by the end of the training level 

the participants have to determine where they made an error without outside assistance. 

(continued on next page) 

9 
Module 3: 

Dynamic Sensor 

Retasking 

Purpose: To train the participants to dynamically retask organic unit or external higherheadquarters sensors based on changing mission, enemy, troops, terrain, time, and civilian

considerations (METT-TC) factors. 

Approach: Participants dynamically retask sensors during a series of increasingly complex 

exercises in which there is a dynamic friendly and enemy situation. Their sensor retasking

will be expected to answer time-sensitive CCIR/PIRs. The participant is provided the higher 

unit’s intelligence, surveillance, and reconnaissance plan and overlay, CCIRs and PIRs, and 

intelligence summaries. The location of all friendly and detected enemy forces will be

displayed on a visual projection of the common operational picture. Initially, the participant 

will be executing an R&S tasking matrix in which the sensor tasking has already been 

developed. During the course of task execution, the participant will be provided additional 

information, which will cause the participant to change the sensor tasking.

As the participant progresses through the levels, the number of sensors that the participant 

will have to retask and the potential vulnerability of the sensors to enemy action will increase. 

Simultaneously, decreases will occur in the amount of information provided about the enemy,

and the amount of time between the retasking directive from the participant’s commander 

until the enemy situational information is expected to be presented. 

Module 4: Sensor 

Integration into 

Tactical

Operations 

Purpose: To train the participants to integrate organic unit-level sensors and available higher 

headquarters sensors into Intelligence, Surveillance, and Reconnaissance (ISR) operations. 

Approach: The participants plan and execute a series of ISR operations, primarily focused 

on organic unit-level sensors and available higher headquarters sensor support. They will 

develop an ISR plan that includes an R&S tasking matrix and an R&S overlay to gain

information to support the CCIR/PIR. The participants will then execute the ISR plan, 

making adjustments to sensor taskings based on an evolving enemy and friendly situation. 

Participant performance will be graded based on the amount of information that is obtained 

from the ISR plan and the efficiency of the operation in terms of whether the sensor tasking 

obtained the desired information (more information is better), number of sensors required to

obtain the information (fewer is better), the number of sensor losses due to enemy activity

(fewer is better), and the amount of time required to answer the CCIR/PIR (shorter is better). 

As the participant progresses through the training, the ISR tasks become increasingly 

complex based on the tactical situation. For example, the initial operation might be an attack

against a defending enemy located in open terrain. Subsequent operations may include

moving enemy forces, complex terrain such as jungles or forests, and urban operations. The 

embedded Learning Management System (LMS) will track participant performance. If the 

performance warrants, the student will be referred back to Module 2 or 3 for refresher 

training. At the successful completion of the training, the participant’s training record will be 

annotated by the LMS and an appropriate training certificate issued.

Within the design description are initial recommendations on training audience, 

prerequisite training, and training objectives. Entering as an untrained participant, the individual 

will perform a series of tasks to acquire a basic understanding of sensor concepts and idealized 

outputs, and then conduct controlled and directed practice exercises to build upon this base. 

Certain exercises function as criterion checks on learning, or gate exercises, to verify knowledge 

of basic sensor capabilities and outputs. 

The BCV 101 design is predicated on a small set of specified training prerequisites. 

Particularly, the expectation is that participants are already trained on general operation of the C2

system in which the training program will be embedded. They should also be familiar with their 

unit’s organization and equipment, including the types and numbers of organic sensor systems; 

the types, numbers, and capabilities of external sensors that may be made available to them; and, 

10 
troop leading procedures at the company or troop level, which includes reconnaissance and 

surveillance planning. 

With these stipulations on prerequisite skills, the training design could concentrate on

tasks more directly related to the See First Quality. Target recognition tasks would also be 

trained separately, though BCV 101 would reinforce this pre-training through the exercises. The 

exact nature of the prerequisite tasks for BCV 101 was not delineated; that information would 

best be determined as a byproduct of prototype experiments with the training. 

 Critical to understanding the training design for BCV 101 are the embedded training 

requirements that initiated and guided the research effort (DA, 2003b). The FCS embedded 

training requirement is that the participant can conduct training on an actual operational system. 

For the target training audience, an FCS company command group, the requirement is for 

training available in their C2

 vehicle and delivered via their C2 system. Embedded training 

should also provide high- fidelity and performance-based training. For high fidelity, the training 

mode of BCV 101 should look and perform like a C2 system in the operational mode. Training 

should also provide a performance medium, namely virtual simulation, which accurately models 

the interactive conditions, behaviors, and outcomes associated with networked sensors. 

Exercise Design 

Based on the BCV 101 training framework in Appendix B, a sample of 20 exercises on 

networked sensors were designed and developed in a COFT-like training matrix. Design focused 

on conceptual skills, in part because the exact configuration of future C2

 systems and FCS sensor 

networks are still evolving. Design also addressed a limited set of procedural skills to employ 

sensors, but basic procedural operations were identified as a prerequisite for BCV 101 training. 

The sample exercises were designed to address technical and tactical aspects of sensor 

exploitation. To train conceptual skills, the exercises required more than passive viewing of 

sensor outputs under different conditions, but did not require participants to procedurally select 

sensor systems, input sensor routes, or flight patterns, or designate a search area. Rather, the 

primary task for the participant was recognition and identification of the sensor images received 

during the exercise. A secondary task was to relate the sensor information received to the 

commander’s information requirements and the tactical setting. 

Three key design decisions affected the purpose and approach for the sample set of 

exercises. First, the exercises were constrained to three air sensor systems for the initial BCV 

101 effort to limit development to a manageable segment of the domain. These were two UAVs, 

the Shadow 200, and A160 Hummingbirds with SAR or MTI radar sensors, and four 

microUAVs that provided camera-based sensor images. Second, sensor capabilities and 

limitations were based on the sensor models incorporated in the C2

 system developed by DARPA 

and CECOM. Finally, the exercises were set in a digital terrain database of the National 

Training Center (NTC) at Fort Irwin, CA, that was used by the FCS C2

 program. 

In terms of covering the complete domain of sensors, terrain, and participant activity, 

these design decisions may seem severely constraining. However, in terms of demonstrating the 

11 
BCV 101 training approach, there was less need to represent the complete domain, and a greater 

need to take advantage of available resources for this initial exercise development effort. Table 2

contains basic descriptions of the 20 exercises developed during the current effort. The followon BCV 101 effort will expand the training to approximately 90 exercises and relate them to a 

Sensor Training Matrix that is only notionally depicted in Figures 2 and 3. 

Table 2 

Basic Specifications for 20 Exercises in the Sensor Training Matrix 

Exercise Task Unmanned Air Vehicle 

System(s) Target Type and Condition 

1 Confirm cooperative cueing and outputs Shadow 200, microUAV 1 Stationary Draega 

2 Confirm cooperative cueing and outputs Shadow 200, microUAV 1 Stationary GARM 

3 Confirm cooperative cueing and outputs Shadow 200, microUAV 1 Stationary BRDM-AT 

4 Identify and report, confirming SAR 

detections 

Shadow 200, microUAV 1 Stationary Draega 

5 Identify and report, confirming SAR 

detections 

Shadow 200, microUAV 1 Stationary BRDM 

6 Identify and report, confirming SAR 

detections 

Shadow 200, microUAV 1 Stationary Draega 

7 Identify and report, confirming SAR 

detections 

Shadow 200, microUAV Stationary 1 Draega, 1 GARM, 1 Darya 

8 Confirm templated enemy assembly area Shadow 200, microUAV Stationary Draegas, GARMs, Ural trucks 

9 Detect and confirm intelligence report of 

an enemy formation 

A160 (MTI), Shadow 

200, microUAV 

Moving Draegas, GARM 

10 Detect and confirm intelligence report of 

an enemy column

A160 (MTI), Shadow 

200, microUAV 

Moving Draegas, GARMs, BRDMs 

11 Detect and confirm intelligence report Shadow 200, microUAV 1 Stationary, dug-in or camouflaged Purga 

12 Confirm templated enemy positions A160 (SAR), Shadow 

200, microUAV 

Stationary, dug-in or camouflaged 2 

Draegas, 2 GARMs, 2 Purgas; 

Civilian vehicles present 

13 Confirm intelligence reports and 

templated enemy positions

A160 (SAR), Shadow 

200, microUAV 

Stationary, dug-in or camouflaged 3 

GARMs, 2 BRDM-ATs, 4 dismounted 

infantry squads; Civilian vehicles present 

14 Confirm intelligence report of companysize Assembly Area Shadow 200, microUAV 3 Stationary Draegas, 3 GARMs, 2 Ural 

trucks; Civilian vehicles present 

15 Confirm intelligence report of a moving 

company sized unit 

Shadow 200, microUAV 6 Moving Garms, 3 BRDM-ATs; Civilian 

vehicles present

16 Confirm template position of artillery unit 

in firing position

Shadow 200, microUAV Stationary, in firing position, 5 Purgas, 5 

Ural trucks 

17 Confirm intelligence report of tactically

moving mixed formation 

Shadow 200, microUAV 1 Moving Draega, 2 GARMs, 3 BRDMs 

(continued on next page)

12 
Exercise Task Unmanned Air Vehicle 

System(s) Target Type and Condition 

18 Confirm intelligence report of a tactically 

moving formation 

Shadow 200, microUAV 3 Moving BRDM-Recons, 3 Orels; Civilian 

vehicles present

19 Detect, identify and confirm multiple 

intelligence reports of stationary and 

moving enemy formations 

A160 (MTI), A160 (SAR), 

Shadow 200, 3 

microUAVs 

3 Draegas, 3 GARMs, 1 Purga, 1 Darya, 1 

BRDM-Recon, 1 BRDM-AT, 3 Ural trucks, 

3 MTLBs 

20 Detect, identify and confirm multiple 

intelligence reports of stationary and 

moving enemy formations 

A160 (MTI), A160 (SAR), 

Shadow 200, 3 

microUAVs 

3 Draegas, 2 GARMs, 1 Purga, 1 SA-13, 2 

Daryas, 3 BRDM-Recons, 1 Ural truck 

Note: Under “Target Type and Condition”, the named systems are all former Soviet/Warsaw Pact style vehicles or platforms 

and developments of them – BRDM-variants, Draegas (tanks), Daryas (artillery), GARMs (IFVs), MTLBs, Purgas, SA-13s, 

Orels, and Ural trucks. “microUAV” = micro unmanned air vehicle. “A160” = Hummingbird unmanned air vehicle. “SAR” = 

synthetic aperture radar. “MTI” = moving target indicator. 

