This PDF file contains the front matter associated with SPIE Proceedings Volume 6965, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Deception detection plays an important role in the military decision-making process, but detecting deception is a challenging task. The deception planning process involves a number of human factors. It is intent-driven where intentions are usually hidden or not easily observable. As a result, in order to detect deception, any adversary model must have the capability to capture the adversary's intent. This paper discusses deception detection in multi-agent systems and in adversary modeling. We examined psychological and cognitive science research on deception and implemented various theories of deception within our approach. First, in multi-agent expert systems, one detection method uses correlations between agents to predict reasonable opinions/responses of other agents (Santos & Johnson, 2004). We further explore this idea and present studies that show the impact of different factors on detection success rate. Second, from adversary modeling, our detection method focuses on inferring adversary intent. By combining deception "branches" with intent inference models, we can estimate an adversary's deceptive activities and at the same time enhance intent inference. Two major kinds of deceptions are developed in this approach in different fashions. Simulative deception attempts to find inconsistency in observables, while dissimulative deception emphasizes the inference of enemy intentions.

Understanding the intent of today's enemy necessitates changes in intelligence collection, processing, and dissemination. Unlike cold war antagonists, today's enemies operate in small, agile, and distributed cells whose tactics do not map well to established doctrine. This has necessitated a proliferation of advanced sensor and intelligence gathering techniques at level 0 and level 1 of the Joint Directors of Laboratories fusion model. The challenge is in leveraging modeling and simulation to transform the vast amounts of level 0 and level 1 data into actionable intelligence at levels 2 and 3 that include adversarial intent. Currently, warfighters are flooded with information (facts/observables) regarding what the enemy is presently doing, but provided inadequate explanations of adversarial intent and they cannot simulate 'what-if' scenarios to increase their predictive situational awareness. The Fused Intent System (FIS) aims to address these deficiencies by providing an environment that answers 'what' the adversary is doing, 'why' they are doing it, and 'how' they will react to coalition actions. In this paper, we describe our approach to FIS which includes adversarial 'soft-factors' such as goals, rationale, and beliefs within a computational model that infers adversarial intent and allows the insertion of assumptions to be used in conjunction with current battlefield state to perform what-if analysis. Our approach combines ontological modeling for classification and Bayesian-based abductive reasoning for explanation and has broad applicability to the operational, training, and commercial gaming domains.

One of the major challenges in human behavior modeling for military applications is dealing with all factors that can influence behavior and performance. In a military context, behavior and performance are influenced by the task at hand, the internal (cognitive and physiological) and external (climate, terrain, threat, equipment, etc.) state. Modeling the behavioral effects of all these factors in a centralized manner would lead to a complex rule-base that is difficult to maintain or expand. To better cope with this complexity we have developed the Capability-based Human-performance Architecture for Operational Simulation (CHAOS). CHAOS is a multi-agent system for human behavior modeling that is based on pandemonium theory. Every agent in CHAOS represents a specific part of behavior, such as 'reaction to threat' or 'performing a patrol task'. These agents are competing over a limited set of resources that represent human capabilities. By combining the element of competition with multiple limited resources, CHAOS allows us to model stress, strain and multi-tasking in an intuitive manner. The CHAOS architecture is currently used in firefighter and dismounted soldier simulations and has shown itself to be suitable for human behavior and performance modeling.

This work extends existing agent-based target movement prediction to include key ideas of behavioral inertia, steady states, and catastrophic change from existing psychological, sociological, and mathematical work. Existing target prediction work inherently assumes a single steady state for target behavior, and attempts to classify behavior based on a single emotional state set. The enhanced, emotional-based target prediction maintains up to three distinct steady states, or typical behaviors, based on a target's operating conditions and observed behaviors. Each steady state has an associated behavioral inertia, similar to the standard deviation of behaviors within that state. The enhanced prediction framework also allows steady state transitions through catastrophic change and individual steady states could be used in an offline analysis with additional modeling efforts to better predict anticipated target reactions.

The modeling of adversarial intent is compared with another area requiring the modeling of human intent - the representation of knowledge in a contract. The symmetry of the parties to a contract is used as an analog of the symmetry required to model hostile parties, where each attempts to monitor and predict the actions of the other. The dynamic construction of undirected, self-extensible structures using associative patterns is described. New methods of constraint reasoning are introduced to allow it to direct the construction of new structure and to allow free structures to crawl over other cognitive structures to modify them or to link structures together. The close integration of existence and time with logic and the use of relations on relations in a multi-layered active structure allow the system to be very much closer both to the reality of battle and the human intention about which it must infer.

In a typical military application, a wireless sensor network will operate in diffcult and dynamic conditions. Communication will be affected by local conditions, platform characteristics and power consumption constraints, and sensors may be lost during an engagement. It is clearly of great importance to decision makers to know what quality of information they can expect from a network in battlefield situations. We propose the development of a supporting technology founded in formal modeling, using stochastic process algebras for the development of quality of information measures. A simple example illustrates the central themes of outcome probability distribution prediction, and time-dependency analysis.

