Remote Detection of Covert Tactical Adversarial Intent of 

Individuals in Asymmetric Operations 

by Ann Bornstein, Thyagaraju Damarla, John Lavery, Frank Morelli, 

and Elmar Schmeisser 

ARL-SR-197 April 2010 

Approved for public release; distribution is unlimited. 
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Army Research Laboratory 

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ARL-SR-197 April 2010 

Remote Detection of Covert Tactical Adversarial Intent of 

Individuals in Asymmetric Operations 

Ann Bornstein 

Computational and Information Sciences Directorate, ARL 

Thyagaraju Damarla 

Sensors and Electron Devices Directorate, ARL 

John Lavery 

Elmar Schmeisser 

U.S. Army Research Office 

Frank Morelli 

Human Research and Engineering Directorate, ARL 

Approved for public release; distribution is unlimited. 
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Remote Detection of Covert Tactical Adversarial Intent of Individuals in 

Asymmetric Operations 

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Ann Bornstein, Thyagaraju Damarla, John Lavery,* Frank Morelli, and Elmar 

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14. ABSTRACT 

The goal of this report is to design a first-order road map for modeling research to bridge the scientific gap between 

observations from physical sensor networks at 3–50 m on the one hand and determination of covert tactical adversarial intent of

individuals with deception and in extensive clutter on the other. The research needs to integrate components from kinesiology,

neurophysiology, psychology, cognitive science, sociocultural anthropology, and information science. Research and 

development (R&D) issues that need to be considered include metrics for cognitive phenomena and how well detection systems 

work, data sets, determining whether actors can provide sufficient verisimilitude to create data sets, and relevant sensing 

technologies and information fusion techniques. Successful procedures may need to include actively (but unobtrusively) 

perturbing the situation in order to elicit specific responses. Comprehensive Department of Defense, Department of Homeland 

Security, Intelligence Advanced Research Projects Activity, and Federal R&D programs are required to promote rapid progress. 

The Federal Government should fund R&D programs with the objective of producing a theoretically founded design of a 

prototype system for remote detection of covert tactical adversarial intent of individuals in asymmetric operations within 5 

years and a working operational system within 10 years. 

15. SUBJECT TERMS

adversarial, asymmetric, covert, detection, intent, remote 

16. SECURITY CLASSIFICATION OF: 

17. LIMITATION 

OF ABSTRACT 

UU 

18. NUMBER 

OF PAGES 

46 

19a. NAME OF RESPONSIBLE PERSON

Ann Bornstein 

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Unclassified 

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Unclassified 

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410-278-8947 

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Prescribed by ANSI Std. Z39.18 
iii

Contents 

Preface v

Executive Summary vii

1. Introduction 1

1.1 Technical Themes............................................................................................................3

1.2 Delineation vs. Neighboring Areas .................................................................................4

2. Remote Detection of Intent: The Current Situation and the Future 5

2.1 The Current Situation ......................................................................................................5

2.1.1 Traditional Warfare .............................................................................................5

2.1.2 Rise of Terrorism .................................................................................................5

2.1.3 Asymmetric Warfare ...........................................................................................5

2.2 The Future .......................................................................................................................6

3. Research and Development (R&D) Directions 8

3.1 Cognitive/Perceptual Phenomena....................................................................................8

3.2 Sensing ..........................................................................................................................12

3.2.1 Detectable Indicators of Adversarial Intent .......................................................13

3.2.2 Sensors of Detectable Indicators of Adversarial Intent .....................................14

3.3 Information Fusion ........................................................................................................18

3.4 System Design, Testing, and Execution ........................................................................22

4. Coordination 23

4.1 Balance Between Near-Term Development and Long-Term Research ........................24

4.2 Collaboration Among Government, Academia, and Industry .......................................25

5. A Path to the Future 25

5.1 Recommendations .........................................................................................................25

5.2 Conclusion .....................................................................................................................26

6. References 27
iv

Appendix A. Workshop Participants 29

Appendix B. Workshop Steering Committee Contact Information 31

Distribution List 32 
v

Preface 

On December 7 and 8, 2009, the U.S. Army Research Laboratory (ARL) held a Strategic 

Directions Workshop “Remote Detection of Covert Tactical Adversarial Intent of Individuals in 

Asymmetric Operations” at its laboratory in Adelphi, MD. Collaborating in this workshop were 

ARL’s U.S. Army Research Office (ARO), Computational and Information Sciences Directorate 

(CISD), Human Research and Engineering Directorate (HRED), and Sensors and Electron 

Devices Directorate (SEDD); the U.S. Army’s Communications and Electronics Command’s 

(CECOM’s) Night Vision and Electronic Sensors Directorate (NVESD) and Intelligence and 

Information Warfare Directorate (I2WD); the U.S. Air Force Research Laboratory’s (AFRL’s) 

Human Effectiveness Directorate; and the Defense Academy for Credibility Assessment 

(DACA). The participants in the workshop are listed in appendix A of this report. The goal of 

the workshop was to design a first-order road map for research to bridge the scientific gap 

between observations from physical sensor networks at 3–50 m on the one hand and 

determination of covert tactical adversarial intent of individuals on the other. 

The first day of the workshop consisted mainly of short (5–10 min) presentations of relevant 

material coupled with extensive, in-depth discussion. At the end of the first day, information on 

paths to the future in many of the technical themes of the workshop was available. The 

information was sketchy, not well organized, and partially conflicting. At the end of the first 

day, the workshop participants set up three working groups (cognitive/perceptual phenomena, 

sensors, and information fusion) for the second day. The second day of the workshop consisted 

of working sessions to write and organize the technical information and resolve conflicts. Based 

on the material written at the workshop and material subsequently written by the workshop 

participants, the present unified report was collated and written by the workshop steering 

committee with the input and advice of the workshop’s participants. The workshop and the 

report are unclassified with unlimited distribution. The main audience for this report is U.S. 

Department of Defense and Federal management, and the scientific and engineering research and 

development community interested in remote detection of adversarial intent. 

It is because of the participants in the workshop, who generously shared their time, experience, 

and advice, that this report could be written. The workshop steering committee expresses its 

gratitude to them. This report affirms that remote detection of covert tactical adversarial intent 

of individuals in asymmetric operations is an area that is feasible and holds great promise for the 

future. It is our hope and expectation that this document will contribute to harnessing 

interdisciplinary science and technology in this area for the benefit of the United States and its 

allies. 

Ann Bornstein, CISD, ARL; Thyagaraju Damarla, 

SEDD, ARL; John Lavery and Elmar Schmeisser ARO, 

ARL; Frank Morelli, HRED, ARL 
vi

INTENTIONALLY LEFT BLANK. 
vii

Executive Summary 

The ability to identify covert intent of individuals who may be hostile would significantly 

improve asymmetric counter-insurgency and peace-keeping operations. Such individuals are 

generally embedded in extensive “clutter” of neutral and friendly human beings and various 

physical objects. At present, covert adversarial intent is identified through judgment of Soldiers 

and close-range sensing and searching, which often entail significant danger and possibly high 

false-positive and false-negative rates. Approaches to checkpoints (before a person gets close 

enough to blow up the checkpoint) and remote screening of people on patrol missions are 

defense scenarios where remote determination of adversarial intent is needed. Determining 

covert adversarial intent will help shift the balance in operations, mission planning, training, and 

simulation from more costly and dangerous sweeping operations toward much safer pinpoint 

operations based on refined estimates of people from which danger may come. Dual-use civilian 

benefits will be in crowd control and antidrug, anticrime, and border security. 

The fundamental principles that allow remote (i.e., at 3–50 m) identification of covert adversarial 

intent based on externally observable physical information are not known. The goal of this 

report is to design a first-order road map for modeling research to bridge the scientific gap 

between observations from physical sensor networks at 3–50 m on the one hand and 

determination of covert tactical adversarial intent of individuals with deception and in extensive 

clutter on the other. Although empirical observations and experiments will play large supporting 

roles in this research, the main emphasis is on discovery of theoretically justified quantitative 

predictive principles (models) and their implementation in tractable analytical and computational 

procedures. To be successful, the research needs to integrate components from kinesiology, 

neurophysiology, psychology, cognitive science, sociocultural anthropology, and information 

science. 

An important and often overlooked concept is measuring the problem. Metrics for cognitive 

phenomena and for how well detection systems work are needed. In addition to being practically 

useful, the metrics need to be computationally feasible (not combinatorially expensive) and 

mathematically justified. In cases where the computational cost of the desired metric(s) is too 

large, approximate ersatz metrics need to be developed. Whatever metrics or ersatz metrics are 

proposed should be justified not based on traditional use of the metrics in other areas, successful 

as that use may be, but rather on the basis of human goals in the remote detection of covert 

tactical adversarial intent. 

One major issue is the development of data sets. Can “method acting” (or any other school of 

acting) provide sufficient verisimilitude on all scales, including emotive/biochemical (sweat, 

breath, body habitus, kinesiology) to permit its use as a surrogate for “real” data? If so, the 
viii

creation of data sets, while still expensive, will be less expensive. One methodology for 

developing valid empirical data sets is to design experimental scenarios so that behaviors of 

interest are likely to be expressed. If enacted experiments cannot provide data that matches data 

of “real” situations, the expense and uncertainty will be larger. 