The training matrix in Table 2 begins with single stationary targets and progresses to 

situations with multiple moving targets. Participants are first faced with a basic series of short 

tasks to determine what is at the forward edge of the battle area and in near contact with friendly 

forces. Subsequent exercises require the participants to observe the battle area in depth including 

multiple moving targets. All target type conditions are set to require participants to recognize 

and identify potential threats on the battlefield. Overall, the matrix represents a COFT-like 

progressive system of increasing difficulty and complexity in structured, repeatable training. 

The specifications in Table 2 contain information on three key sensor related parameters: 

task, sensor system(s), and target type/condition. Additional parameters, including named areas 

of interest and enemy locations (grid coordinates) are specified in Appendix C. As might be 

expected, the sensor characteristics available in the prototype C2 system were also key in the 

design of the sample exercises. In contrast, the available terrain database from the NTC was not. 

A developer could situate similar exercises in the mountains of Afghanistan, built-up areas in 

central Europe, or jungles in Southeast Asia. Design adaptations to other terrain should occur, as 

other digital terrain databases are available for future efforts. 

Exercise Development 

Exercise development used a prototype C2

 system developed by the FCS C2 program for 

exploring FCS command and control issues. Notably, the FCS C2

 system used for BCV 101 is 

not the actual C2

 system currently under development for FCS. However, the FCS C2 prototype 

represents DARPA’s unique and leading-edge approach to the command and control of manned 

and robotic/unmanned elements for FCS. The FCS C2

 prototype system, referred to hereafter as 

the C2

 system, reflects the commander-centric and networked sensor focus required for the 

Future Force to See First. Coupled with virtual simulation models, the C2

 system represents the 

FCS interdependent system-of-systems concept and a high fidelity environment for developing 

embedded training on networked sensors. 

Figure 4 provides an overview illustration of the C2

 system in a surrogate C2 vehicle, as 

configured for the FCS C2 

program. The four participant command group members in the 

foreground are identically equipped with a dual monitor interface to the C2

 system. The C2

13 
system at Fort Monmouth for the FCS C2 

program comprised over 20 separate computer servers 

operating in a network to support the command group. For BCV 101 training development, a 

scaled-down version of the C2

 system, with 12 servers and one dual monitor interface, was 

developed by CECOM and provided to ARI at Fort Knox. 

Figure 4. Command group participants in a surrogate command and control (C2

) 

vehicle. 

Description of the Command and Control (C2

) Interface 

This section provides a brief description of the prototype C2

 system with a focus on 

individual and networked sensors for BCV 101 training development. 

Figure 5 provides a sample display on the C2

 interface. Each C2 workstation affords dual 

displays which are identical and redundant. However, each display can be independently 

configured by the user to support C2 requirements for different duty positions as well as user 

preferences. For the FCS C2

 program, the C2 interface provided a near real-time common 

operational picture of the battlefield situation across the command group, such as the Deliberate 

Attack mission against an enemy battalion shown in Figure 5. The C2 interface served as the 

primary means by which the command group performed basic C2 functions. For BCV 101, the 

C2 interface provides participants and training developers the ability to task sensor platforms and 

receive information collected from a mix of individual and networked sensors. 

14 
Target

Catalog

Image

Viewer

Resource

Availability

Battlefield

Assistant

Tactical Map with Unit Cell in Deliberate Attack Against Red Battalion (Open Terrain)

Target

Catalog

Image

Viewer

Resource

Availability

Battlefield

Assistant

Tactical Map with Unit Cell in Deliberate Attack Against Red Battalion (Open Terrain)

Figure 5. Sample view of the command and control (C2) interface. 

 The C2

 system was developed to be compatible with virtual simulation, namely 

Distributed Interactive Simulation (DIS). Virtual simulation allows users to interact with 

elements of the friendly force, the threat force, civilian entities, and a digital terrain data base. 

The Army has determined its principal form of virtual simulation will be One Semi-automated 

Forces (OneSAF) Objective System (OOS). Currently, the C2

 system is compatible with 

OneSAF Testbed Baseline (OTB) with plans to migrate to OOS when fielded. 

 The C2

 system was developed to be compatible with virtual simulation, namely 

Distributed Interactive Simulation (DIS). Virtual simulation allows users to interact with 

elements of the friendly force, the threat force, civilian entities, and a digital terrain data base. 

The Army has determined its principal form of virtual simulation will be One Semi-automated 

Forces (OneSAF) Objective System (OOS). Currently, the C2

 system is compatible with 

OneSAF Testbed Baseline (OTB) with plans to migrate to OOS when fielded. 

As indicated in Figure 5, the C2 system provides a relatively standard interface design 

composed with windows, icons, menus, and pull downs (WIMP). The four open windows on the 

tactical map shown in Figure 5 indicate some of the C2

 system’s advanced functionality. 

Overall, these and numerous other tools and features could be opened, configured, and used 

through either display at the C2 workstation. Features available from the four open windows are 

briefly described below: 

As indicated in Figure 5, the C2 system provides a relatively standard interface design 

composed with windows, icons, menus, and pull downs (WIMP). The four open windows on the 

tactical map shown in Figure 5 indicate some of the C2

 system’s advanced functionality. 

Overall, these and numerous other tools and features could be opened, configured, and used 

through either display at the C2 workstation. Features available from the four open windows are 

briefly described below: 

• Battlefield Assistant provides information regarding various alerts that participants 

could tailor to their information requirements and activate. For example, alerts could 

signal the participants when a previously identified and tracked friendly, enemy, or 

neutral vehicle had crossed a phase line or entered a named area of interest (NAI). 

• Battlefield Assistant provides information regarding various alerts that participants 

could tailor to their information requirements and activate. For example, alerts could 

signal the participants when a previously identified and tracked friendly, enemy, or 

neutral vehicle had crossed a phase line or entered a named area of interest (NAI). 

• Image Viewer displays target images for human target recognition (HTR) and battle 

damage assessment (BDA). Image Viewer allows participants to input or revise the

information associated with icons on the map including affiliation (e.g., friendly, 

enemy, neutral), type (e.g., tank, artillery), and status (e.g., suspected target, dead 

target). 

• Image Viewer displays target images for human target recognition (HTR) and battle 

damage assessment (BDA). Image Viewer allows participants to input or revise the

information associated with icons on the map including affiliation (e.g., friendly, 

enemy, neutral), type (e.g., tank, artillery), and status (e.g., suspected target, dead 

target). 

15 
• Target Catalog allows participants to input or revise information associated with 

enemy targets including what friendly sensor(s) identified the target, when the target 

was identified, and the location and route of the enemy target. 

• Resource Availability is used to access and revise information on friendly Unit Cell 

assets including operational status, available fuel, and current speed and location. 

Certain tools in the C2

 interface enable the tasking and re-tasking of individual and 

networked sensors for information collection, presentation, and decision-making. Figure 6 

provides a sample of sensor tasking information entered on the C2

 interface including a Restricted 

Operating Zone (ROZ), UAV routes, and NAIs. The ROZ defines where UAVs may fly without 

requiring airspace coordination with other units also conducting flight operations. The routes 

depict UAV paths to and through the NAIs. The NAIs serve to focus intelligence assets into a 

limited area where enemy elements are suspected, predicted by enemy doctrine, or previously 

reported. Although Figure 6 and other interface figures may appear here in black and white, the 

C2

 interface uses colors to depict routes, areas, and sensor platforms. Color codes consistently 

relate sensor platforms, routes, and coverage areas to help differentiate sensor taskings. 

Many of the graphic control measures for the C2

 interface are “smart” graphics. For 

example, the C2

 system encodes the NAIs entered by the user to coordinate and control sensor 

taskings, such as a microUAV tasking across a series of NAIs. Similarly, the C2

 system supports 

the user by automatically checking ROZ-to-NAI visibility, or estimating the time required for 

SAR coverage of an NAI, and then providing automated feedback for user approval or 

adjustment. 

The lower portion of Figure 6 depicts a tasking matrix, by sensor platform and time, to 

help manage sensor taskings and avoid conflicts. The four tasking bars shown in Figure 6 

provide information on tasks entered for the Shadow 200 and three microUAVs. The tasking 

matrix combined with the sensor graphic control measures show how the coverage provided by 

each sensor supports and complements that of the other sensors. This may mean that different 

sensors, possibly on different platforms, will provide dual or overlapping coverage of the same

area to detect and confirm the presence of enemy elements at a particular location. 

 The C2 system automatically updates the common operational picture across all C2 

interfaces. For example, sensor taskings or control measures entered by any user are 

automatically shared with all other users. Any user can modify another user’s inputs to include 

re-tasking another’s tasks. Users can also save their interface inputs into files, sometimes called 

planning files. Planning files may be routinely stored on the system and later used to initiate a 

new plan or modify an old plan. 

Of special interest, the planning files can be used to animate the plan providing users a 

preview or rehearsal of the plan. With animation, sensors and other friendly platforms move 

across the battlefield at user-defined speed to provide a visualization of the plan, including a way 

of checking total coverage of assigned reconnaissance objectives. Plan animation does not link 

to an OTB simulation of threat forces, and animated friendly elements will not detect or act on 

enemy elements. When downloaded to OTB, however, planning files can be used to initialize a 

virtual simulation exercise in which the friendly force elements interact with enemy elements. 

16 
Operating Recon Platforms 

and Scheduled Activities

Named Areas of Interest (NAIs) 

for UAV Observation

Unmanned Air Vehicle 

(UAV) Routes

Restricted Operating Zone (ROZ) for Airspace 

Deconfliction of Manned Aircraft and UAVs

Figure 6. Command and control (C2

) interface with sensor tasking information. 

Figure 7 shows a snapshot view of animation during a typical BCV 101 exercise with 

microUAVs en route or on-station at their assignments, waiting cueing from Shadow 200 

detections. In animation mode, entity taskings can be run faster than real-time, repeated, 

adjusted, and saved for later use or sending to others. 

For the FCS C2

 program, multiple courses of action for a mission were planned, saved, 

and then played in animation to aid decision-making. The final course of action plan file was 

adjusted prior to execution based on any incoming new enemy intelligence and then downloaded 

to OTB to begin mission execution in virtual simulation. 