Thales-Raytheon Systems' Firefinder PC Simulation (PCS) tool allows a rapid simulated evaluation of Firefinder radar performance from a personal desktop computer. Firefinder radars are designed to track hostile rocket, artillery and mortar projectiles in order to accurately estimate weapon ground location. The Firefinder tactical code is used within PCS. This design provides a low risk path to rapid prototyping and evaluation of candidate software changes. PCS is used to evaluate candidate software changes to the Firefinder. Candidate design changes which perform well in PCS testing require minimum system level checkout before being checked into the tactical software baseline. The PCS tool contains a simulation engine which reads program control information from input data files. The PCS tool also generates and maintains simulated targets and clutter, simulates the radar signal processing function, performs Monte-Carlo "batch" processing, produces complex target trajectories internally or from an input text file and creates simulation data recording files identical in format to those created by the actual radar.

There is an increasing emphasis on the intelligent use of multiple sensor assets within military applications which is driven by a number of factors. Firstly, the deployment of multiple, co-operative sensors can provide a much greater situational awareness which is a key factor in military decision making at both strategic and tactical levels. Secondly, through careful and timely asset management, military tempo and effectiveness can be maintained and even enhanced such that the mission objectives are optimally prosecuted. Thirdly, intrinsic limitations of individual sensors and their processing demands can be reduced or even eliminated. From a mission perspective, this renders the constraints and frailties of the associated with the sensor network transparent to the military end users. Underpinning all of these factors is the need to adaptively control and manipulate the various sensor search vectors in both space and time. Such a design and operational capability is provided through Cerberus, an advanced design tool developed by Waterfall Solutions Ltd. Within this paper, investigations into a range of different military applications using the Cerberus design environment are reported and assessed in terms of the associated military objectives. These applications include the use of both manned and uninhabited air vehicles as well as land and sea based sensor platforms. The use and benefits of available a priori knowledge such as digital terrain data and mission intelligence can also be exploited within the Cerberus environment to great military advantage.

The Irma synthetic signature prediction code is being developed by the Munitions Directorate of the Air Force Research Laboratory (AFRL/RW) to facilitate the research and development of multi-sensor systems. There are over 130 users within the Department of Defense, NASA, Department of Transportation, academia, and industry. Irma began as a high-resolution, physics-based Infrared (IR) target and background signature model for tactical weapon applications and has grown to include: a laser (or active) channel (1990), improved scene generator to support correlated frame-to-frame imagery (1992), and passive IR/millimeter wave (MMW) channel for a co-registered active/passive IR/MMW model (1994). Irma version 5.0 was released in 2000 and encompassed several upgrades to both the physical models and software; host support was expanded to Windows, Linux, Solaris, and SGI Irix platforms. In 2005, version 5.1 was released after extensive verification and validation of an upgraded and reengineered ladar channel. The reengineering effort then shifted focus to the Irma passive channel. Field measurements for the validation effort include both polarized and unpolarized data collection. Irma 5.2 was released in 2007 with a reengineered passive channel. This paper summarizes the capabilities of Irma and the progress toward Irma 5.3, which includes a reengineered radar channel.

In recent years, military operations have seen an increasing demand for high-fidelity predictive ground target signature modeling in the hyperspectral thermal IR bands (2 to 25 μm). Simulating hyperspectral imagery of large scenes has become a necessary component in evaluating ATR algorithms due to the prohibitive costs and the large volume of data amassed by multi-band imaging sensors. To address this need, MuSES (Multi-Service Electro-optic Signature code), a validated infrared signature prediction program developed for modeling ground targets, has been enhanced to compute bi-directional reflectance distribution radiances and atmospheric propagation hyperspectrally, and to generate hyperspectral image data cubes. In this paper, we present the extensions in MuSES and report on how the additional features have allowed MuSES to be integrated into the Infra-Red Hyperspectral Scene Simulation (IRHSS), a scene simulation tool that efficiently models sensor-weighted hyperspectral imagery of large IR synthetic scenes with full thermal interaction between the target and terrain.

This research investigates how aggregation is currently conducted for simulation of large systems. The focus is on the exploration of the different aggregation techniques for hierarchical lower-level (higher resolution) models into the next higher-level. We develop aggregation procedures between two simulation levels (e.g., aggregation of mission level models into a campaign level model) to address how much and what information needs to pass from the high-resolution to the low-resolution model in order to preserve statistical fidelity. We present a mathematical representation of the simulation model based on network theory and procedures for simulation aggregation that are logical and executable. The proposed process is a collection of various conventional statistical and aggregation techniques, but we present them in a coherent and systematic manner. Our desired real-world application for the developed simulation aggregation process is in the area of military combat. We show preliminary results as applied to a complex hierarchical flying training model. There is no best universal aggregation technique for different simulation models; however, the method developed here is a well-defined set of procedures for statistically sound simulation model aggregation.