Sensing will require utilizing as many different measureable indicators of intent as possible and, 

thus, integration of multiple sensor modalities. Potential indicators of adversarial intent include 

posture, posture rigidity, heartbeat waveform, heart rate, breath rate (volume approximation, 

patterns, anomalies), wheezing, coughing, gasping, blood pressure trends (waveform shape and 

transit time), pulse-wave velocity (beat-by-beat approximation of blood pressure), movement 

(fidgeting, remaining still, shaking, shivering, having spasms), body stiffness, muscle tension, 

resonant frequency of body movement, voice stress analysis, voice onset timing, gastrointestinal 

distress, bowel sounds, reluctance to engage socially (distance from others, response to attempts 

to engage verbally), observation tendencies of subject (eye-glancing, head turning, situational 

awareness), people whose actions are coordinated or who are actively avoiding each other, 

exposure to bomb-making materials/chemicals, hyperthermia from stress (generally expressed in 

the face, palms of the hands, and soles of the feet), gait as indicators of stiffness (stress) or 

carrying a load or wearing protective clothing, breath biochemistry, and microbiology. Sensing 

technologies that may be able to measure relevant data include visible bandwidth imagers, 

thermal imagers, hyperspectral imagers, laser Doppler vibrometry, E-field, radar, ladar, gas 

chromatography, interrogators of the genetic signatures of prokaryotic microorganisms, chemical 

sensors (laser-induced fluorescence, laser-induced breakdown spectroscopy, Raman 

spectroscopy), photoacoustic sensors, retroreflection sensors, seismic sensors, and magnetic 

sensors. 

Fusion of the information from multiple sensors will be required to achieve accuracy. The Joint 

Directors of Laboratories (JDL) Data Fusion Model is the most widely used method for 

categorizing data-fusion-related functions. The JDL model is composed of levels of abstraction, 

with level 0 being the lowest or the minimally processed information level and with level of 

abstraction increasing in levels 1–4. Although there are many criticisms of the JDL model and 

many competing models, the JDL model has, in general, withstood the test of time, and most of 

the fusion community has accepted the JDL fusion levels. The JDL fusion framework is a 

suitable (but not the only) framework in which fusion of the output of many different sensors 

could take place. 

Successful identification procedures may need to include actively (but unobtrusively) perturbing 

the situation in which the sensing takes place in order to elicit specific responses (an abstract 

analogue of putting speed bumps in approaches to checkpoints so that the oscillation of cars can 

be observed and one can infer whether the car is carrying a heavy load). 
ix

Comprehensive U.S. Department of Defense, Department of Homeland Security, Intelligence 

Advanced Research Projects Activity, and Federal research and development (R&D) programs 

are required to promote rapid progress. Specific recommendations are as follows: 

• The Federal Government should fund R&D with the objective of producing a theoretically 

founded prototype system for remote detection of covert tactical adversarial intent of 

individuals in asymmetric operations within 5 years and a working operational system 

within 10 years. 

• The Federal Government should continue to provide broad support for academic and 

industrial efforts both in remote detection of adversarial intent and in areas (such as linkage 

of these systems with databases, media, and human input) that are useful for larger systems 

of systems. 

• The strong interdisciplinary nature of remote detection of adversarial intent should be 

reflected in all efforts supported by the Federal Government. 
x

INTENTIONALLY LEFT BLANK. 
1 

1. Introduction 

Today’s operations are being conducted in congested urban and remote mountainous terrains 

against an asymmetric threat. The targets that they encounter consist of (1) humans trained to 

maximize their disguise and conceal their intentions, (2) commercial vehicles, compact cars, 

sedans, station wagons and trucks optimized for quiet and smooth driving, and (3) explosives in 

the form of IEDs (improvised explosive devices) and UBEs (unknown bulk explosives) well 

hidden and camouflaged to minimize detection. These targets are hard to locate. Efforts are 

made by foes to plan these operations and conduct repeated rehearsals to provide the appearance 

of normal day-to-day events. The threat targets blend in the crowd, hide behind objects, and 

purposely occlude their appearance. Our foes spend many hours training and rehearsing every 

step of the operation to make the preparation and deployment up until the time of the actual 

attack or detonation appear as normal as possible. 

The ability to identify covert intent of individuals who may be hostile would significantly 

improve asymmetric counter-insurgency and peace-keeping operations. Such individuals are 

generally embedded in extensive “clutter” of neutral and friendly human beings and various 

physical objects. At present, covert adversarial intent is identified through judgment of Soldiers 

and close-range sensing and searching, which often entail significant danger and possibly high 

false-positive and false-negative rates. Approaches to checkpoints (before a person gets close 

enough to blow up the checkpoint) and remote screening of people on patrol missions are 

defense scenarios where remote determination of adversarial intent is needed. Determining 

covert adversarial intent will help shift the balance in operations, mission planning, training, and 

simulation from more costly and dangerous sweeping operations toward much safer pinpoint 

operations based on refined estimates of people from which danger may come. Dual-use civilian 

benefits will be in crowd control and in antidrug, anticrime, and immigration enforcement. 

It has been known since the Facial Action Coding System (FACS) created by Ekman and Friesen 

(1978) that the expression and microexpression of certain emotions related to adversarial intent 

take place partially involuntarily through facial muscles. Other physiological actions such as 

speech, heart rate, respiration rate, skin temperature, and perspiration can also carry information 

about emotions related to intent, although they are more subject to environmental influence than 

facial expressions. Laser methods can detect muscle movement, heart, and respiration rate and 

skin temperature. Visual sensors provide information about facial and body dynamics. 

Computer vision techniques can now automatically track facial expressions, eye movement and 

gestures. The ability to fuse information, for example, laser information with visual information, 

and to identify seemingly hidden patterns is increasing rapidly. Many of these techniques 

require close-range sensing/observation, often in a controlled environment and at the 0–2 m 
2 

range. This is suitable for airport screening but not appropriate for asymmetric defense 

scenarios, where threats need to be detected at distances at 3 m and, preferably, up to 50 m. To 

what extent close-range sensing techniques can be extended to larger ranges is not yet known. 

Moreover, modeling of the connection between emotions and intent is quite incomplete. 

Detecting that a person is afraid, for example, does not provide significant evidence for or 

against a hypothesis that the person has adversarial intent, since both friendly and adversarial 

people often have fear in commonly encountered scenarios. Conversely, a person with 

adversarial intent may show little or no fear. While there is some understanding about how to 

detect emotions, there is much less understanding about how to go further into the cognitive 

realm and determine intent. 

The fundamental principles that allow remote (i.e., at 3–50 m) identification of covert adversarial 

intent based on externally observable physical information are not known. Indeed, the step from 

recognizing physical objects, events, and patterns to recognizing intent is fairly described as a 

scientific chasm. Recent basic research related to bridging this chasm includes, but is not limited 

to, “Future Attribute Screening Technology (FAST)” (Department of Homeland Security—

DHS), “Violent Intent Modeling and Simulation (VIMS)” (DHS), “Detection of Intent through 

Perception of Biomotion Signatures” and “Visualization of Belief Systems” (U.S. Army 

Research Laboratory Human Research and Engineering Directorate—ARL/HRED), “Remote 

and Passive ID of Electrodermal Response” (Night Vision and Electron Sensors Directorate—

NVESD), “Behavioral Signatures” and “Human MASINT” (U.S. Air Force Research 

Laboratory—AFRL), “Hostile Intent” (U.S. Naval Research Laboratory—NRL), 

“Computational Modeling of Adversary Attitudes and Behaviors” (U.S. Air Force Office of 

Scientific Research—AFOSR, George Mason University), “Dynamic, Adaptive Techniques for 

Adversary Behavior Modeling” (AFOSR, University of Maryland—U MD), “Human, Social, 

Cultural, and Behavioral Modeling” (U.S. Army Research Office—ARO, Carnegie Mellon 

University, U MD), and “Tools for Recognizing Unconscious Signals of Trustworthiness 

Program (TRUST)” (Intelligence Advanced Research Projects Activity—IARPA). 

The goal of this report is to design a first-order road map for modeling research to bridge the 

scientific gap between observations from physical sensor networks at 3–50 m on the one hand 

and determination of covert tactical adversarial intent of individuals with deception and in 

extensive clutter on the other. Although empirical observations and experiments will play large 

supporting roles in this research, the main emphasis is on discovery of theoretically justified 

quantitative predictive principles (models) and their implementation in tractable analytical and 

computational procedures. To be successful, the research needs to integrate components from 

kinesiology, neurophysiology, psychology, cognitive science, sociocultural anthropology, and 

information science. 

Throughout this report, the phrase “remote detection of adversarial intent” will mean “remote (at 

3–50 m) detection of covert tactical adversarial intent of individuals in asymmetric operations.” 
3 

1.1 Technical Themes 

The technical themes of the workshop were as follows: 

• The strong interdisciplinary nature of remote detection of adversarial intent should be 

reflected in all efforts supported by the Federal Government. 

• Definition and quantification of types and/or classes of covert adversarial intent of 

individuals that generate terrorist/insurgent activities. 

• Principles from kinesiology, neurophysiology, psychology, cognitive science, sociocultural 

anthropology, and information science that link covert adversarial intent with 

data/information that can be observed noncooperatively through physical sensor networks 

at 3–50 m. The principles must take extensive clutter and deception by the targets into 

account. 

• Research on identifying emotions. However, emotions do not uniquely map (or, at least, it 

is not yet known that a collection of emotions can be uniquely mapped) to intent. For 

approaches involving emotions, the models that link the emotions to intent need to be 

considered. 

• Both passive and active sensing modes need to be considered. 

• Metrics for measuring adversarial intent. All metrics need to be based on cognitive, 

psychological, neurophysiological, and/or kinesiological principles. Ad hoc or heuristic 

metrics are not preferred. Most classical metrics (such as the root-mean-square or “rms” 

metric, Lp metrics, etc.) are of doubtful applicability. The metrics need to be capable of 

distinguishing “normal” and “anomalous.” 

• Identification of relevant data sources, determination of what data are relevant, and 

determination of new types/forms of information, sensors, and exploitation that may be 

needed. 

• Quantitative procedures that can, in extensive clutter, automatically infer from remotely 

observed data/information (shape, color/spectrum, movement, temperature, etc., on scales 

from micro to macro) covert adversarial intent of individuals. These procedures will 

involve fusion of parallel, unreliable, asynchronous data and information from 

“orthogonal” sensing modes. 