17 
Unmanned Air Vehicle (UAV)

En-route to a Designated Named 

Area of Interest (NAI)

UAV Operating Inside a 

Designated Restricted 

Operating Zone (ROZ)

Subsequent UAV NAIs Tasked for Continuous 

Recon of the Area of Operation (AO)

Figure 7. Animation snapshot with microUAVs en-route or on-station awaiting next 

tasking from Shadow 200. 

Construction of the Sample Exercises 

This section describes the research team’s efforts to construct sample exercises, or

visualization tasks, for the BCV 101 training program. Based on a COFT-like training model, 

the goal was to develop a small set of progressive exercises designed under controlled and 

deliberate task conditions with automated feedback relating task performance to standards. 

Realistically, the research team expected this initial effort at exercise development would help to 

identify and address, at least partially, some key technical challenges in developing BCV 101 

training and embedding training, as required by FCS (DA, 2003b). 

Accordingly, the 20 exercises in Table 2 were constructed in a manner compatible with 

the prototype C2

 system and OTB virtual simulation. Overall, exercise development was a 

promising start on the extended process of developing a working example of embedded training. 

However, technical limitations in the C2

 system and OTB required workarounds that fell short of 

18 
the BCV 101 training design and embedded training objectives, as discussed in this section. The 

Training Implications section that follows provides supplementary discussion and summarizes 

lessons learned on technology dependent training. 

Train-the-Trainer 

Several train-the-trainer sessions preceded exercise development. The BCV 101 research 

team based at Fort Knox, KY, traveled to Fort Monmouth, NJ, in September 2003 to observe two 

days of participant training during Experiment 4A under the FCS C2

 program. Coordination 

with DARPA and CECOM on the BCV 101 effort was conducted during the visit. The need for 

a scaled-down version of the C2 system at Fort Knox for BCV 101 was identified, and a scaled 

C2 system was delivered to ARI at Fort Knox in December 2003. During its installation, two 

days of training were provided by a CECOM support team. The first training day was directed at 

technical support of the scaled C2 system. On the second day, instruction focused on sensor 

capabilities and exercise development issues of concern to the BCV 101 research team. 

For the next several weeks, the primary training developer for BCV 101 engaged in 

hands-on learning with a focus on developing the initial set of 20 exercises. During this time, 

many of the necessary procedures to program and manipulate the C2 system for exercise 

development were identified and established. Key procedures included how to adjust sensor 

locations, routes, and observable areas with respect to enemy locations in order to ensure sensor 

feedback in the time allocated for each exercise. As technical limitations were encountered, the 

training developer also started to identify potential workarounds in an effort to meet BCV 101 

design requirements. 

Major Technical Limitation 

Exercise development quickly surfaced some key training advantages and disadvantages 

with the C2

 system and OTB. Most technical limitations were surmounted by workarounds. 

However, the prolonged time to shift from one exercise to the next exercise forced a re-design 

described below. Future development efforts should address this limitation, as discussed in the 

Training Implications section. 

By design, short (5-10 minute) exercises were to provide progressive, part-task training 

with deliberate practice opportunities and direct, immediate feedback. At an average of six 

minutes per exercise, including a minute post exercise feedback, it was estimated the 20 sample 

exercises could be completed in two hours. By comparison, COFT training time is allocated in 

one/two-hour slices for reasons applicable to BCV 101 training. Rationales for the COFT time

slices include training principles, such as avoiding diminishing returns from individual and crew 

inattention, and practical concerns such as maximizing the throughput of individuals and crews 

for COFT required training. 

However, the time to initialize the next BCV 101 exercise was almost equivalent to the 5-

10 minutes allotted per exercise, based on multiple trials by the primary training developer. The 

time to start the next exercise was primarily attributed to OTB re-initialization requirements. 

Starting the next exercise required bringing down and then re-starting or re-initializing the two 

19 
OTB servers representing the friendly and enemy elements in the upcoming exercise. Moreover, 

the trainer was required to perform an exacting and time-consuming set of computer procedures 

to re-initialize the next exercise. The trainer also needed to conduct a post-exercise feedback 

session or after action review (AAR) of the just-ended exercise with one or more participants. 

In sum, the total time from start of one exercise to start of the next was estimated at not 

less than 15 minutes, given the current C2

 system and OTB configuration. The addition of a 

second support person to take down and bring up the configuration between exercises might 

lessen the total exercise time to 10 minutes, for the shortest exercises. The estimated costs in 

time and manpower were considered unacceptable for BCV 101 or future embedded training 

programs, so an exercise bundle approach was adopted to continue exercise development. 

Exercise Bundles 

A compromise to the BCV 101 training design was devised to temporarily mitigate the 

time problem in starting a new exercise. A compromise solution sorted the 20 individual 

exercises into five exercise bundles, as specified in Appendix C. The bundle approach required 

crafting the individual exercises into a series of interrelated exercises. It also required 

developing and providing to the user a more overarching tactical framework to relate the 

exercises within each bundle. The use of bundles did reduce the number of times required to reinitialize OTB and the C2

 system during a training session. 

It was estimated that participants could complete even a relatively large bundle of 6-8 

exercises and a post exercise feedback/coaching session in about one hour. Exercises within a 

bundle comprised a related series of sensor tasks. However, the use of exercise bundles versus 

discrete exercises weakened the link between tasks, conditions and standards for each exercise, 

limited repeated practice opportunities, and complicated proficiency-based training progression. 

Exercise Overview

The basic design for each exercise paired a Shadow UAV for early detections with a 

microUAV for subsequent recognition and identification by the participant. More complex 

exercises added an A160 UAV platform for additional detections, and multiple micro UAVs for 

additional recognition and identification tasks. 

Initial exercises addressed individual sensors targeted at more detectable enemy entities, 

such as single, stationary, wheeled or track vehicles in open terrain. The initial exercises were 

also designed to provide a basic understanding of the C2 interface or tactical display, particularly 

sensor related data and information. Intermediate exercises targeted less detectable enemy, such 

as dismounts in more closed terrain, or enemy vehicles mixed with civilian vehicles. Advanced 

exercises included a combination of less detectable entities, more sensors, and more exacting 

recognition and identification tasks. 

All exercises provided simulation-based feedback on the process and outcome of sensor 

employment during the exercise. Process feedback included visual depiction of dynamic sensor 

footprints or coverage areas, and outcome feedback included automated alerts and data on sensor 

20 
detections and sensor images received. Feedback after exercises was provided by the trainer 

rather than the C2 system, as discussed in the Training Implications section under Performance 

Feedback. 

In sum, the 20 exercises were divided into five exercise bundles with 3-7 exercises of 

progressive difficulty per bundle. Time required to complete each bundle was estimated at 30-60 

minutes to allow enough time for sensor detections, sensor cross-cueing, sensor images received, 

participant assessment of sensor images, and feedback to the participant. More detail on the 

sample exercises is provided in Table 2 and Appendix C. 

Development Process 

 Development required iterative refinement of draft exercises and bundles to match the 

BCV 101 training objectives, to the extent possible. A valuable resource for development is the 

plan animation tool and the ability to store and modify draft exercise files, as described earlier in 

the section C2 Interface Description. The training developer could repeatedly review draft 

exercises in the animation mode to check visually the sensor taskings for individual or networked 

sensors including assigned routes and coverage. Visual review of sensor coverage is facilitated 

by the C2 system’s ability to display sensor fans or “footprints” as the sensors move along the 

routes assigned. In addition, the training developer could input the known location of enemy 

elements for an exercise directly on the C2

 interface’s tactical map to supplement visual review 

of sensor coverage. 

During the next phase of development, the training developer verified sensor coverage 

with automated feedback from OTB’s virtual simulation. To “run” the exercise, the training 

developer downloaded the friendly exercise file to OTB from the C2

 interface and initialized a 

complementary OTB enemy file previously built to match the threat task conditions. If the 

exercise files for friendly and enemy elements were correct, this simulation-based “run” for the 

exercise bundles would result in early detections by larger sensor platforms followed by the 

expected sensor images from microUAVs. If not, the training developer would iteratively revise 

the draft exercise files until task conditions for the exercise were correctly set, and then save the 

final exercise or exercise bundle. 

Additional detail on the exercise development process is provided in the following 

paragraphs for BCV 101 documentation, and potential use in other training development efforts. 

Exercise development deliberately positioned the enemy array to ensure it could be detected by 

the air sensors in the time allotted for the exercise, or series of exercises within a bundle. The 

NAIs were used as a control mechanism to focus the reconnaissance effort in accordance with 

doctrinal guidelines. Developmental review of the exercises indicated the NAIs should be 

limited to four square kilometers in area, given the time allotted per exercise. 

For each exercise bundle, the radar capable Shadow 200 UAV or the A160 were carefully 

positioned with respect to pre-identified ROZs and NAIs. At the start of each exercise, these 

radar sensor platforms would move to the areas assigned and begin their search. As noted, the 

MTI and SAR sensor systems onboard these platforms designate the locations of detected 

elements on the battlefield. These detections automatically generate “unknown” icons on the C2

21 
interface, as shown in Figure 8. They also generate complementary warnings and messages 

respectively in the Battlefield Assistant and Target Catalog windows shown in Figure 5. 

SAR detections from a Shadow 200

(enhanced to show detections clearly)

SAR detections from a Shadow 200

(enhanced to show detections clearly)

 Figure 8. Sample of synthetic aperture radar (SAR) detections.

Exercise development stressed that an integrated reconnaissance plan, even at the 

individual exercise or task level, should skillfully balance and complement the capabilities of the 

available sensor assets. For example, the radar equipped sensors onboard the Shadow 200 and 

A160 platforms covered far larger areas of the battlefield faster than the microUAVs. In fact, the 

MTI footprint was larger than the entire area of operations assigned to the company level 

participants in the FCS C2 experiments and the participants anticipated for BCV 101. The SAR 

footprint was substantially smaller than MTI, but it could sweep across designated NAIs much 

faster than a microUAV.

A tactics, techniques, and procedures adapted for BCV 101 from the FCS C2 participants 

was to task the microUAVs based on radar detections. This TTP provides an example of 

22 
exploiting automatic sensor cross-cueing made possible by the sensor network represented in the 

prototype C2 system. Such TTPs are particularly appropriate with microUAVs that provide only 

snapshot images versus streaming video, as currently modeled by DARPA for the FCS C2

program to avoid potentially unrealistic outcomes. In sum, the microUAV models available 

were slow movers with a small “soda straw” footprint that returned only snapshot images taken 

at designated locations. 