This paper contrasts two techniques for analyzing blog content and making use of this information to model blog content. One method uses classical text content and analysis presented for human interpretation. The second method relies on a data mined list of descriptive words characterizing the blogs. We examine the use of different data mining tools, Kryltech's "Subject Search Summarizer", Leximancer, and QUEST, to provide orthogonal and independently generated key word lists. These lists are then converted into Data Models, enabling mathematical modeling of blog content.

Unmanned Aerial Vehicle (UAV) system integration with naval vessels is currently realized in limited form. The operational envelopes of these vehicles are constricted due to the complexities involved with at-sea flight testing. Furthermore, the unsteady nature of ship airwakes and the use of automated UAV control software necessitates that these tests be extremely conservative in nature. Modeling and simulation are natural alternatives to flight testing; however, a fully-coupled computational fluid dynamics (CFD) solution requires many thousands of CPU hours. We therefore seek to decrease simulation time by accelerating the underlying computations using state-of-the-art, commodity hardware. In this paper we present the progress of our proposed solution, harnessing the computational power of high-end commodity graphics processing units (GPUs) to create an accelerated Euler equations solver on unstructured hexahedral grids.

Military camps in out-of-area missions are permanently threatened by rockets, artillery projectiles, and mortar grenades (RAM) launched by terrorists. A good portion of these attacks are undertaken by mortars due to their specific advantages for the warfare of irregular forces and their worldwide distribution. The military installations can be protected by counter-RAM systems consisting of several artillery weapons, radar and electro-optical sensors, C² and fire control computers. A system analysis has shown that the precision of the sensors is vital for defending the camp with low ammunition consumptions. Furthermore, the type of ammunition is also of great impact: 35 mm Ahead ammunition is hardly suited for this application due to its small hit density and low kinetic energy of the sub-projectiles, especially in the case of mortar grenades. Therefore, 155 mm high-explosive (HE) ammunition is investigated using experimentally determined fragment data. Russian mortar projectiles are considered as worst-case RAM targets and their ballistics are mathematically modeled by an air drag function that is also used for computing firing tables. Due to uncertainties of the target positions that are given by an elliptic cylinder for specific sensor parameters, simulations are conducted in order to determine the ammunition consumption. Penetration and detonation criteria for the terminal impact are also considered and the resulting thresholds are displayed in a 3D fragment map. The results show that HE ammunition is superior to low-caliber ammunition because of their high numbers of effective fragments reducing the number of rounds significantly from hundreds to less than ten.

The proliferation of unmanned vehicles carrying tactical payloads in the battle-space has accelerated the need for user-friendly visualization with graphical interfaces to provide remote command and control. Often these platforms and payloads receive their control functions from command centers located half a world away via satellite communications. Operators require situational awareness tools capable of graphically presenting the remote battlefield asset positions and collected sensor data. Often these systems use 2D software mapping tools in conjunction with video for real time situational awareness. The Special Projects Group (SPG) in the Tactical Electronic Warfare Division of the U.S. Naval Research Laboratory has been developing an operator control interface called the Jammer Control Station (JCS) to provide 3D battle-space visualization with built-in, remote EW payload command and control (C²) capabilities. The JCS interface presents the operator with graphic depictions of both the platforms' states and the RF environment. Text based messaging between the JCS and the EW payload reduces the impact of the system on the available bandwidth. This paper will discuss the use of the SIMDIS 3-D visualization tool as a real-time command and control interface for electronic warfare (EW) payloads.

This paper discusses an improved design of vehicle-based mobile terrain profile measurement system that derives the terrain profile by combining information from several different sensors measuring distance, altitudes and position. The main challenge of the measurement system design is to derive the instantaneous dynamic motion of the platform vehicle in order to correct the direct profile elevation measurement from a set of laser optical sensors. By processing the velocity and attitude data from an Inertial Measurement Unit (IMU) and the absolute position data from a Global Positioning System (GPS), a Kalman Filter/Smoother algorithm is utilized in this sensor fusion application as a key step to obtain an accurate measurement of the platform vehicle's dynamic motion. Through the implementation of this approach, not only is a high accuracy of measurement during short-time vehicle dynamic motion achieved, the algorithm also eliminates a sensor drift problem associated with the long term stability of the measurement system. The hardware and software prototype of this design have been implemented, and initial field tests show that the methodology has achieved good measurement accuracy.

This paper describes the requirements, data structures, and algorithms utilized in the run time Player Unit of the OneTESS program. OneTESS is a combined instrumentation suit designed to satisfy the requirements for both training and operational testing being developed by a team lead by AT&T. Specifically we will describe the terrain services and Player Unit services required for geometric pairing and engagement processing along with the accurate database design and procurement strategy required to build it. The paper will also describe a voxel based visualization engine adapted to perform dynamic terrain updates and high accurate test site preparation. We will also describe the process for procuring and testing the fidelity of the terrain environment and describe the analysis to answer the "what is good enough" question within the context of instrumentation accuracies and development strategies. Lastly we will discuss the implications and opportunities afforded by onboard environment models both for future test and training applications as well as in future deployable units.