• “Data tractability” of the quantitative procedures (i.e., the ability to produce accurate 

results with data no more than is realistically likely to be available). 

• Scalability and overall computational feasibility of the quantitative procedures. 

• Limitations of the procedures, including whether the procedures do not work under various 

circumstances and/or cannot identify some types of covert adversarial intent. Estimation of 

the extent to which the procedures produce false positives and false negatives. 
4 

• Creation of empirical data sets to aid in creating new theory and quantitative procedures 

and for verification and validation. How can the data sets be collected to emphasize U.S. 

Department of Defense (DOD) needs? 

• Verification and validation of the quantitative procedures on empirical data sets. 

1.2 Delineation vs. Neighboring Areas 

The topic of this report is to detect individual intent and deception at distances (3–50 m) at which 

the damage that an individual can do is limited. There are many important related topics that are 

not focal topics of this report. We describe these topics here. 

1. Information operations and deciphering enemy strategic and tactical intent in predicting 

what actions the enemy force will undertake when and where are highly important but not 

the focus of this report. 

2. Although close-range techniques for airport security checking are generally not included, 

the extent to which the ranges of these techniques can be extended and whether they can 

work in noncooperative scenarios is included. Skin conditions that can be remotely 

detected—even though they may usually be detected at short range—do fit the focus of the 

report. Very close-range cooperative techniques such as EEG (electroencephalography), 

fMRI (functional magnetic resonance imaging), polygraph, etc., are not included. 

3. Research on physical pattern recognition (physical appearance and action) is likely to form 

a significant portion of the basis for the research envisioned in this report. However, 

research on physical pattern recognition is not, per se, a focus of the report. Research on 

physical pattern recognition is widely labeled research about determining intent, but such 

labeling is often inaccurate. For example, detecting a person with a gun running across a 

field toward a high-value target in video images is not equivalent to detecting adversarial 

intent. That person may well be a local security officer (whose job requires him to carry a 

gun) who wishes to urgently communicate that the real threat is a suicide duo disguised as 

fruit merchants poised to strike within a short time. Physical pattern recognition needs to 

be accompanied by science-based modeling that links the physical and cognitive. 

4. In future operations, the physical sensor networks will certainly be supported by human 

reporting, media, databases, data mining, learning, World Wide Web, game theory, etc. 

These topics are not foci of this report. However, the report does recognize that models 

that bridge the gap between remote physical sensing and cognitive phenomena will be 

embedded into larger frameworks that use these additional sources of information. 

5. Behavior modeling is the basis and context for this report but not the theme. 

6. Predictive modeling (What specifically will that person do next?) is a potential next step 

but not the focus of this report. 
5 

2. Remote Detection of Intent: The Current Situation and the Future 

In the early part of the 21st century, we are in the middle of “asymmetric warfare.” Asymmetric 

warfare, i.e., armed conflict between a nation and often faceless, nameless people, and 

organizations not affiliated with any government, has occurred throughout human history but has 

recently become more prevalent. 

2.1 The Current Situation 

Over the past two decades, warfare has shifted from traditional warfare (i.e., wars of fire and 

maneuver) to asymmetric warfare (i.e., wars of insurgency) due to many factors, including, but 

not limited to, terrorist actions in the U.S. homeland and operations in Afghanistan and Iraq. 

2.1.1 Traditional Warfare 

This type of warfare is characterized by large force-on-force actions. Traditional armies face 

each other over an invisible line of control. In this situation, individuals are not as important as 

collectives, and leaders are well known or easily identifiable. Furthermore, there are definable 

start and end points of these conflicts. This type of conflict predominated until the collapse of 

the Soviet Union in 1991. While still important, traditional warfare is now less common than 

asymmetric warfare. 

2.1.2 Rise of Terrorism 

Individuals or groups of individuals rebelling against a large county have been known 

throughout human history. Over the past few decades, terrorism has been increasing. While 

there is no widely agreed definition of terrorism, terrorism commonly refers to violent acts by 

civilians intended to create fear (terror), are carried out based on ideological goals, and 

deliberately target or disregard the safety of noncombatants. 

2.1.3 Asymmetric Warfare 

During the 1990s, with the Intifada in Palestine, the rise of al Qaeda in Afghanistan, and the 

coalition operations in Afghanistan and Iraq, asymmetric warfare has become prevalent. 

Terrorist groups unable to directly confront traditional armies resort to covert operations. The 

leaders are typically in hiding, and the terrorists are dispersed. Defeating such a faceless, 

nameless enemy embedding itself within a peaceful civilian population is a major military 

problem that confronts the United States and its coalition partners. 

Given the current situation, the United States has the need to find individuals who are hiding in 

civilian populations and remove them without causing collateral damage. In order to find these 

individuals, it is necessary to determine their intent—hence, the need for remote detection of 

covert, tactical adversarial intent of individuals. Since determining intent of adversaries is
6 

extremely important to the DOD, the urgency to date has resulted in widespread adoption of ad 

hoc solutions. Discovering solutions that will work in a wide variety of situations and are based 

on sound scientific principles is greatly needed. This report attempts to shine light on this 

significant problem important to the DOD. 

2.2 The Future 

The concept “intent” is widely used in the signal processing literature for “a physical pattern that 

(in a layman’s view) allows a conclusion of intent.” However, the number of false positives and 

false negatives produced when intent is interpreted as “a physical pattern” is large. Physical 

states such as sweating are also poor indicators of intent. In this report, intent is a concept with 

cognitive content that does not need to coincide with the seemingly obvious physical patterns or 

states that are often connected by nonspecialists with intent. The step from fused physics-based 

information to a conclusion of cognitive intent is huge. In this process, physical patterns and 

states may not individually indicate intent. However, by proceeding from the physical patterns 

and states through a fusion process that will put “orthogonal” signals together based on cognitive 

principles, intent can be determined. 

Humans who are intending to carry out preplanned violence have usually been coached and 

prepared in a manner not common to daily life. Historical data on World War II kamikaze pilots 

may be relevant to these considerations. Differentiable commonalities may exist in schooling 

and kinship. Just knowing one’s task and fate can have consequences in biological motion, 

especially if the violence is technologically enabled (e.g., triggered bomb vest). There is 

evidence that observers watching video clips on closed-circuit television can reliably detect 

individuals carrying weapons by judging that these individuals appear to have higher levels of 

malaise and restlessness (Blechko et al., 2009). 

This report focuses on remote detection of covert tactical adversarial intent of individuals in 

asymmetric operations. In future operations, this topic will, of course be embedded in a larger 

framework that will include use of information from databases, media, human sources, and other 

nonphysics-based sources, which can operate at long distances (greater than 50 m) and long time 

scales (hours, days, months). The lower arrow in figure 1, which is meant to be an analogy of 

the stages of defense from launch to landing of a missile, indicates the time scale for processing 

information. The apex is where the transition from intent to planned action occurs. After this 

point, the action will generally take place unless it is thwarted. The time scale gets shorter as one 

gets closer to the event. The “months-minute-second” arrow highlights that the time to 

successfully respond is progressively shorter. 
7 

Figure 1. Processing times (lower arrow) for defense against adversarial asymmetric operations (represented by 

pictures and upper arrow). 

There are many different approaches to understanding adversarial intent. They are organized by 

their relative time to the event as follows: 

• Postevent: Persistent and pervasive sensing offers the ability to do detailed forensics of the 

events that led up to an event. This can be used to follow clues to determine where the 

adversaries came from and where they went and can also be useful for developing a deep 

understanding of the local components of an adversarial group. 

• Imminent event: This is the focus of the present report. Thwarting an imminent event will 

most likely rely on very local information. Context or other indications that something is 

coming would help. However, this part focuses on and assumes that those indicators 

haven’t come or are insufficient. In this case, local fusion is necessary to bring together, 

with sufficient time for prevention, the pieces of information that add up to a threat 

warning. 

• Developing event: Most significant events do not take place without significant planning 

and preparation. Major components include fundraising, recruiting, indoctrination, 

training, team assembly, reconnaissance, infiltration, integration, recruiting of subjectmatter experts, infiltration of materials, final reconnaissance, final communications, and 

execution. Each of these stages can be interrupted, but the timeline is progressively more 

compressed. 
8 

A wealth of potentially fruitful research directions is discussed in the next section. These 

directions indicate that it is fair to estimate that remote detection of adversarial intent is indeed 

feasible. Success, however, will depend on the details of how the already-existing building 

blocks plus new nonexistent building blocks can be put together to carry it out. 

3. Research and Development (R&D) Directions 

In this section, we estimate potentially fruitful R&D directions for remote detection of covert 

tactical adversarial intent of individuals in asymmetric operations. The objective here is to 

describe R&D directions that will eventually bridge the gap between physical phenomena and 

cognitive intent using principles, known or hypothesized, that provide a scientifically based 

(rather than a layman’s experience-based) bridge from physical phenomena to the cognitive 

issue. 

3.1 Cognitive/Perceptual Phenomena 

In research on sensing adversarial intent, a basic assumption is that every brain action or state 

has a corresponding effect or state, however diminutive or hidden, in the body and that the effect 

or state in the body is amenable to some sort of sensing, even if not remote. What is sought is 

twofold: (1) an anomalous individual – a person who should not be in that place at that time and 

(2) a person acutely intending local violence after having planned and rehearsed the action. The 

first, considered for physics-based sensing, is a signal/noise problem. The second, a cognitive 

issue, implies a difference between “hot” anger (acutely triggered rage) and “cold” anger (similar 

to vengeance motivation), both of which can be physiologically differentiated from normal 

cognitive states. Can this be related to “state” vs. “trait” consideration in psychological 

profiling? One should note that superficial “profiling” seems to fail. The Transportation 

Security Agency (TSA) has noted that random screening produces more valid “hits” than 

superficial profiling. However, this experience may not be applicable without caveats to combat 

or quasi-combat situations. Can cognitive state be quantified in order to be related to detectable 

signals, or is this even needed? Is physiological state enough to determine that a person is 

“anomalous” and has an adversarial “plan”? 