After reaching the designated location of a detected element, microUAVs begin 

automatically to return snapshot images at that location. An example of the raw output from a 

microUAV, a good sensor image of a stationary enemy tank, is shown in Figure 9. Moving 

targets are particularly difficult to capture in a microUAV snapshot image, in part because the 

entity may have moved from the designated location used to trigger the snapshot. Exercise 

development positioned the microUAVs sequentially to provide pictures of all the radar 

detections in a particular NAI. As the exercises progressed in difficulty, including moving 

entities, the quality of the images returned by the microUAVs often dropped appreciably. 

Thermal Picture and Identification Pop-up Menu for Verification; 

Linked to the Tank Shown in the Left-Center of the Display

Figure 9. Command and control (C2

) interface with microUAV sensor image. 

23 
The primary task of the participant during the 20 exercises was to examine and evaluate 

the raw sensor output from the microUAVs. Recognition and identification of the images 

received by the participant included: indicating the enemy/friendly status of the entity, vehicle 

type, and vehicle status as well as assessing any battle damage depicted, and associating the 

image to a templated (if any) enemy position. 

Training Implications

This section briefly reviews some key implications on technology dependent training that 

apply to the BCV 101 effort, and may apply to the Army’s ongoing development of embedded 

training for FCS. More extensive reviews of training implications for FCS are available from

ARI (e.g., Campbell, Throne, Black, & Lickteig, 2003; Carnahan, Lickteig, Sanders, & Durlach, 

2004; Throne & Burnside, 2002). Table 3 summarizes the training implications identified and 

discussed in the remainder of this section. 

Table 3 

Summary of BCV 101 Training Implications 

Training Challenge 

 Accept that FCS raises warfighters’ tactical and technical skill requirements. 

 Recognize training is the glue that will hold FCS and the Future Force together. 

Training Design 

 Structure training to directly relate tasks, conditions, and standards. 

 Shift training from procedural to conceptual skills as automation emerges. 

Training Development 

 Ensure proactive collaboration between the developers of training and technology. 

 Immerse training developers in a user-based C2 system development process. 

 Provide training developers with authoring tools in C2

-based training systems. 

 Develop methods to adapt training as fast as training requirements change. 

Training Delivery

 Acknowledge the imposing technical challenges to technology dependent training. 

 Address now the requirements for tomorrow's embedded training with simulation. 

Training Feedback 

 Provide the feedback instrumental to learning; practice without feedback is insufficient. 

 Stress measurement methods for post-exercise feedback and gated training. 

Note: “FCS” = Future Combat Systems. “C2 system” = command and control system. 

FCS Training Challenge 

Training requirements for FCS-equipped Soldiers with highly automated and 

interdependent network-based systems may entail unprecedented levels of tactical and technical 

expertise (Lickteig et al., 2003). More automated systems can relieve routine tasks, but often 

raise the demand to understand the decision rules and parameters designed into the automation. 

24 
During Experiment 3 in the FCS C2 

program, for example, the Shadow UAV tended to “wander 

off” toward enemy elements under Auto Recon and be destroyed, a critical loss to the command 

group’s See First capability. This unintended consequence of high automation was due to 

misunderstood reconnaissance parameters, information overload that limited monitoring, and 

lack of an effective human override to abort an automated routine in a timely manner. 

Understanding the limits and strengths of FCS technology was a severe challenge even 

for relatively expert participants. After the FCS C2 Experiment 4, experienced participants, 

many of whom had participated in over 40 mission runs across four experiments, expressed 

strong concerns about training and their ability to exploit fully their FCS capabilities. 

Training is the glue that will hold FCS and the Future Force together. ARI’s conclusions 

on the FCS C2 research stressed basic but far-reaching recommendations to improve the training 

of individuals and units in future FCS organizations, and particularly command groups (Lickteig 

et al., 2003). The BCV 101 effort addressed, in part, one of the recommendations: Develop 

progressive simulation-based training exercises directed at individual tactical and technical skills 

and the operational consequences of automated systems.

Training Design: Structured Training 

A structured training approach develops and sustains performance proficiency and 

improves knowledge, skills, and abilities (Campbell, Quinkert, & Burnside, 2000). Structured 

training is characterized by an incremental series of exercises often situated in a simulated 

environment to provide task realism and performance feedback. The essence of structured 

training is a training approach that directly relates tasks, conditions, and standards. The BCV 

101 training design emulated a structured training approach with a set of progressive exercises 

designed under controlled and deliberate task conditions with automated feedback relating task 

performance to standards. 

The bundle approach adopted to overcome delays between exercises was a compromise 

in the BCV 101 design based on structured training. Exercise bundles weakened the close link 

between task conditions and standards, limited the opportunity for repeated practice and contextspecific feedback, and complicated development of a gate pass structure. For instance, exercise 

bundles forced the initial location of microUAVs immediately near the NAIs to respond rapidly 

to cross-cue detections and provide sensor images. As a result, the interface quickly became 

more cluttered and demanding than desired for initial exercises. Although such a challenging 

environment is anticipated for Soldiers in FCS units, a progressive approach to skill acquisition 

provides initial exercises that are less challenging. It also models the Army’s crawl-walk-run 

approach to training. 

Training Development: Technical Collaboration and Authoring Tools 

Training development for new systems is often reactive. How can you develop training 

on a system that does not yet exist? Even when initial versions of a new system are available, 

they are often so incomplete, so inadequate, that training development is a frantic mix of catch 

up and workarounds. For Experiment 3 in the FCS C2

 program, for example, 13 new features 

25 
were added to the prototype C2

 system. The new features, such as an Attack Guidance Matrix 

(AGM) that generates fires based on sensor-to-shooter networks, automated numerous timeconsuming and time-limited tasks. However, these features created new training requirements 

and they shifted the focus of training from procedural to conceptual skills, as task performance 

shifted from doing to understanding and controlling what was automatically being done. 

Proactive collaboration between the developers of training and technology is the only 

way to efficiently and effectively develop the technology dependent training required for FCS. 

Moreover, the burden is on training and tactical experts to provide to the technical experts 

identified training requirements and proposed training designs to meet those requirements. In 

important ways, this type of collaboration is ongoing with FCS system and training development 

(J. Shiflett, personal communication). However, too often the training developers are not 

adequately positioned or immersed in the system development process. 

System development for the Army is a user-based process. The FCS C2

 program

provides an excellent example of how the design of a C2

 system is best determined by expert 

users who repeatedly employ and modify a prototype C2

 system in a futuristic operational 

environment. It is also a good model of how to immerse training developers into the system

development process, into the formative dialog that shapes a C2

 system. This process model is 

particularly important for FCS embedded training in which the C2

 system also serves as a 

training delivery method. As a sample result, the training requirement for basic visualization 

skills was identified and the BCV 101 effort initiated to provide a COFT-like approach for 

training sensor exploitation. 

The BCV 101 effort also underscores the need for authoring tools and methods by which 

trainers can adapt a C2 system to meet training requirements. Training development is an 

iterative process in which the design of training programs and supporting exercises is best 

accomplished across a series of design-develop-test-redesign cycles. Moreover, the requirement 

for simulation-based training extends the need to develop training on a C2 system to include the 

supporting simulation, namely OTB and OOS. Over a decade ago, ARI developed and applied a 

powerful set of training tools compatible with an earlier C2 system prototype and OTB virtual 

simulation (Atwood, Winsch, Quinkert, & Heiden, 1994). Similar and even more powerful C2

system- and simulation-based training tools are needed for FCS training development. 

 The C2 system used for BCV 101 was an adopted system originally developed for use in 

FCS C2 research efforts, and as such did not have the tools, features, or documentation needed 

for developing the subject training program as designed. The available tools adapted to support 

training development included: the animation files for reviewing and revising draft training 

exercises, smart graphics for controlling and assessing sensor taskings, color-coded sensor fans 

or footprints for visualizing sensor coverage, and simulation-based process and outcome 

feedback on sensor deployment. 

However, the BCV 101 goal of providing perceptually augmented task conditions and 

feedback for the user/participant was constrained by technical limitations and the tools available. 

The C2 system provided no interface tools by which the training developer, or the participant, 

could readily adjust or modify the system’s current color codes or patterns for representing 

26 
sensor platforms, routes, and coverage areas. Additionally, any attempt to change color codes or

system software by the BCV 101 research team was deemed inadvisable by the technical 

developers of the FCS C2 system due to system complexity. To develop and refine training, the 

C2 system should be sufficiently modularized to permit modifications by training developers. 

Such modularization may begin by creating a “Training Mode” in the C2

 system, but a “Training 

Development Mode” is also needed. 

More responsive tools for training developers are also needed to provide simulationbased training on C2 systems, as required by FCS. Such tools are projected with the fielding of 

OneSAF (OneSAF, 2004); however, the tools currently available with OTB did not support the 

BCV 101 requirement for minimal duration and re-initialization requirements between exercises. 

The simulation system also did not support an adaptive sequencing of exercises or gates with 

decision points for subsequent levels of training. 

Training Delivery: Simulation-Based Embedded Training 

A key performance parameter (KPP) for FCS is embedded training delivered by an 

advanced C2 system compatible with OneSAF Objective System for virtual simulation (DA, 

2003b). A fundamental value of virtual simulation is that it affords participants and trainers a 

performance medium that models interactive conditions, behaviors, and outcomes. To provide 

an exemplar of embedded training for FCS, the BCV 101 effort focused on the development of 

basic visualization training delivered by a prototype C2 system linked to OTB. 

A key finding is that the technical challenges to technology dependent training are 

imposing. This is not a new finding, but it is one of increasing relevance as the Army transitions 

to FCS and the Future Force. By design, FCS poses an extraordinary alliance of humans and 

machines—an interdependence that significantly impacts the small unit and the individual 

Soldier. The impact is positive, as advertised, but potentially quite negative if the humanmachine fit is not made or fails. Training will prove essential to maintaining and sustaining a 

forceful human-machine alliance. 

However, a major technical limitation encountered in BCV 101 exercise development 

was the prolonged time required to shift from one exercise to the next exercise. The time to start 

the next exercise was primarily attributed to OTB re-initialization requirements. Starting the 

next exercise required almost the same amount of time as the conduct of the exercise, and 

required the trainer to perform an exacting set of computer procedures between exercises. 

Ideally, embedded training would provide rapid and controlled progression through exercises by 

the participant, particularly for individual training. 

Technical limitations not only impacted training efficiency with prolonged shifts between 

exercises, but also training effectiveness as envisoned in the BCV 101 design. As discussed 

under Training Design implications, exercise bundles undermined the design’s structured 

approach to training, limited practice, and complicated proficiency-based training progression. 

As discussed in the following section, providing the performance measurement and feedback 

considered instrumental to training also requires overcoming technical limitations and effectively 

leveraging technical capabilities. 

27 
Training Feedback: Performance Measurement and Perceptual Augmentation 

Process measures of learning are needed to provide the feedback instrumental to learning. 

In many real world settings, the “measured” comparison between actual and desired performance 

may be so obvious, so intuitive, that it belies any measurement requirement. In more artificial 

and complex performance settings, however, feedback is often less obvious and more difficult to 

interpret. 

During FCS C2 missions or “runs” with interdependent and highly automated 

technologies, performance feedback was too often missing and inadequate. Causes for many of 

the “problems” identified during a run were often unclear. Was it a shortcoming in the 

technology, the training, or the performers’ TTPs? The BCV 101 training design stressed the 

importance of feedback during and after exercises. A design goal was to exploit the ability of 

digital technologies, such as C2 systems and simulation, to help solve many of the training 

challenges they create. 

 Feedback during the BCV 101 exercises provided simulation-based feedback on the 

process and outcomes of sensor employment. Process feedback included visual depiction of 

dynamic sensor footprints or coverage areas, and outcome feedback included automated alerts 

and data on sensor detections and sensor images received. For example, feedback on sensor 

coverage for a specified route includes visible sensor “footprints” moving across the tactical map 

and digital “readouts” on percent of NAI covered by time for a designated ROZ. Entering nonviable routes is not permitted by the C2 system, and they automatically result in a warning notice 

to the user when selected. 

However, the prototype C2 system provided limited and not easily modified mechanisms

for improving feedback, particularly perceptual augmentation for sensor visualization and 

management. For example, visual feedback on sensor “footprints” was often almost invisible

due to indiscriminate color codes, screen clutter, and human perceptual limits. Figure 10 depicts 

several of the sensor footprints that a user can turn on/off from a window on the C2 interface. 

However, the more detailed information presented within the relatively small footprints depicted 

is hard to discern, particularly when a larger map area is displayed. As a form of perceptual 

augmentation for the reader, Figure 10 also provides adjacent zoom views of the same sensor 

footprints below the interface. 

 Feedback after the 20 sample exercises was provided by the trainer rather than the C2 

system. The C2 configuration at ARI-Knox does not currently include a data logger with the 

ability to collect and present exercise data for automated feedback. For future research on more 

automated performance measurement, a data logger can be added to the C2

 configuration at ARIKnox. Meanwhile, the current BCV 101 effort focused on the design of measurement methods 

required for effective post-exercise feedback and a gated pass structure. Currently, the measures 

designed track the participant’s recognition and identification of the sensor images received by 

enemy/friendly status of the entity, vehicle type, vehicle status, battle damage assessment, and 

relation to templated enemy position.

28 
Shadow 200 Synthetic Aperture

Radar (SAR) Visual Enhancement

Shadow 200 Daylight/Thermal

Visual Enhancement

Shadow 200 Symbol

MicroUAV Symbol

MicroUAV Daylight/Thermal 

Visual Enhancement

Shadow 200 Synthetic Aperture

Radar (SAR) Visual Enhancement

Shadow 200 Daylight/Thermal

Visual Enhancement

Shadow 200 Symbol

MicroUAV Symbol

MicroUAV Daylight/Thermal 

Visual Enhancement

 Figure 10. Visual enhancements of sensor coverage and footprint areas. 

Conclusions 

Training developers have long called for an integrated training and operational system. 

The concept may become a reality as the Army addresses embedded training as a “no kidding, no 

excuses” requirement for FCS (DA, 2003b). The goal of embedded training underscores the 

need for Soldiers to train as they fight in their operational systems. Coupled with simulation, 

embedded training also reinforces the Army’s commitment to performance-based training. 

Achieving the Army’s goal of embedded training is a formidable task. It requires no less 

than a transformation in training that will occur only by sustained and cumulative effort. The 

concept of truly embedded training will only be realized if training developers and system

developers work on a peer-to-peer basis from the initial conceptual design stages. Proactive 

research on the ways embedded training can and should be delivered will pave the way to the 

Future Force. Providing embedded training designs and programs at the earliest stages of system 

design is a training development requirement. 

29 
Training and tactical experts bear the burden for communicating to system builders the 

training designs that are relevant and realistic for C2

 systems in a virtual environment. Important 

lessons learned, ideas, critiques, and questions will not be addressed without early and sustained 

communication on substantive findings. Training and operations are merging, just as virtual and 

real worlds are, to support the Future Force. Having a short operations-to-training modification 

feedback loop, will best support the Future Force and meet the Quality of Firsts. 

Overall, the BCV 101 effort provides a promising start on the extended process of 

developing a working example of embedded training to help meet the See First requirement. The 

design of the training program and exercises combines theory and technology to address an 

important and unmet training objective of the Future Force. One of the key products of this 

effort was the prototype training design for BCV 101 as an exemplar for FCS embedded training, 

but that design alone is incomplete. Results from the BCV 101 effort stress the need for C2

training systems to support such designs, systems that are rapidly configurable and focused to 

provide reinforcement of operational concepts and needs. 

Based on lessons learned, the BCV 101 follow-on effort will extend and refine the 

training design and exercises required for networked sensor exploitation to meet the See First 

requirement. Future research should generate user-sanctioned embedded training designs on 

networked sensors through external evaluations. More generally, the BCV 101 follow-on effort 

should help forge the needed link between training development and C2

 system development to 

meet Future Force training requirements. The training objective for BCV 101 remains clear: 

Current Force small unit commanders have little experience with networked sensors on a regular 

basis – with the right training, their Future Force counterparts will routinely exploit them. 

30 
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32 
Appendix A 

Acronyms 

AAR After Action Review 

AGM Attack Guidance Matrix

AO Area of Operation 

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

BCV Battle Command Visualization 

BDA Battle Damage Assessment 

C2 Command and Control 

C4ISR Command, Control, Communications, Computers, Intelligence, Surveillance, and 

Reconnaissance 

CCIR Commander’s Critical Information Requirements 

CECOM Communications-Electronics Command 

COFT Conduct-of-Fire Trainer 

COP Common Operational Picture 

CSE Commander Support Environment 

DA Department of the Army

DARPA Defense Advanced Research Projects Agency 

DIS Distributed Interactive Simulation 

EO/IR Electro-Optical/Infrared

FBC Future Battlefield Conditions 

FCS Future Combat Systems 

FM Field Manual 

HPT High Pay-Off Target 

HTR Human Target Recognition 

IFV Infantry Fighting Vehicle 

INFOSYS Information Systems 

ISR Intelligence, Surveillance, and Reconnaissance 

KPP Key Performance Parameter 

LMS Learning Management System

A-1 
M&D C2 Multi-Cell and Dismounted Command and Control 

METT-TC Mission, Enemy, Troops, Terrain, Time, and Civilian Considerations 

MTI Moving Target Indicator 

NAI Named Area of Interest 

NTC National Training Center 

OneSAF One Semi-Automated Force 

OTB OneSAF Test Bed Baseline 

OOS One Semi-Automated Forces (OneSAF) Objective System

PEO Program Executive Office 

PIR Priority Intelligence Requests 

POI Program of Instruction 

R&S Reconnaissance and Surveillance 

ROZ Restricted Operating Zone 

SAR Synthetic Aperture Radar 

STO Science & Technology Objective 

STRI Simulation, Training and Instrumentation 

TRADOC U.S. Army Training and Doctrine Command 

TTP Tactics, Techniques, and Procedures 

UAV Unmanned Air Vehicle 

UGS Unmanned Ground System

UGV Unmanned Ground Vehicle 

WIMP Windows, Icons, Menus, and Pull Downs 

A-2 
Appendix B 

Prototype Program of Instruction 

B-1 
Battle Command Visualization 101: 

PROTOTYPE PROGRAM OF INSTRUCTION 

Introduction 

Purpose The U.S. Army has a future force battle command goal of enabling dominant 

maneuver through combined arms operations. Dominant maneuver requires that 

future leaders and Soldiers be able to “see first” which requires, among other things, 

the application of technologically superior sensor capabilities to a well thought-out 

reconnaissance and surveillance (R&S) plan. The purpose of this prototype training 

program is to provide small unit commanders the technical and tactical skills to 

employ and exploit networked sensors and help meet the “see first” objective. This 

training program provides basic or introductory training as the first requisite to 

battle command visualization (BCV). Visualization in a battle command context is 

the common operational picture (COP) presentation of knowledge derived from 

information and intelligence that leads to an understanding of the situation. 

Background Future battle command will be executed through a multi-layered, robust command, 

control, communications, and computer (C4

) network that is enabled by multiplatform/multi-functional intelligence, surveillance, and reconnaissance (ISR) 

systems. These ISR systems will include space-based, airborne, and ground-based 

sensors mounted on manned and unmanned systems. It is anticipated that all 

combat units will have organic sensors as well as the ability to receive information 

from other external sensors. Future leaders employing these sensors must quickly 

be able to make sense of the information that is being provided to them, especially if 

they are viewing the raw information output of these systems before it is fused with 

other sources of information and presented as part of the COP. They also need to 

understand the limitations and vulnerabilities of these systems so that they do not

have unrealistic expectations about sensor performance. By fully exploiting the 

capabilities of organic and external sensors and compensating for their limitations 

and vulnerabilities, future leaders and Soldiers will be able to “See First” which will 

allow them to begin to understand the enemy’s future actions and what they will 

need to do to defeat those actions. 

Future leaders and Soldiers will have much of their battle command training 

delivered through their C4 and ISR systems that have a training environment, 

performance support systems, and automated performance feedback embedded 

within them. The training environment will seamlessly integrate live, virtual, and 

constructive training that supports the training of future leaders on demand, 

anywhere or anytime. 

B-2 
Introduction, continued

Approach This BCV 101 instruction will be delivered by interactive courseware that is 

embedded into the FCS Command and Control (C2

) and R&S training environment 

or other media. The sequencing of the training anticipates that participants first 

understand the capabilities of the command and control system or sensor operator 

control unit in which this training will be embedded, as well as the operational 

characteristics and limitations of their unit’s organic R&S sensors and the enemy’s 

capability to either destroy or deceive the sensor. The participants will then 

participate in individual instruction delivered by interactive courseware that 

contains all of the materials required for the learner to master the included lessons. 

They will learn and practice the needed skills, building from basic to more complex 

requirements. Feedback will be provided in the context of the learned material. 