Specific questions to be answered include the following: What are the effects of people who are 

actually innocent bystanders but have knowledge that can potentially jeopardize them and their 

family? Can research be done on bystanders? What are the cues that a bystander gives off if he 

or she knows something about a plan? Are these cues detected by “expert human detectors”? 

Can these cues be automatically detected by sensors? 

Exploration of enemy tactics, techniques, and procedures (TTPs), which often predictably vary 

relative to weather conditions, as contextual factors for determining intent should be considered. 

Most individuals intending to do harm have gone through at least some type of training and 
9 

practice. There is often a need to actively induce or ramp-up a response from individuals so that 

it can be sensed. By actively inducing a response, the flow of a plan will be altered and perhaps 

more easily observable. Those who have a plan may be affected more by even normal delays 

than others. The TSA Screening Passengers by Observation Technique (SPOT) program has 

noted that people with a plan can get increasingly angry with delays, whereas others may just 

have ordinary annoyance but not anger. 

It is impossible to have a thought without some physical manifestation of the thought. Detecting 

such a manifestation may present a challenge when actively suppressed. It is in these instances 

that perturbing the individual’s decision process may be effective for revealing covert adversarial 

planning. It may be worthwhile to explore the introduction of cognitive “speed bumps” 

(perturbations) into the decision process for individuals of interest, where real-time analysis of 

behavioral or physiological reactions is oriented toward a decision either to break contact and 

move on to checking other individuals or to retain contact and press further (i.e., take action with 

the present individual). “Active elicitation” of information may be a fruitful research area. 

Active elicitation processes should be minimally intrusive so that they are not easily detected as 

probing countermeasures by the subject of interest. The cognitive load of such an individual is 

likely to be at some times greater and at other times less than that of a normal individual—

greater if they are attempting to appear “normal” and the planned event is still some time in the 

future, less if the event is imminent. 

After the beginning of a stimulus, physiological cues in some/many processes can peak in 

5–10 s. The human body tends to have many nonspecific responses (i.e., physiological responses 

that are part of the homeostatic or other internal state of the body unrelated to external cognitive 

stimuli). In order to understand which physiological cues relate to which stimulus, it is 

necessary to know the timing of the stimulus. Can a person’s cognitive load be probed in time? 

If so, these probes need to be both culturally appropriate and relatively inconspicuous. How and 

when can previously rehearsed patterns be perturbed? It will be important to develop good 

stimuli for evoking a response that might be an idealized, adversarial intent, cognitive state, 

emotion, or arousal level. There may be differences due to different situations. Those carrying 

bomb vests implying self-immolation can have a different cognitive set (with associated 

biomarkers) than those controlling remotely triggered explosive devices. Research about how 

one actively (and unobtrusively) controls the situation so that threat indicators are likely to be 

expressed and thereby achieves subject engagement and behavioral authenticity is needed. 

Given the enormity of the topic and its currently amorphous state, perhaps an iterative approach 

to experimentation, in which scenarios are initially kept relatively simplistic and then 

experimental complexity is increased as knowledge is gained, is best. 

Research on states of mind related to the timing of the event can be beneficial. For example, if 

an act and the associated verbiage to use if interrogated have been well rehearsed, then there 

might still be some variability in responses up until very close to the act. At that point, there 

could be a reduction in normal physiological responsiveness but more of a response to an 
10 

unexpected event delaying the plan. Research related to planning and disruption of plans might 

be helpful to distinguish the effect of the type of plan on the response to disruption or delay. 

Much of what may have to be done, without the benefit of an interview/interrogation, is to 

induce a response. This response may be cognitive or behavioral and will be biased by cultural 

constructs. Stimulus-response, operational design in a complete theoretical framework is likely to 

be advantageous. However, the likely difficulty in constructing a suitable theoretical framework 

suggests that beginning with an empirical and observational approach may be advisable. 

The physiological state of the observer should be considered as well as that of the target 

individual, specifically with respect to sleep deprivation, dehydration, and associated cognitive 

decrements such as decreased attention and hallucination. A related research direction could 

explore the impact of stimulant use (e.g., caffeine delivered via energy drink consumption), 

which is widespread among Soldiers. If a physics-based sensor system is created that focuses on 

the Soldier who is doing “human-based” sensing, it can act as an amplifier of perhaps ignored 

hunches and take advantage of the experience just cited. 

Biomarkers can mark internal or external events. Animals can sense internal human states (e.g., 

fear). They are sensing (perhaps smelling) a characteristic of a person’s state. People who have 

been around explosives may have explosives on their skin or clothes and can ingest them in some 

way into their bodies and may then emit them as biomarkers for very sensitive chemical 

signature detection. 

Differentiation needs to be made between psychological issues (ideologues, disaffected, etc.) and 

psychopathological issues (not sociopathic or psychopathic). Clinical data may not be applicable 

as physiological indicators. For example, sociopaths may be emotionally numb. Further, there 

could be possible links to religious fanaticism and the associated training involved (Alamut, The 

Secret Doctrines of the Assassins, 1998). Can we identify the different types of terrorist 

psychologies? Are there significant differences between the person who totally believes he or 

she is carrying out an important duty vs. the person who has been forced into compliance with 

the plan through threats and intimidation? 

Another important and often overlooked concept is measuring the problem. Metrics for 

cognitive phenomena and for how well detection systems work are needed. It helps to know 

what problem we are really trying to solve and define how well the processes are working. So 

often, current practices assume a certain level of competence; however, there are currently no 

good metrics in place to assess the effectiveness of current practices. It is important that these be 

developed. More than likely, most operational processes are working worse than assumed. 

What is the deterrence effect? The metrics themselves may be computationally intensive but 

should run seamlessly in the background, invisible to the user. Results should be easily 

interpretable and not lend themselves to a “so what” response by the user. Often, new metrics 

are developed in conjunction with user communities, which usually ensures that such metrics are 
11 

practically useful. In addition to being practically useful, however, the metrics need to be 

computationally feasible (not combinatorially expensive) and mathematically justified. In cases 

where the computational cost of the desired metric(s) is too large, approximate ersatz metrics 

need to be developed. Whatever metrics or ersatz metrics are proposed should be justified not 

based on traditional use of the metrics in other areas, successful as that may be, but rather on the 

basis of human goals in the remote detection of covert tactical adversarial intent. In the past, 

metrics based on information theory have been recommended for use in the fusion process. 

However, these metrics are designed for nonbiological physical processes. Evidence that they 

are applicable to the physiological, psychological, and cognitive processes of interest here is not 

available. 

Good metrics to determine behavioral authenticity in experimental scenarios do not currently 

exist. This should be a focus for efforts in this domain. At the 6.1 level, the theory needs to be 

the driver in developing valid metrics of intent detection. Associated research questions include 

the following: What is the relation between emotion and adversarial intent? What are the 

emotions that a person would be experiencing as he/she goes through the plan from planning to 

execution? What are the effects of seeing an authority figure while carrying out the plan? What 

are the effects of changes in planned timing? Statistical interpretation of results will need to 

address the operational world (rather than the academic world). Group differences and 

associated parametric statistics often used in the academic world are only rarely useful in the 

operational world where we need to know how to react to an individual’s behavior. 

One major issue is the development of data sets. Can “method acting” (or any other school of 

acting) provide sufficient verisimilitude on all scales, including emotive/biochemical (sweat, 

breath, body habitus, kinesiology), to permit its use as a surrogate for “real” data. If so, the 

creation of data sets, while still expensive, will be less expensive. One methodology for 

developing valid empirical data sets is to design experimental scenarios so that behaviors of 

interest (e.g., facial expressions, specific human kinematics) are highly likely to be expressed. 

These behaviors of interest, while seemingly artificial within the framework of a contrived 

scenario, may be close to the behaviors in “real” scenarios. If enacted experiments cannot 

provide data that matches data of “real” situations, the expense and uncertainty will be larger. It 

is often expensive and difficult to get large amounts of data on “real” situations. The ground 

truth, i.e., the true intent of the subject, is often difficult to determine because of deception or 

disappearance of the subject. One notable exception is the vast store of video footage within the 

defense community depicting actual battlefield hostility, often including the behavioral 

precursors to a hostile event. The clear validity of such data is at least partially offset, however, 

by issues related to data format variability, resolution, and classification. Also, the sheer volume 

of these data sets, which often do not include sufficient event tagging or categorical organization, 

makes them less tractable for scientists attempting to exploit the content for empirical use. 

The virtual reality community has a well-established database for determining participant 

engagement. This should be explored for its applicability to the current effort. The applicability 
12 

of virtual reality research to adversarial intent detection should be explored. Again, a primary 

concern is authenticity of the data gathered when the input is simulated. Time commitment does 

not seem to be a factor for subjective engagement levels. Good engagement can apparently be 

achieved over a few hours. Data from the virtual reality immersion literature is, however, mainly 

in the realm of psychological treatment (e.g., phobias) and may be contaminated from being in a 

patient population. An alternative might be to examine the principles of immersive game system 

design. 

Automatic detection of adversarial intent can benefit from using methods that human experts use, 

including, but not limited to, criminological strategies of presenting photos of a crime scene, 

presenting mug shot albums, exposing subject to news clips or headline news, filling out surveys 

or forms, and delaying forward movement. There is, however, an open question about whether 

such expertise is transferable without extensive personal immersion within a conflict 

environment. Also, whether these can be adapted for the desired scenario (3–50-m distance) will 

need to be examined. Finally, without clear metrics for how experts detect intent, there is no 

theoretical basis for transferring such expertise. 