Sometimes, it will be presented immediately during exercise performance; at other 

times, feedback will be delayed until after an exercise to permit the learner to 

identify performance deficiencies. 

The program follows the structured training approach, wherein all exercises have

clearly defined tasks, conditions, standards, and specific feedback. 

Training 

description 

This POI describes a set of four training modules. This individual training is 

directed at Soldiers in the Company or Troop Command Group. The modules are: 

• Module 1: Sensor Capabilities, Limitations, and Vulnerabilities 

• Module 2: Sensor Tasking 

• Module 3: Dynamic Sensor Retasking 

• Module 4: Sensor Integration into Tactical Operations

Training 

participants 

The primary training audience is the Company or Troop Headquarters which may 

include: the Commander, Executive Officer, First Sergeant, Platoon Leaders, 

Platoon Sergeants, and the Fires and Effects Non-Commissioned Officer. 

Additional training audience participants may include brigade and battalion 

commanders, staff members, and future force leaders who plan R&S operations or 

view raw sensor outputs. Robotics or Sensor Operators assigned to the Company or 

Troop Headquarters are not considered part of the training audience for this 

program since they will have received comparable training during their advanced 

individual military occupational specialty qualification course. 

Training 

prerequisites

Members of the Company or Troop Headquarters are expected to be proficient in 

the operation of the Future Combat System (FCS) Battle Command system in which 

this training program will be embedded. They should also be familiar with their 

unit’s tables of organization and equipment, including the types and numbers of 

organic sensor systems; the types, numbers, and capabilities of external sensors that 

may be made available to them; and troop leading procedures at the company or 

troop level, which include R&S planning. 

B-3 
Introduction, continued

Training 

objectives 

Specific training objectives for the levels are shown in the separate sections of this 

Program of Instruction (POI) which address each one in detail. The desired end 

state for the training includes several overarching goals. The training audience 

members will be able to: 

• Demonstrate the capabilities, limitations, and vulnerabilities of their unit’s 

organic R&S sensors, as well as the external sensors that may be made 

available to them; 

• Given an enemy target set, select the appropriate sensor or sensor payload to 

employ based on weather, terrain, and other information; 

• Adjust sensor employment strategies based on analysis of sensor outputs, other 

ISR cues, and the tactical situation; and 

• Plan the employment of the unit’s organic R&S sensors, using the FCS C4ISR 

system to meet the commander’s priority intelligence requirements. The plan 

should be synchronized with higher headquarters’ R&S plans to exploit the 

information provided by external sensors. 

Hours of 

Training Time 

(Estimates) 

TBD – The amount of time required to execute this POI will depend upon the 

project team’s analysis of the developed full set of training levels after it has been 

implemented in the DARPA/CECOM FCS C2 Training Environment at ARI-Fort 

Knox.

Overview of the Training Modules 

General The modules have been designed using the M1 Tank Conduct-of-Fire Trainer 

(COFT) as a model. Like the COFT, the BCV 101 POI places the participants in a 

realistic tactical situation and presents them with a full range of simulation-based 

exercises that vary in target type and number, target motion, visibility, and other

complex conditions. The result will be challenging, progressive visualization 

training. 

The training participants are expected to have already completed their individual 

training on the operation of the FCS C2 system. It is also anticipated there will be 

an embedded Learning Management System (LMS) to track Soldier participation 

and performance during the training. The program has also been designed so that an 

instructor will not be required to facilitate the training. Intelligent tutors will be 

embedded into the interactive courseware to the extent that technology supports this 

concept. Participants will also have access to an internet portal that will allow them

to reach sensor subject matter experts for assistance if they need additional support 

beyond that provided by the courseware. 

All training will be done in a tactical context with the participants working with 

R&S tasking matrices and overlays appropriate to their organization. They will also 

be provided the CCIR and PIR upon which the unit R&S plan is based. Where 

required, they will also be provided with intelligence summaries and IPB products 

such as a Modified Combined Obstacle Overlay to assist them in determining the 

appropriate sensor to use for a given condition.

B-4 
Overview of the Training Modules, continued

BCV 101 

Training 

Exercises 

The BCV 101 POI, when fully developed, contains a structured training exercise 

library designed to train visualization skills. The following figure represents the 

potential structured exercises library that could be created for BCV 101. 

Figure B-1. BCV 101 Training Exercise Matrix. 

The number of exercises that would be created depends on a number of factors. For 

example, there are 13 types of future force sensors, a minimum of two weather types 

(optimal, sub-optimal), three types of terrain (open, close, urban), 10 target 

conditions (based on type, number, activity, and countermeasures), and two types of 

cues (detected, predicted). This could result in a potential library of 1,560 

structured exercises if just a single version was created for each possible 

combination of factors. 

Training 

Progression 

After completing a series of progressively more complex exercises in a module, the 

participant will be required to successfully complete a gate exercise that will test the 

participant’s mastery of the material. If the participant successfully completes the 

exercise, the BCV 101 training program software will pass the qualification score to 

the LMS and allow the participant to continue with the training. If the participant 

does not successfully complete the gate exercise, the BCV 101 training program

software will determine the areas in which the participant needs additional training 

and will present additional training exercises for practice. Once the participant has 

completed those exercises, the BCV 101 training program will again present a gate 

exercise. If the participant does not successfully complete the second gate exercise, 

then the BCV 101 training program software will alert the BCV 101 training 

coordinator who will analyze the participant performance and recommend 

additional training. 

Weather

Terrain

Target

Cue

Sensor Type

Training

Module

Weather

Terrain

Target

Cue

Sensor Type

Training

Module

Weather

Terrain

Target

Cue

Sensor Type

Training

Module

B-5 
Training Modules Specifications 

Module 1: 

Sensor 

capabilities, 

limitations, and 

vulnerabilities 

Purpose: To train the participants on their unit’s organic sensor capabilities, 

limitations, and vulnerabilities, as well as the external sensors that may be made 

available to them. 

Approach: The participants will log into the LMS embedded into the FCS C2 

system. Based on duty position, they will then be directed to the interactive 

courseware for their unit’s organic sensor system type for the rest of the training. 

Initially, the participant will be given a pre-test to determine his or her 

understanding of the subject matter. If the participant scores at an acceptable level, 

the participant will be directed to Module 2. If the participant’s score does not meet 

standard, then the participant will begin Module 1. 

The participant will be shown a series of informational pages, pictures, animations, 

and simulations that will cover the capabilities, limitations, and vulnerabilities for 

each system. Where appropriate they will be provided TTPs on the employment of 

various systems. Specific information covered during this training will include the 

effects of weather and terrain on the sensor. Participants will also be trained on the 

methods that enemy forces can be expected to use to destroy sensors, spoof them, or 

negate their capabilities. 

As the participant progresses through the training, there will be automated checks 

on learning to assess participant comprehension, such as presenting tactical 

situations with various weather, terrain, and enemy conditions and asking the 

participants to select the appropriate sensor. The participant could also be asked to 

identify raw sensor images and determine which sensor provided the image. The 

participant could also be provided degraded sensor images and asked to identify

changes to the sensor payload or mission tasking (such as altitude, speed, sensor 

payload orientation) that could improve the quality of the image. The interactive 

courseware will provide immediate feedback to the participant. 

If the participant is not progressing satisfactorily through the training, the 

courseware will direct the participant to additional resources that can provide 

additional information about the sensors. After demonstrating sufficient mastery of 

sensor capabilities, limitations, and vulnerabilities, the participant will be directed to 

Module 2. 

B-6 
Training Modules Specifications, continued

Module 2: 

Sensor Tasking

Purpose: To train participants to task organic unit sensors to gain information to 

answer the commander’s critical information requirements (CCIR) and Priority 

Intelligence Requests (PIR). The participants will also be trained to identify CCIR 

and PIR that are beyond the capabilities of their unit’s organic capabilities and to 

initiate requests for information to higher headquarters to obtain support from

external sensors to provide the needed support.

Approach: The participants will be trained using a series of exercises that build on 

the expertise gained through completing the Module 1 training. The participants 

will be provided a tactical framework, including the higher unit’s R&S plan and 

overlay, CCIRs and PIRs and intelligence summaries. A visual projection of the 

location of all friendly forces based on the COP will be available. They will also be 

provided their unit’s R&S tasking matrix that has been completed except for the 

designation of the sensor that will be employed to answer the CCIR/PIR. The 

matrix is designed so that each successive task becomes more complex through 

manipulation of weather, terrain, the target type, the availability of sensors, and the 

ambiguity of the enemy situation. 

To answer each PIR, the participant will be required to select from a list of possible 

sensor taskings that provide the sensor’s mission profile (speed, altitude, sensor 

payload, sensor payload orientation, and other pertinent information). Once the 

participant selects a sensor, the interactive courseware will provide a simulation of

the sensor tasking and provide the appropriate sensor output for that selection. If 

the participant has made the adequate selection, the desired information will be 

displayed. If the participant has made an inadequate selection, the participant will 

be provided additional feedback as to why that selection was incorrect and then 

required to make another selection. This process will be repeated until all of the 

pre-designated PIR elements have been covered. 

Initially, the participant will be selecting taskings for individual sensors. Later in 

the module, the participant will be required to select among multiple combinations 

of sensors to answer the CCIR/PIR. As the participant progresses through the 

training, the amount of feedback or coaching that is being presented for an incorrect 

selection will be reduced so that by the end of the training level the participants 

have to determine where they made an error without outside assistance. 

The embedded LMS will track participant performance. If the performance 

warrants, the student will be referred back to Module 1, for refresher training, 

directed to repeat designated Module 2 exercises, or permitted to move on to 

Module 3 training. 

B-7 
Training Modules Specifications, continued

Module 3: 

Dynamic 

Sensor 

Retasking 

Purpose: To train the participants to dynamically retask organic unit or external

higher-headquarters sensors based on changing METT-TC factors.

Approach: The participants, building upon their mastery of the Module 2 training, 

will be expected to dynamically retask sensors during a series of increasingly

complex exercises in which there is a dynamic friendly and enemy situation. Their 

sensor retasking will be expected to answer time-sensitive CCIR/PIRs. The 

participant is provided the higher unit’s ISR plan and overlay, CCIRs and PIRs, and 

intelligence summaries. The location of all friendly and detected enemy forces will 

be displayed on a visual projection of the COP. Initially, the participant will be 

executing an R&S tasking matrix in which the sensor tasking has already been 

developed. During the course of task execution, the participant will be provided 

additional information which will cause the participant to change the sensor tasking. 