The difficulty in obtaining ground truth for adversarial intent detection is an issue. The 

occurrence of adversarial intent, less than 1/1000, is also an issue. This fact makes a 

nonnormative approach to statistics important. Each individual may have unique responses 

relevant to his or her adversarial intent. Since we don’t know what a “real bad guy” with 

adversarial intent may be like, perhaps we need to know in exquisite depth what a good guy 

looks like. Are there special populations that can be ethically tapped to provide appropriate 

cognitive or psychological constructs, i.e., can incentives provide the amount of “buy-in” needed 

for a valid proxy of a “bad guy”? These issues in basic research attempting to quantify the 

cognitive mechanisms that underlie threat detection skill are a primary concern. Are results valid 

when the actions observed are conducted by individuals who aren’t actually hostile in the course 

of their normal existence? It should be noted that, among actual attackers, many are not actually 

hostile in the course of their normal existence and have varied motivations for conducting 

attacks. 

3.2 Sensing 

Individuals with adversarial intent will experience physiological changes and possibly display 

altered motions and behaviors that may be detected. In this section, we discuss physics-based 

sensors that may be able to remotely detect indicators for subsequent fusion to determine intent. 

Physics-based sensors will provide the input for a cognitive approach to sensing adversarial 

intent and offer the advantage of a quantitative assessment. 

Sensing will require utilizing as many different measureable indicators of intent as possible and, 

thus, integration of multiple sensor modalities. The indicators relevant to adversarial intent must 

still be experimentally identified, underscoring the importance of developing experimental and 

simulated systems that reliably reflect intent. Sensing of indicators of intent will require data 
13 

collection on time scales of seconds to minutes. The fusion of this data from many sensors could 

enable monitoring of an individual over several hours, reinforcing the determination of intent. 

Moreover, indications of intent will ideally be sensed using passive observation as well as active 

perturbation of the individual. Determining the most effective means of active perturbation is an 

area needing exploration. 

Efforts in this area should encompass fundamental research as well as application of sensing 

systems in testable environments, i.e., basic research (“6.1 research”), applied research (“6.2”), 

and advanced development (“6.3”). R&D may include the discovery and development of novel 

sensing capabilities for identifying indicators of adversarial intent within relevant experimental 

and simulated environments and detecting these identified indicators in relevant field situations. 

Individual sensors will need to be integrated into comprehensive systems to increase the overall 

sensitivity of the measurements, and sensor systems will need to be tested with increasingly 

larger sample sizes. It is anticipated that many of these sensors will flag significant numbers of 

false positives and false negatives. However, even though the measured parameters may 

individually have unacceptable error rates, they may still significantly improve the value of the 

system as a whole. It is important not to prematurely discard sensing technologies merely 

because false positives or false negatives will be generated. 

3.2.1 Detectable Indicators of Adversarial Intent 

The goal is to identify measureable physiological, biochemical, genetic, or other types of 

indicators that are correlated with an underlying elevated level of stress; a determination to carry 

out a plan with adversarial consequences as well as other less temporal predictors of violent 

activity including, but not limited to, social disconnectedness; and an embracing or acceptance of 

violence and death. Psychological, physiological, genetic, and biochemical factors that can 

correlate with social disconnectivity and a willingness to commit violence include aggression, 

impulsivity, autism, post-traumatic stress disorder (PTSD), unattractiveness, chromosomal copy 

number variants, and mutations in specific genes. 

The first level of defense is to determine whether or not people are present. Technologies have 

been developed to detect the presence of human skin, heat from living organisms, etc. Once the 

presence of humans has been identified, analysis needs to move onto a deeper level to identify 

adversarial intent. Informative biological parameters that can be measured include the 

following: 

• Posture 

• Posture rigidity 

• Heartbeat waveform 

• Heart rate 

• Breath rate, volume approximation, patterns, anomalies 
14 

• Wheezing, coughing, gasping 

• Blood pressure trends: waveform shape and transit time 

• Pulse-wave velocity giving a beat-by-beat approximation of blood pressure 

• Movement: fidgeting, remaining still, shaking, shivering, having spasms 

• Body stiffness, muscle tension, resonant frequency of body movement 

• Voice stress analysis and voice onset timing 

• Gastrointestinal distress, bowel sounds 

• Reluctance to engage socially: distance from others, response to attempts to engage 

verbally 

• Observation tendencies of subject, eye-glancing, head turning, situational awareness 

• People whose actions are coordinated or who are actively avoiding each other 

• Exposure to bomb-making materials/chemicals 

• Hyperthermia from stress (generally expressed in the face, palms of the hands, and soles of 

the feet) 

• Gait as indicators of stiffness (stress) or carrying a load or wearing protective clothing 

• Breath biochemistry 

• Microbiological organisms on skin or clothing 

Attention should also be given to screening people in vehicles. Some potential informative and 

measureable signals include the following: 

• Body and movement rigidity/stiffness as a measure of stress, probably more informative if 

measured in response to unexpected perturbation such as a unusual speed bump, 

unexpected timing of red light, provocative noise, or other stressor. 

• Acoustic signals: If one can hear what is being said or happening in the vehicle, are the 

occupants making small talk, silent, listening to the radio, or praying? 

• Lip and facial movements: Are the occupants talking, praying, silent, listening to the radio, 

or listening for outside cues? 

3.2.2 Sensors of Detectable Indicators of Adversarial Intent 

Body-contacting sensors that assess human physiology, such as those used for polygraphy, 

biometric identification, and medical diagnosis, are common. However, using these sensors at 
15 

stand-off distances presents both physics-based and operational hurdles. Many of the current 

sensors cannot operate at even modest distances (1 m) away from human bodies. Ongoing R&D 

is dramatically increasing sensor sensitivity, bandwidth, field of regard, and many other 

characteristics but still may not be able to overcome many of the technical barriers associated 

with remote intent assessment. Many of the sensors mentioned in this section may not be ready 

for immediate application but may offer significant benefits if sensor system maturity advances 

quickly. 

We attempt to highlight sensing technologies that we believe can measure useful data for 

assessing adversarial intent. Passive and active sensors can be used to remotely characterize a 

human’s physical features, clothing and equipment, and his or her interaction with the immediate 

surrounding environment. Passive sensors do not emit a signal and rely on a target’s emission in 

some sensing domain. Active sensors emit a signal that interacts with the target in a known 

manner to produce a return that is quantifiable and related to the stimulus. Hybrid systems may 

use an active signal to produce a response observable in one or more domains. Sensor fusion can 

combine multiple inputs and domains to enhance features and remove noise or interferers. 

Redundancy of diverse sensor modalities will help corroborate physiological parameters in noise 

as well as alternative inputs for fusion algorithms. Some potential sensor domains and expected 

contributions are as follows: 

• Imagers, in general: Imagers with related image-processing hardware and software can 

assess shape and contrast and change in single images and multiple frames. Change 

detection algorithms detect, track, and/or assess movements and establish “normal” traits, 

gait, movement tracking, surveillance activity, facial expression, emotion, phenotypic 

patterns, and body language. Imagers provide data for extracting behaviors and 

interactions among people in the field of view through pattern recognition. All of these 

capabilities can be implemented using thermal, hyperspectral, visible, and narrowband 

imagery. 

• Thermal imagers: Passive thermal imagery can monitor overall surface temperature 

patterns, radiometric levels, facial thermal patterns, capillary dilation effects, 

bombs/weapons, clothing thickness, and specific body features. 

• Hyperspectral imagers: Passive multiband imagers are optimized to select specific 

wavelengths of interest related to particular optical phenomena or material. Narrowband 

imagery can focus on sweat, subsurface blood flow, and particular types of clothing or 

equipment. 

• Visible bandwidth imagers: These imagers establish “normal” visible appearance, 

identifiable traits for biometric ID, facial expressions, skin-tone changes, phenotypic 

patterns, and body language.
16 

• Laser Doppler vibrometry: Active laser interrogation of skin or body surfaces reveals 

vibrational cues related to heartbeats, breaths, body resonances, muscle stiffness, voice, and 

voice stress. 

• E-Field: Passive free-space electrodes (capacitively coupled) can detect electrical activity 

of the heart, brain, muscles, and hidden electronic devices. 

• Radar: Active radar can detect and track human gait, heartbeats, breaths, radar cross 

section (RCS), and arm/leg movements. 

• Ladar: Active laser radar produces three-dimensional (3-D) imagery related to “hostile” 

stance, 3-D posture, gait dynamics, facial recognition, carrying of backpacks, and unnatural 

frame proportions. 

• Gas chromatography: Active collection of exhaled gas, such as through a suction tube near 

a microphone or portal, can provide a sample for trace gas analysis, chemical emission, 

odor, genetic material, and biochemistry assessment. 

• Genetics of prokaryotic microorganisms: Genetic mutation of prokaryotic microorganisms 

on or in humans is environmentally influenced. DNA mutates in prokaryotes at a rate of 

approximately 1 in 300 nucleotides per generation; generations can be as short as 10 min. 

If the mutation patterns were untangled, the resulting information could reveal where the 

organism has been and what it has been exposed to. Mutation and mutation rates of 

prokaryotic organisms on or in humans might thus help determine where humans have 

recently been, what they have been eating, whether they have had unusual exposures to 

man-made or other toxins or mutagens and, in general, whether the patterns in their 

microorganisms’ DNA is consistent with their purported history, identity, and activities. In 

addition, the relative fitness of different species of prokaryotes depends on the 

environment; the environment of prokaryotes growing in or on humans is influenced by 

that human’s activity. The relative abundance of different species of prokaryotes on 

humans is also reflective of that human’s environment and could be exploited to reveal 

information about that person’s activities. 