As the participant progresses through the module, the number of sensors that the 

participant will have to retask will increase, the potential vulnerability of the sensors 

to enemy action will increase, the amount of information provided about the enemy

will decrease, and the amount of time between the retasking directive from the 

participant’s commander until the enemy situational information is expected to be 

presented will be decreased. 

During this module, the participant will not be provided feedback about 

performance until the time has expired for the collection of enemy information. The 

feedback presented will focus on whether the enemy information in the exercise has 

been detected ( i.e., 5 of 5 enemy tanks located), whether sensors were lost because 

their vulnerability to enemy action had not been considered, and whether the most 

efficient choice for retasking sensors was made based on the tactical situation. A 

solution to the exercise will be presented. The participant will be required to 

execute similar exercises until the required level of performance is obtained. The 

embedded LMS will track participant performance. If the performance warrants, 

the student will be referred back to Module 2 for refresher training or permitted to

move on to Module 4 training.

Module 4: 

Sensor 

Integration into 

Tactical 

Operations 

[Note: This module will not be developed by the BCV Project Team]

Purpose: To train the participants to integrate organic unit-level sensors and 

available higher headquarters sensors into ISR operations. 

Approach: The participants, adding to their mastery of Module 3 training, will be 

expected to plan and execute a series of ISR operations, primarily focused on 

organic unit-level sensors and available higher headquarters sensor support. They

will develop an ISR plan that includes an R&S tasking matrix and an R&S overlay

to gain information to support the CCIR/PIR. The participants will then execute the 

ISR plan, making adjustments to sensor taskings based on an evolving enemy and 

friendly situation. Participant performance will be graded based on the amount of

information that is obtained from the ISR plan and the efficiency of the operation in 

terms of whether the sensor tasking obtained the desired information (more 

information is better), number of sensors required to obtain the information (fewer 

is better), the number of sensor losses due to enemy activity (fewer is better), and 

the amount of time required to answer the CCIR/PIR (shorter is better). 

B-8 
Training Modules Specifications, continued

Module 4: 

Sensor 

Integration into 

Tactical 

Operations 

(continued)

As the participant progresses through the training, the ISR tasks become

increasingly complex based on the tactical situation. For example, the initial 

operation might be an attack against a defending enemy located in open terrain. 

Subsequent operations may include moving enemy forces, complex terrain such as 

jungles or forests, and urban operations. The embedded LMS will track participant 

performance. If the performance warrants, the student will be referred back to 

Module 2 or 3 for refresher training. At the successful completion of the training, 

the participant’s training record will be annotated by the LMS and an appropriate

training certificate issued. 

Training Support Materials 

Module 1 Sensor Capabilities, Limitations, and Vulnerabilities materials include a listing of 

the sensors to be trained; a schedule of the training flow; participant training guides, 

either paper-based or electronic-based; embedded simulation files with augmented 

sensor cues as required; participant performance measurement metrics; participant 

performance examples; and demonstration of participant performance.

Module 2 Sensor Tasking materials include a description of the training exercises contained in 

the level; simulation system files; participant training guides; the supporting tactical 

materials including CCIR/PIRs, R&S tasking matrixes and overlays, and 

intelligence summaries as appropriate. Participant performance measurement 

metrics and performance examples are also required.

Module 3 Dynamic Sensor Retasking materials include a description of the training exercises 

contained in the level; simulation system files; participant training guides; the 

supporting tactical materials including CCIR/PIRs, R&S tasking matrixes and 

overlays, ISR graphics, and intelligence summaries as appropriate. Participant 

performance measurement metrics and performance examples are also required.

Module 4 Sensor Integration into Tactical Operations materials include a description of the 

training exercises contained in the level; simulation system files; participant training 

guides; the supporting tactical materials including CCIR/PIRs, R&S tasking 

matrixes, ISR graphics, and intelligence summaries as appropriate. Participant 

performance measurement metrics and performance examples are also required.

B-9 
Module 1 Training Details 

Overview Depending on the combat unit of assignment, the participant may be responsible for 

planning the employment of various sensor platforms that may include three types 

of Unmanned Air Vehicles (UAVs), three types of Unmanned Ground Vehicles 

(UGVs), and five types of unattended ground systems (UGSs). The UAVs and 

UGVs can be equipped with various combinations of sensor payloads. The 

participant may also be required to exploit the raw information that is provided by 

external sensors, such as those from a higher headquarters. The Module 1 training 

will have to address each of these systems and their payloads. 

An upfront demonstration of individual Soldier performance related to 

understanding sensor capabilities and limitations and the Soldier's role in 

tasking/controlling sensors during tactical operations may also be included in this

level. 

A description of a representative exercise for Module 1 training follows. 

Exercise Task: Participant selects the most appropriate UAV sensor. 

Condition: 

• Participant is provided with the following information 

− a COP that has the locations of all friendly units and the locations of enemy

units that have been detected 

− an R&S overlay with a target area of interest 

− it is night and there is atmospheric haze in the operational area 

− the operational area has rolling terrain with numerous forested hills along 

with agricultural fields 

− there is no battlefield clutter in the target area of interest 

• Participant provided with an intelligence report that indicates multiple enemy

tactical vehicles have been reported moving along a major highway toward the 

target area of interest 

• Participant can choose among microUAVs equipped with electro-optical and 

infrared sensors and Shadow 200 UAVs equipped with SAR or MTI sensor 

packages 

• The target area of interest is within range of both the microUAV and the 

Shadow 200 UAV 

• Participant is required to select the most appropriate UAV sensor/package to 

detect the moving tactical enemy vehicles. 

Standard: Participant selects the right UAV and sensor package. 

Performance Feedback: After the participant makes a selection, the training 

program provides immediate feedback on whether the selection was right or wrong. 

If the selection was wrong, the participant is provided the reason the selection was 

incorrect and then provided an opportunity to try again to select the best sensor. 

Once the participant has selected the right sensor, then simulation associated with 

the training program shows the sensor flying a route with the orientation and 

coverage of the sensor payload. As the sensor continues to fly, the training program

provides information on the effects of weather and terrain on the sensor and sensor 

payload as well as showing the information that the sensor will be reporting. The 

participant is then presented another exercise to continue the training. 

B-10 
Module 2 Training Details 

Overview Unit members have now completed the training on sensor capabilities, limitations, 

and vulnerabilities. That training prepared the participants to select, given an enemy

target set, the appropriate sensor or sensor payload to employ based on weather, 

terrain, and other information. 

Following are descriptions of two representative exercises for Module 2 training. 

Module 2 

Exercise 1 

Task: Participant confirms intelligence report. 

Condition: 

• Participant provided with the following information 

− a COP that has the locations of all friendly units and the locations of enemy

units that have been detected 

− an R&S overlay with a target area of interest 

− it is daylight with unrestricted vision 

− there is no battlefield clutter in the target area of interest 

• Participant provided with an intelligence report that indicates a single, stationary

DRAEGA has been reported in the target area of interest; the location has been 

given to within 100 meters 

• Participant has tasking authority for microUAVs equipped with electro-optical 

and infrared sensor and Shadow 200 UAVs equipped with SAR and MTI sensor 

packages 

• The target area of interest is within range of both the microUAV and the 

Shadow 200 UAV 

• Based on the conditions presented, the participant has to select from a menu the 

best combination of sensor, sensor payload, and route to confirm the 

intelligence report. 

Standard: Participant selects the best UAV, sensor, and route, based on conditions, 

to confirm report. 

Performance Feedback: After the participant makes a selection, the sensor route, 

the orientation and coverage of the sensor payload is displayed on the FCS C2 

system’s COP. If the selection was correct, the stationary DRAEGA is displayed. 

The participant is then given credit for properly tasking the sensor and the score is 

reported to the learning management system. The participant is then provided a 

more complex exercise. If the selection was incorrect, the sensor route, the 

orientation, and the coverage of the sensor payload are displayed along with the 

correct location of the stationary DRAEGA and a description of why the selection 

was incorrect for the conditions. The participant is then presented another exercise 

with similar conditions to continue the training. 

B-11 
Module 2 Training Details, continued

Module 2 

Exercise 2 

Task: Participant confirms intelligence report. 

Condition: 

• Participant provided with the following information 

− a COP that has the locations of all friendly units and the locations of enemy

units that have been detected (enemy units have short range anti-aircraft 

guns) 

− an R&S overlay with a target area of interest 

− it is daylight with unrestricted vision 

− there is no battlefield clutter in the target area of interest 

• Participant provided with an intelligence report that indicates a single, stationary

DRAEGA has been reported in the target area of interest; the location has been 

given to within 1000 meters 

• Participant has tasking authority for microUAVs equipped with electro-optical 

and infrared sensor and Shadow 200 UAVs equipped with SAR and MTI sensor 

packages 

• The target area of interest is within range of both the microUAV and the 

Shadow 200 UAV 

• Based on the conditions presented, the participant has to select from a menu the 

best combination of sensor, sensor payload, and route to confirm the 

intelligence report. 

Standard: Participant selects the best UAV, sensor, and route, based on conditions, 

to confirm report. 

Performance Feedback: After the participant makes a selection, the sensor route, 

the orientation and coverage of the sensor payload is displayed on the FCS C2 

system’s COP. If the selection was correct, the stationary DRAEGA is displayed. 

The participant is then given credit for properly tasking the sensor and the score is 

reported to the learning management system. The participant is then provided a 

more complex exercise. If the selection was incorrect, the sensor route, the 

orientation, and the coverage of the sensor payload are displayed along with the 

correct location of the stationary DRAEGA and a description of why the selection 

was incorrect for the conditions. The participant is then presented another exercise 

with similar conditions to continue the training. 

B-12 
Module 3 Training Details 

Overview After completing Module 2, participants will be trained to adjust their sensor 

employment strategies based on analysis of sensor outputs, other ISR cues, and the 

tactical situation. The adjustments to the sensor strategies should exploit the 

information that organic and external sensors and other systems are providing to the 

participant. 

A description of a representative exercise for Module 3 training follows. 

Module 3 

Exercise

Task: Participant retasks sensors to locate high pay-off target. 

Condition: 

• Participant’s unit is in contact with enemy forces 

• Participant provided with the following information 

− a COP that has the locations of all friendly units and the locations of enemy

units that have been detected (enemy units have short range anti-aircraft 

guns) 

− an R&S overlay with target area of interest 

− it is daylight with unrestricted vision 

− there is battlefield clutter in the area of operation 

• Participant has tasking authority for microUAVs equipped with electro-optical 

and infrared sensor and Shadow 200 UAVs equipped with SAR and MTI sensor 

packages 

• Participant is monitoring the execution of the sensor taskings required by his 

unit’s R&S plan 

• Participant provided with an intelligence report that indicates a high pay-off 

target (HPT) may be located in an area that is not being covered by sensors. 

The HPT is in a four square kilometer area, may or may not be moving, and 

may or may not be camouflaged 

• Participant’s commander wants to continue some sensor coverage required by

the R&S plan to support maneuver against enemy forces 

• Based on the conditions presented, the participant has to select from a menu the 

best retasking combination of sensor, sensor payload, and route to locate the 

HPT as well as continue to support current tactical operations. 

Standard: Participant selects the best retasking option based on the situation. 

Performance Feedback: After the participant makes a selection, the sensor route, 

the orientation and coverage of the various sensors being monitored and controlled 

by the participant is displayed by the training program on the FCS C2 system’s 

COP. If the selection was correct, the HPT is displayed. The participant is then 

given credit for properly retasking sensors and the score is reported to the learning

management system. The participant is then provided another complex exercise. If 

the selection was incorrect, the sensor route, the orientation, and the coverage of the 

sensor payload are displayed along with the correct location of the stationary 

DRAEGA and a description of why the selection was incorrect for the conditions. 

The participant is then presented another exercise with similar conditions to 

continue the training. 

B-13 
Module 4 Training Details 

Overview After completing Module 3, participants will be trained to plan the employment of 

the unit’s organic R&S sensors, using the FCS C2 system, to meet the commander’s 

priority intelligence requirements. They will also be trained to synchronize the unit 

plan with higher headquarters’ R&S plans to exploit the capabilities of external 

sensors. 

[Note: The Project Team will not be developing this training module] 

B-14 
Appendix C 

Training Matrix 

Exercises 1 through 7 are saved in a single file titled: BCV_101_Ex1to7 in both Red OneSAF 

Testbed Baseline (OTB) and Commander Support Environment (CSE) #1. All tasks are 

expected to have the identification and verification of the enemy vehicle within one minute of 

the microUAV taking its picture. 

Bundle 1 

Exercise Task Unmanned

Aerial Vehicle

Named Area of 

Interest

Target Type and 

Condition 

Target Grid 

1 Confirm cooperative

cueing and outputs 

1 Shadow 200 

1 microUAV 

NAI N1 1 Stationary 

Draega 

591171 

2 Confirm cooperative

cueing and outputs 

1 Shadow 200 

1 microUAV 

NAI N1 1 Stationary 

GARM 

596168 

3 Confirm cooperative

cueing and outputs 

1 Shadow 200 

1 microUAV 

NAI N1 1 Stationary 

BRDM-AT 

592163 

4 Identify and report, 

confirming SAR 

detections 

1 Shadow 200 

1 microUAV 

NAI N2 1 Stationary 

Draega 

573131 

5 Identify and report, 

confirming SAR 

detections 

1 Shadow 200 

1 microUAV 

NAI N2 1 Stationary 

BRDM 

583123 

6 Identify and report, 

confirming SAR 

detections 

1 Shadow 200 

1 microUAV 

NAI N2 1 Stationary 

Draega 

588112 

7 Identify and report, 

confirming SAR 

detections 

1 Shadow 200 

1 microUAV 

NAI N3 1 Draega 

1 Garm 

1 Darya 

Stationary 

543148 

556155 

538162 

C-1 
Exercises 8 through 11 are saved in a single file titled: BCV_101_Ex8to11 in both Red OTB 

and CSE #1. All tasks are expected to have the identification and verification of the enemy 

vehicle within one minute of the microUAV taking its picture. 

Bundle 2 

Exercise Task Unmanned

Aerial Vehicle

Named Area of 

Interest

Target Type and 

Condition 

Target Grid 

8 Confirm templated

enemy assembly 

area 

1 Shadow 200 

1 microUAV 

NAI N8 Stationary Draegas,

Garms, Ural trucks

565230-

585230-

565200-

585195 

9 Detect and confirm 

intel report of an 

enemy formation 

1 A-160 (MTI) 

1 Shadow 200 

1 microUAV 

NAI N9 Moving Draegas, 

GARM 

537263 to 

554257 to 

563205 

10 Detect and confirm 

intel report of an 

enemy column

1 A-160 (MTI) 

1 Shadow 200 

1 microUAV 

NAI N10 Moving Draegas, 

Grams, BRDMs 

515219 to 

450220 

11 Detect and confirm 

intel report 

1 Shadow 200 

1 microUAV 

NAI N11 1 Stationary, dug-in

or camouflaged 

Purga 

460250 

C-2 
Exercises 12 through 14 are saved in a single file titled: BCV_101_Ex12to14 in both Red OTB 

and CSE #1. All tasks are expected to have the identification and verification of the enemy 

vehicle within one minute of the microUAV taking its picture. 

Bundle 3 

Exercise Task Unmanned

Aerial Vehicle

Named Area 

of Interest

Target Type and 

Condition 

Target Grid 

12 Confirm

templated 

enemy positions 

1 A-160 (SAR) 

1 Shadow 200 

1 microUAV 

NAI N12: 

2816-3421-

3721-3718-

3434-3014-

2815 

Stationary, dug-in 

or camouflaged 2 

Draegas, 2 

Garms, 2 Purgas; 

Civilian vehicles 

present 

Draegas: 

2881514796, 

3382117645; 

Garms: 

3077716123, 

3479420436; 

Purgas: 

3585919078, 

2936415357 

13 Confirm intel

reports and 

templated 

enemy positions 

1 Shadow 200 

1 microUAV 

NAI N13: 

4115-4513-

4410-4010-

3911 

Stationary, dug-in 

or camouflaged 3 

GARMs, 2 BRDMATs, 4 

dismounted 

infantry squads; 

Civilian vehicles 

present 

Garms: 

4056112951, 

3972711601, 

4133910814; 

BRDM-ATs: 

4087014028, 

4333512211; 

Dismounted Inf: 

3987712464, 

4010211292, 

4089913907, 

4122710215 

14 Confirm intel

report of 

company-size 

Assembly Area 

1 Shadow 200 

1 microUAV 

NAI N14 3 Stationary 

Draegas, 3 

Garms, 2 Ural

trucks; Civilian 

vehicles present 

Draegas: 

3748007932, 

3747207852, 

3759607888; 

Garms: 

3785707605, 

3785007525, 

378790743; Ural 

trucks: 3759607794, 

3795907547 

C-3 
Exercises 15 through 18 are saved in a single file titled: BCV_101_Ex15to18 in both Red OTB 

and CSE #1. All tasks are expected to have the identification and verification of the enemy 

vehicle within one minute of the microUAV taking its picture. 

Bundle 4 

Exercise Task Unmanned

Aerial Vehicle

Named Area 

of Interest

Target Type and 

Condition 

Target Grid 

15 Confirm intel

report of a 

moving 

company sized 

unit 

1 Shadow 200 

1 microUAV 

NAI N15 6 Moving Garms, 

3 BRDM-ATs; 

Civilian vehicles 

present 

Garms: 

5402212590, 

5417712617, 

5428712628, 

5439712639, 

5478912672, 

5488812672, 

5498112683; 

BRDM-ATs: 

5451312650, 

5459612661, 

5468412661 

16 Confirm

template 

position of 

artillery unit in

firing position

1 Shadow 200 

1 microUAV 

NAI N16 Stationary, in 

firing position, 5 

Purgas, 5 Ural 

trucks

Purgas: 

4950016322, 

5034615916, 

4946615341, 

5021016931, 

4926317370; Ural 

trucks: 4942717377, 

5030216933, 

4958016328, 

5041415925, 

4954115349 

17 Confirm intel

report of 

tactically moving 

mixed formation 

1 Shadow 200 

1 microUAV 

NAI N17 1 Moving 

Draega, 2 

Garms, 3 

BRDMs 

Draega: 

3662307099; 

Garms: 

3675007412, 

3646207319; 

BRDMs: 

3664007640, 

3635207564; 

BRDM-AT: 

3654707454 

18 Confirm intel

report of a 

tactically moving 

formation 

1 Shadow 200 

1 microUAV 

NAI N18 3 Moving BRDMRecons, 3 Orels; 

Civilian vehicles 

present 

BRDM-Recons: 

3213107426, 

3240507443, 

3250807212; Orels: 

3318707160, 

3342707400, 

3369306920 

C-4 
Exercises 19 and 20 are saved in a single file titled: BCV_101_Ex19to20 in both Red OTB and 

CSE #1. All tasks are expected to have the identification and verification of the enemy vehicle 

within one minute of the microUAV taking its picture. 

Bundle 5 

Exercise Task Unmanned

Aerial Vehicle

Named Area 

of Interest

Target Type and 

Condition 

Target Grid 

19 Detect, identify

and confirm 

multiple intel 

reports of 

stationary and 

moving enemy

formations

1 A-160 (MTI) 

1 A-160 (SAR) 

1 Shadow 200 

3 microUAV 

Multiple NAIs 3 Draegas, 3 

Garms, 1 Purga, 

1 Darya, 1 

BRDM-Recon, 1 

BRDM-AT, 3Ural 

trucks, 3 MTLBs

Draegas: 

3977788385, 

3930495387, 

3659792309; 

Garms: 

4113088013, 

3808694542, 

3747789772; Purga: 

3226787573; Darya: 

3064494034; 

BRDM-Recon: 

4552898398; 

BRDM-AT: 

4745698499; Ural 

trucks: 

4725398127, 

4099587336, 

3057693764; 

MTLBs: 

2790492309, 

2746492546, 

2702492783 

20 Detect, identify

and confirm 

multiple intel 

reports of 

stationary and 

moving enemy

formations

1 A-160 (MTI) 

1 A-160 (SAR) 

1 Shadow 200 

3 microUAV 

Multiple NAIs 3 Draegas, 2 

Garms, 1 Purga, 

1 SA-13, 2 

Darvas, 3 

BRDM-Recons, 

1 URAL truck 

Draegas: 

3483800525, 

3262902716, 

2992804681; 

Garms: 

3536799789, 

3094803849; Purga: 

2599901281; SA-13: 

2465398070; 

Daryas: 

2586200129, 

2888498655; 

BRDM-Recon: 

3976601451, 

4057899543, 

3564908562; URAL 

truck: 3517699789 

C-5