• Chemical sensors: Laser-induced fluorescence, i.e., active laser illumination with passive 

response monitoring (either fluorescent imaging or nonimaging amplitude of a particular 

wavelength), can be used to inspect a portion of the body for sweat or other compounds 

(salivary amylase or cortisol in the mouth or in the breath). Laser-induced fluorescence, 

laser-induced breakdown spectroscopy, and Raman spectroscopy can be used to inspect 

body and clothing for traces of explosive chemicals. 

• Photoacoustics: Active laser stimulation of a test area and acoustic analysis of induced 

resonance can give indications of trace gas from the mouth, biological markers, and 

chemical residue on skin. 
17 

• Retroreflection: Active laser illumination and passive imaging of the collimated returns 

can be used for detecting optics/video cameras used by a target and can also detect naked 

eyes in close proximity for staring or eye tracking. 

• Seismic: Passive sensing of ground or floor vibrations can be used to assess gait anomalies 

from concealed objects and weight anomalies for motion tracking. Active stimulation can 

induce movement of hidden objects. 

• Magnetics: Passive magnetic sensing can detect hidden objects that might indicate intent, 

such as a pistol, knife, or explosive initiator component. 

On a slightly higher level, one could measure the following: 

• Physical evidence of psychological traits: Physical evidence of psychological traits of 

interest (PTSD, autism, antisocial behavior, embracing violence, indifference to human 

death, etc.) could be measured. One would have to develop an enabling database for 

phenotypic-to-psychological correlation. 

These are just a few technologies that can potentially provide useful results. New sensors with 

enhanced capabilities as well as new data analysis techniques and information interpretation 

techniques are continually emerging. 

All systems must undergo thorough evaluation to quantify efficacy. Since detection of intent by 

noncontact sensors is a new area, performance metrics have not yet been established. Individual 

sensor developers need to provide a full physics-based description of expected sensor capabilities 

as well as important parameters. Residual capabilities of a sensor may also have beneficial 

“artifacts” or add capabilities to other sensor modalities. The more diverse-modality information 

available, the better the fusion opportunities. The ability to individually update the algorithms 

for each sensor modality will accelerate the creation of new parameters or fusion results. 

Signal processing methodology must provide a robust and automated ability to reduce highbandwidth data to a few useful parameters and the ability to relate these measures to adversarial 

intent, stress, behavior, or other cognitive quantity. Obviously, robustness will also depend on 

each sensor’s specific detection range, sensitivity, field of regard, dwell time, autonomy, and 

other characteristics. 

Operational considerations include covertness of stand-off sensing, amount of clothing that 

might mask measureable parameters, interfering signals from motion artifacts, environmental 

considerations, ambient noise, speech, and the dynamic movement of the subject or sensor 

system. If a particular assessment of an individual requires a comparison with some preexisting 

“baseline,” a plan needs to be established to access the baseline in the operational environments 

being discussed. An obvious objective is to process the data in real time to show states and 

changes in measured parameters. However, there may be a requirement to log raw data for postprocessing for complex analysis of high-bandwidth imagery or multichannel fusion. 
18 

The scope of sensor system development for human intent prediction may range from initial 

proof of concept for novel sensing technologies to the evaluation and validation of sensing 

modalities for the new application of remote intent evaluation. Projects that involve the 

development of new remote sensing technologies should, at the very least, incorporate some 

element of validation using established “ground truth” sensors. For example, a novel sensor 

intended to evaluate heart rate at a distance should be validated with standard on-contact 

electrocardiogram (EKG) sensors to establish the accuracy of the system. This approach should 

also involve some consideration for potentially confounding factors. 

Contact sensors may provide a level of measurement fidelity that permits a system to distinguish 

between an aggressive individual and someone who is merely distraught; however, the potential 

loss of sensitivity and incorporation of movement artifact with a novel remote sensor may 

obviate that distinction. Also, although it is not expected that a development project will involve 

validation with a complete human subject dataset, there should be some consideration of the 

sample size necessary for performance estimation. The sensor development approach should 

incorporate more than a demonstration of feasibility. 

Mature sensor technologies originally developed for other applications will require testing and 

validation in this new setting. Testing should address elements of measurement paradigm 

(passive or stimulus/response), performance limitations and potential confounding factors, 

possibility of fusion with additional sensing modalities, and substantive testing with a dataset of 

human subjects for performance evaluation. One should identify and quantify sensor limitations 

related to environmental conditions such as ambient temperature, daytime vs. nighttime, 

visibility conditions (fog/rain vs. clear), and requirements related to sensor positioning and 

regarded field. The validation of remote sensors should also include fusion with alternate 

sensing modalities. 

3.3 Information Fusion 

Information fusion is a process that correlates, combines, modifies, and enhances the 

interpretation of a set of information from a state of lower generalization to one of higher 

generalization. The fusion process starts from data from sensors or sources. There are many 

existing sensing modalities and associated signal processing techniques that have matured over 

the years and that can extract target features for detection, classification (human, vehicle, 

animal), identification (e.g., man, woman vs. child; car vs. truck; dog vs. horse), recognition 

(e.g., John vs. Richard; Toyota vs. Honda), localization (x, y, and z) and tracking (as a function 

of time). Each sensing modality may be individually robust, but no one sensor can accurately 

produce the information needed to defeat the current asymmetric threat. Imaging sensors are 

needed for confirming the presence of a target and (by human judgment of the images) the intent 

of the target. However, remote and unattended imagers have a limited field of view, consume a 

lot of power, and are hard to camouflage and expensive (especially in night operation). Their 

performance is susceptible to weather changes. Acoustic sensors are passive, inexpensive, 
19 

provide accurate bearing, have a 360° field of view and non-line-of-sight capabilities but are 

susceptible to wind. Their detection range varies widely with diurnal and weather changes, and 

their performance degrades rapidly in the presence of multiple targets. Although radars provide 

accurate range, good target classification, and multiple target detection and tracking capabilities, 

they are active, have limited field of view, and consume a lot of power when emplaced in a 

remote, unattended scenario. Other sensors such as magnetic (detection of metal content on a 

target), seismic (ground vibration), and passive infrared (IR) (motion detection) are affordable 

and used in today’s unattended ground sensors configurations as triggers for the more powerconsuming sensors such as imagers and radars. Due to the limitations of each sensing modality, 

fusion of the output of many sensors will be needed to enable accurate determination of the 

detected threat. However, there is a challenge in designing fusion processes that achieve the 

higher confidence and lower false alarm rate expected when the output of different sensors is 

combined. 

Remote human detection with high classification and determination of intent over ranges up to 

50 m is a challenging task. Once detection is made, extracting the intent to determine a pending 

threat is desirable. There are new sensing modalities and signal-processing techniques that are 

being researched to extract this information. Humans engaged in planning a threat carefully plan 

their mission, rehearse, and take every precautionary measure to hide and disguise their intention 

as a normal event. Passive measurement of observable physical items (gait, arm swings, and 

posture—carrying a heavy load might alter the way a human walks) or a vehicle (determining the 

weight it is carrying—abnormal RPMs) can be accomplished. In addition, one can introduce a 

stimulus that generates an element of surprise that alters the rehearsed plan of action. A human 

might, in response, conduct unrehearsed sudden irregular motion. Such behaviors may help 

differentiate threats from nonthreats. 

If time permits, the sensors can monitor the pending threat and extract additional information 

over longer intervals. With networked sensors and fusion of information from all available 

sources, accuracy may increase. Robust communication networks and compression are key, with 

only small bites of information exchanged to continuously monitor and pass forward information 

to sensors that can anticipate the arrival of a threat for efficient processing (detection, 

recognition, and tracking). 

The Joint Directors of Laboratories (JDL) Data Fusion Model is the most widely used method 

for categorizing data-fusion-related functions (Steinberg et al., 1998, 1999). The data fusion 

process involves combining information—in the broadest sense—to estimate or predict the state 

of some aspect of the universe. These processes may be represented in attributive and relational 

states. It can be useful to include consideration of informational and perceptual states in 

addition to the physical state that was the main focus of the original JDL model. The JDL model 

is composed of levels of abstraction, with level 0 being the lowest or minimally processed 

information level and levels 1–4 having increasing levels of abstraction. Typically, hard 

information (information from physical sensors) is operated on at levels 0 and 1. Soft 
20 

information (information from human-based sources and/or related to human decision making) 

can be at any level. The JDL data fusion model was the first fusion model to capture the 

multiple levels-of-abstraction character of the situation-understanding process. Although there 

are many criticisms of the JDL model and many competing models, the JDL model has, in 

general, withstood the test of time. Most of the fusion community has accepted the JDL fusion 

levels. There are frameworks that extend and more practically functionalize the JDL fusion 

model. An example is the Contextual Fusion Model (Antony and Karakowski, 2008, 2009), 

which includes context and handles input derived from hard and soft sensors in addition to 

producing a more specific functionalization of the desired fusion process for implementation. 

There is a question about whether and to what extent classical probability theory (typically, 

Bayesian theory) is meaningful for fusion processes involving cognitive phenomena. There is 

wide agreement that information from physical sensors can be assessed and fused using classical 

probability measures. When cognitive phenomena are considered, however, other 

representations of uncertainty, ones less dependent on statistical assumptions and more able to 

handle data of varying types and sparse data, may be needed. Consider the information 

processing at the cognitive level as a system of subsystems (i.e., local processing in physical 

devices, information fusion in the network, etc.). Uncertainty is propagated upwards. 

Uncertainty may be in the form of the pedigree of information, degraded sensing, digital 

communications degradation/collisions, etc. As these uncertainties are propagated upwards, the 

performance of the higher-level system is adversely affected. It is possible that the higher-level 

uncertainties will not be described appropriately by probabilities (which may have been 

inappropriately “summed” in prior efforts). There are many alternatives to classical probabilistic 

(often Bayesian) methods. Dezert-Smarandache Theory (DSmT) has had success in robotics. 

Dempster-Shafer Theory has been applied in many areas and has been useful for “real-life” 

problems. The Transferable-Belief Model (TBM) has been successfully used in marketing, and 

Analysis of Competing Hypotheses (ACH) has been used in human intelligence. All of these 

methods have had success in additional areas. Possibility theory is also a candidate for 

assessment of propagated uncertainties. Bossé et al. (2007) discuss various methods for 

representing uncertainty in fusion processes. 

It is known that in some cases, humans can sense context very rapidly. This often requires 

experience within the local context as well as an openness to hunches. The process may work in 

a fractal fashion, with sensory and memory inputs ranging across a large set of scales (e.g., local 

smells, ambient noise, arrangement and density of visual detail at many scales, and building 

arrangements). It can also include dynamic human traffic flow fields, which invoke other 

humans as sensing elements around anomalous individuals or items. Complicating this issue are 

cultural components and sensitivity, which form strong structural elements of the cognitive 

landscape and include social interaction norms. Individuals who display these talents may have 

traits linked to those displayed by ADHD (attention deficit hyperactivity disorder)-type 

individuals (New Hope Media LLC, 2010; About.com, 2010). 
21 

Obvious adversarial action at the tactical level may take place over short time periods on about 

3 s or less. However, remote detection of covert intent need not and preferably should not occur 

mainly at this time scale. Indicators of adversarial intent can be gathered over longer time 

intervals using human intelligence and social network analysis. These indicators are typically 

seen best by trained Soldiers and intelligence experts. Much bang for the buck may be gained by 

understanding how the Soldier-user observes and reacts. How do we train? How are quick 

decisions made? What makes an expert? What are best practices? 

The emphasis here has been on the fusion of hard information from physics-based sensors 

because such fusion is currently not yet feasible and designing it is a huge task in itself. In future 

operations, all-source fusion (“hard/soft fusion”), where the input comes from all available 

sources (physical sensors, informational resources, human reports, etc.), will certainly be 

required. 

Assessing one person by another is actually the analysis of information associated with people 

and their actions and surroundings. The analysis of a person’s “intent” is problematic because 

some individuals may be performing deeds without full knowledge of their intent or actions; 

others may be highly engaged in their belief systems; and finally, individuals may be acting with 

limited knowledge of the full scale of the results of their actions. So, it appears that rather than 

determining intent, it may be better to assess whether someone’s planned actions (thoughts) 

resulting in an outcome that should be acted upon is counter to our mission goals and objectives. 

Accordingly, from a cognitive science point of view, knowledge of the plan and overt indicators 

of the plan should be a main focus for detection. Intent is not always something that is 

attributable to the individual at the end stage of an attack plan (e.g., “bait” children locked in a 

hot car and a scenario designed to attract sympathetic Soldiers as triggers for a vehicle-borne 

improvised explosive device [VBIED]). The scenario itself may be the critical element, and the 

individuals that initiate an attack may exhibit no overt indicators of hostility if they are not in the 

loop. To reiterate, it may be necessary to explicitly address the separation of intent vs. plan to 

deal with bait individuals who may have no intent but are elements of a detectable plan. The 

cognitive issues may be better focused by considering the difference between individuals with or 

without situations reflecting a plan vs. individuals with or without intent. Detecting anomalous 

situations, per se, may be beyond the scope of this document, although it forms a context for 

detecting “intent.” 

The concept of adversarial intent needs to be well defined. The covert tactical adversarial intent 

is not just a generalized “I hate the American soldiers.” Certainly, we cannot detain people until 

they decide to agree with us or like us. It will be important to always put cultural context into 

interpretation of results. All research should be aware of the cultural context in which behavioral 

responses can be properly interpreted. Contextually (at the very least), a sociocultural and 

anthropological framework for detecting intent must be well understood for the research in this 

domain to have any degree of efficacy. It would be appropriate to conduct cultural 
22 

anthropological studies to create the proper context. Most importantly, one must define what is 

“normal” for a particular operational environment. 

Consideration of the local population as an indirect sensor (e.g., when normally populated areas 

become sparsely populated without explanation and when normally friendly locals suddenly give 

Soldiers a clearly hostile stare) may be worthwhile. Using a population as an indirect sensor net 

works only when a priori knowledge exists and the population adjusts its behavior to what may 

be considered anomalous for a particular venue, neighborhood, or district. Otherwise, there is no 

implied action (other than self-preservation) for that population. If the population detects an 

imminent threat soon, that places the population in essentially the same position that the Soldier 

is in—a naïve (relative to a hostile plan) observer who may detect such a change and, in turn, 

indirectly detect an imminent event even though detailed information about a perpetrator or 

detonation location is not evident. It should be noted that a critical difference between the 

population and the Soldier observer is that the Soldier is primed to act. This arguably makes the 

Soldier a better “sensor” if one subscribes to the proposition that environmental cues can lead to 

action and that without the potential for action, perception is degraded. A consequence may be 

that the detection of and behavioral response to someone “who does not belong here” can be 

partially triggered by the difference in observable biomarkers (personal smell, gait, facial 

expression, dress, self-isolation/reticence) and highlight the need to consider the context for any 

such detection system or approach. 

V. S. Subramahnian and his colleagues at the University of Maryland have produced a portal 

called SOMA (Subramahnian, 2010) that catalogues information about terrors groups in the 

Middle East. In particular, Subrahmanian and his colleagues have been able to derive patterns 

and rules underlying the operation of these groups by analyzing news blogs, AP wire reports, and 

other online information. Having access to such information would greatly help the fusion 

process. Based on contextual information, it could help the group manning a checkpoint to 

change the normal flow of people or vehicles queuing up at a checkpoint to thwart a terrorist. A 

proactive approach of dealing with the enemy entails playing a cat-and-mouse game with an 

unknown, or only partially known, enemy. Furthermore, the unknown enemy has a chance to 

observe how a checkpoint is managed without being noticed. Recent events, such as the use of a 

U.S. citizen for surveillance of the sites in Mumbai (including the Jewish Center) for a later 

terrorist attack, clearly point to an enemy that adapts to any adopted security procedure. The 

availability of software agents or tools that make contextual information available during the 

fusion process would enhance the research. 

3.4 System Design, Testing, and Execution 

Developing and evaluating sensor systems for remote characterization of human intent should 

incorporate considerations of operational execution. Although the expectation is that an intent 

recognition system could be employed in a variety of operational settings, the problem can be 

simplified to address two measurement paradigms. First, a system may be employed in a passive 
23 

manner so humans are merely observed and their intent is inferred from their current state. As an 

example, if a subject is approaching a checkpoint, it may be feasible to employ sensors to 

estimate that individual’s physiological state, the physics of their motion, or even the types of 

materials on their person. This type of system implementation involves no interaction with the 

person of interest. Along with passive observation and evaluation, there may be planned 

perturbation of either the human or the environment to elicit a stimulus/response effect that may 

amplify the relevant intent metrics. Perturbations can involve a variety of physical and 

psychological stimuli. One example of a physical perturbation might be the placement of an 

obstacle for which a subject must modify his or her walking path. Such a perturbation might 

augment changes in motion due to hidden loads or cognitive stress. Psychological stimuli could 

be used to evoke a physical response or change in behavior that might amplify indicators of 

human intent. Examples of this are a television screen with the subject’s image on it to let them 

know that they are being watched or some modification to a checkpoint procedure to affect 

someone’s plans. The objective of these and other stimuli is to disrupt the plans of potential 

human threats and elicit an observable response. Whether the ultimate implementation of a 

system is projected for a passive manner or in a stimulus/response application, this element of a 

system’s execution should be addressed in the development and validation process. 

Many different kinds of hierarchical architectures for information processing may be considered. 

All of these networks will most likely be semiautonomous at the lowest levels near the devices 

and cooperative at higher levels. As one proceeds up the hierarchy, the information should 

increase in quality, i.e., have an increasing detection rate and a decreasing false alarm rate. The 

hierarchies should exploit redundancy among the devices. 

It is not expected that any one sensor will provide the complete solution but instead a 

combination of sensors and sensor metrics will be required for remote intent prediction. The 

performance of a given system should be validated with a human subject dataset sufficiently 

large enough to address issues related to a subject’s variability and confounding factors. For 

example, if we consider the possibility of estimating intent from human physiology, it is possible 

that a person with no malicious intent will present physiological indicators that overlap with a 

malicious subject. Also, it is possible that alternative emotional states (e.g., fear and sadness) 

may produce physiological responses that mirror aggression or malicious intent. A large set of 

human subjects and experimental conditions should help sort out these factors. 

4. Coordination 

The infrastructure for R&D for remote detection of adversarial intent is already in place in 

academia, government laboratories, and industry. The way in which researchers and developers 

in various disciplines and organizations collaborate to pursue larger goals will be a large factor in 

determining the rapidity with which R&D is accomplished. Research in this area involves 
24 

kinesiology, neurophysiology, psychology, cognitive science, sociocultural anthropology, and 

information science. Relevant areas include clinical psychology, cognitive neuroscience, 

communications, computer science, criminal science, decision science, developmental 

psychology, ethics, geography, human cognition, industrial organization psychology, linguistics 

and computational linguistics, perception psychology, physical and cultural anthropology, 

psychophysiological psychology, risk and risk management, social psychology, and sociology. 

4.1 Balance Between Near-Term Development and Long-Term Research 

Today, R&D must be achieved with budgets significantly smaller than those of 10 years ago. 

Devices and systems must be cheaper, more robust in performance, and higher in efficiency. It 

is a dual challenge to satisfy these requirements, which are often near and medium term, and, at 

the same time, position the research so that maximum long-term benefits can also be achieved. 

In any long-term research project, there is a multitude of near-term issues that impact the conduct 

of research. On occasion, these near-term issues may hinder progress toward long-term 

solutions. At other times, the near-term issues aid progress toward long-term solutions by 

drawing attention to unforeseen problems that, if recognized and investigated early, significantly 

shorten the path to the long-term goal. The options of focusing exclusively on long-term 

research without recognizing the importance of near-term issues and focusing only on near-term 

issues without cognizance of the need for long-term research are both less than optimal. The 

DOD’s needs require that near-term benefits and long-term goals be balanced against each other. 

The traditional technology transfer paradigm of scientists, engineers, and mathematicians making 

basic discoveries in their research and passing them on to development personnel for 

implementation is no longer the only or best option for R&D. Increasingly, researchers are being 

called upon to interact and collaborate with development personnel to reduce the time necessary 

to build operational systems. It is in a framework of two-way technical collaboration between 

basic researchers and development personnel that issues impacting the long-term development of 

a system can be identified earliest and solved most efficiently. A successful basic research 

program on remote detection of covert tactical adversarial intent of individuals in asymmetric 

operations will be a set of interdependent projects linked interactively with development 

programs. The urgency of the need strongly suggests that 6.2 applied research and 6.3 

development need to begin before 6.1 basic research is complete. In addition, 6.1 basic research, 

which currently has very little access to “real data,” can benefit from 6.2 and 6.3 efforts that will, 

on the way to developing specific systems, likely generate “real data” that can help guide the 6.1 

research. Traditionally, basic research is carried out either by chance or because of a 

demonstrated need over a long period of time. There is currently a great need to carry out basic 

research and applied development hand-in-hand so that the entire problem is solved and the time 

taken to produce a deployable solution is shortened. The current situation of asymmetrical 

warfare demands that we get the basic theoretical framework right the first time.
25 

4.2 Collaboration Among Government, Academia, and Industry 

Currently, there are many efforts in and bordering on the areas discussed in section 3. These 

efforts are supported by many different agencies and coordinated through formal and informal 

meetings of investigators at working groups, conferences, and other events. Some of these 

efforts by DHS, ARL/HRED, ARL/ARO, NVESD, AFRL, NRL, AFOSR, and IARPA are 

mentioned in the Introduction. Given the current constraints on federal, academic, and industrial 

budgets, coordinating future efforts at a deep level and thereby ensuring quicker and greater 

payoff are given high priority. The DOD, DHS, IARPA, the entire Federal Government, state 

and local governments, academia, and industry all have considerable interest in remote detection 

of adversarial intent. Consistent with the approach mentioned in section 4.1, DOD’s effort 

should contain 6.1, 6.2, and 6.3 components. Small, newly emerging companies often connected 

with academic research groups constitute one dynamic factor in this R&D. These companies 

should be supported through programs such as Small Business Innovation Research (SBIR) and 

Small Business Technology Transfer (STTR). Three-way collaboration between government, 

academia, and industrial organizations should be maintained through these programs and other 

channels. 

5. A Path to the Future 

In this report, we have summarized the state of the art in remote detection of adversarial intent 

and have pointed out the need for coordinating R&D in many dimensions, including 

cognitive/perceptual phenomena, sensing and information fusion, near-term development, longterm research, and different types of organizations (government, academia, and industry). The 

scientific and administrative factors to support rapid progress in R&D of remote detection of 

adversarial intent are all in place. 

5.1 Recommendations 

Comprehensive DOD, DHS, IARPA, and Federal R&D programs are required to promote rapid 

progress. Specific recommendations are as follows: 

• The Federal Government should fund R&D programs with the objective of producing a 

theoretically founded a prototype system for remote detection of covert tactical adversarial 

intent of individuals in asymmetric operations within 5 years and a working operational 

system within 10 years. 

• The Federal Government should continue to provide broad support for academic and 

industrial efforts both in remote detection of adversarial intent and in areas (such as linkage 

of these systems with databases, media, and human input) that are useful for larger systems 

of systems.
26 

• The strong interdisciplinary nature of remote detection of adversarial intent should be 

reflected in all efforts supported by the Federal Government. 

5.2 Conclusion 

To stop a missile from hitting its intended target, we need to track/influence the missile 

throughout its whole trajectory, starting as soon as it is fired. It is even better to prevent the 

missile from being produced in the first place. In much the same way, to stop terrorists before 

they reach a target, we should track them through the planning and rehearsal stages and 

discourage them from even entering these stages. Research about this is not the research 

described in this report, which handles the “end stage” of the adversarial intent phenomenon. 

However, it is important research at “the initiation” of the adversarial intent phenomenon. Such 

research on winning the hearts and minds of populations may work well in reducing the 

occurrence of adversarial intent (Atran, 2008a, 2008b). The reduction will, however, never be to 

zero, and the research described here will always be needed. 

As the need for more comprehensive security in asymmetric situations grows, so does the need 

for prioritizing remote detection of adversarial intent. Uses of remote detection of adversarial 

intent in the civilian economy include crowd control, antidrug and anticrime operations, border 

security, and ensuring the security of government and private personnel and property. The many 

different directions of R&D in remote detection of adversarial intent are evidence of the richness 

of this field. While all of these directions are important and interesting in their own right, the 

task is now to coordinate and focus basic R&D efforts in areas that will quickly lead to creating a 

class of detection techniques with acceptable or better false-positive and false-negative rates. 

The bottom line in security 5–20 years from now will depend on the vigor with which this R&D 

is pursued today. 
27 

6. References 

About.com. http://add.about.com/sitesearch.htm?terms=hyperfocus&SUName=add &TopNode 

=2852 &type=1,2010) (accessed 3 March 2010). 

Alamut, The Secret Doctrines of the Assassins. http://www.alamut.com/subj/ideologies/alamut 

/secDoctrines.html (accessed 3 March 2010). 

Antony, R. T.; Karakowski, J. A. First-Principle Approach to Functionally Decomposing the 

JDL Fusion Model: Emphasis on Soft Target Data. Proceedings of the 11th International 

Conference on Information Fusion, IEEE, 2008. 

Antony, R. T.; Karakowski, J. A. Private note, 2009. 

Atran, S. Who Becomes a Terrorist Today. Perspectives on Terrorism 2008a, 2 (5). 

Atran, S. The Making of a Terrorist: A Need for Understanding from the Field. Testimony 

before the House Appropriations Subcommittee on Homeland Security, Washington, DC, 12 

March 2008b. 

Blechko, A.; Darker, I. T.; Gale, A. G. The Role of Emotion Recognition from Non-Verbal 

Behaviour in Detection of Concealed Firearm Carrying. Proc. Human Factors and 

Ergonomics Soc. 53rd Annual Meeting, 2009; pp 1363–1367. 

Bossé, E.; Roy, J.; Wark, S. Concepts, Models, and Tools for Information Fusion; ARTech 

House: Boston, 2007. 

Ekman, P.; Friesen, W. Facial Action Coding System: A Technique for the Measurement of 

Facial Movement; Consulting Psychologists Press: Palo Alto, CA, 1978. (See also 

http://en.wikipedia.org/wiki/Facial_Action_Coding_System, accessed 3 March 2010.) 

New Hope Media LLC. http://www.additudemag.com/additude /article/612.html,2007 (accessed 

3 March 2010). 

Steinberg, A. N.; Bowman, C. L.; White, F. E. Revisions to the JDL Model. Joint NATO/IRIS 

Conference Proceedings, Quebec, October 1998. 

Steinberg, A. N.; Bowman, C. L.; White, F. E. Sensor Fusion: Architectures, Algorithms, and 

Applications. Proceedings of the SPIE 1999, 3719. 

Subramahnian, V. S. SOMA Terror Organization Portal. http://www.umiacs.umd.edu 

/research/LCCD/projects/stop.jsp,2009 (accessed 3 March 2010). 
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Appendix A. Workshop Participants 

Gregory Arnold and Adam M. Fullenkamp 

Human Effectiveness Directorate 

U.S. Air Force Research Laboratory 

Ann Bornstein 

Computational and Information Sciences Directorate 

U.S. Army Research Laboratory (ARL) 

Troy Brown 

Research Branch 

Defense Academy for Credibility Assessment 

Purush Iyer, John Lavery, Elmar Schmeisser, Stephanie McElhinny, and Mimi Strand 

U.S. Army Research Office, ARL 

Joseph Karakowski 

I2WD 

U.S. Army Communications and Electronics Command (CECOM) 

Frank Morelli 

Human Research and Engineering Directorate, ARL 

Barbara L. O’Kane 

Night Vision and Electronic Sensors Directorate, CECOM 

Michael Scanlon and Nino Srour 

Sensors and Electronic Devices Directorate, ARL 
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Appendix B. Workshop Steering Committee Contact Information 

Ann Bornstein 

Computational and Information Sciences Directorate 

U.S. Army Research Laboratory 

Aberdeen Proving Ground, MD 21005 

Tel: 410-278-8947 (DSN 298-8947) 

annb@arl.army.mil 

Thyagaraju Damarla 

Sensors and Electron Devices Directorate 

U.S. Army Research Laboratory 

2800 Powder Mill Road 

Adelphi, MD 20783-1197 

Tel: 301-394-1266 (DSN 290-1266) 

rdamarla@arl.army.mil 

John Lavery 

Mathematical Sciences Division 

U.S. Army Research Office 

U.S. Army Research Laboratory 

P.O. Box 12211 

Research Triangle Park, NC 27709-2211 

Tel: 919-549-4253 (DSN 832-4253) 

john.lavery2@us.army.mil 

Frank Morelli 

Human Research and Engineering Directorate 

U.S. Army Research Laboratory 

Aberdeen Proving Ground, MD 21005 

Tel: 410-278-8824 (DSN 298-8824) 

frank.morelli@us.army.mil 

Elmar Schmeisser 

Life Sciences Division 

U.S. Army Research Office 

U.S. Army Research Laboratory 

P.O. Box 12211 

Research Triangle Park, NC 27709-2211 

Tel: 919-549-4318 (DSN 832-4318) 

elmar.schmeisser@us.army.mil 
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ABERDEEN PROVING GROUND

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