Evolutionary computation is a rapidly expanding field of research with a long history. Much of that history remains unknown to most practitioners and researchers. This paper offers a review of selected foundational efforts in evolutionary computation. A brief initial overview of the essential components of evolutionary algorithms is presented, followed by a review of early research in artificial life, evolving programs, and evolvable hardware. Comments on theoretical developments and future developments conclude the review.

The Air Force Research Laboratory utilizes a value model as a primary input for space technology planning and budgeting. The Space Sector at AFRL headquarters manages space technology investment across all the geographically disparate technical directorates and ensures that integrated planning is achieved across the space community. The space investment portfolio must ultimately balance near, mid, and far-term investments across all the critical space mission areas. Investment levels and growth areas can always be identified by a typical capability analysis or gap analysis, but the value model approach goes one step deeper and helps identify the potential payoff of technology investments by linking the technology directly to an existing or potential concept. The value of the technology is then viewed from the enabling performance perspective of the concept that ultimately fulfills the Air Force mission. The process of linking space technologies to future concepts and technology roadmaps will be reviewed in this paper, along with representative results from this planning cycle. The initial assumptions in this process will be identified along with the strengths and weaknesses of this planning methodology.

Experiences modeling, simulating, and evaluating satellite and ground support systems using the OPNET tool are presented. The specific instance presented concerns work performed as part of a Nuclear Detonation Detection System (NDS) trade study. Models to be presented include a UHF satellite, mobile and fixed ground systems, and airborne systems. Model presentations start and proceed through scenario, node, component(s), detailed component attributes, and code variations providing a look at engineering level modeling of complex SATCOM systems. Traffic used in the scenarios is continuous transmission of message traffic used to evaluate the ability of UHF systems to perform in a "worst case" high altitude nuclear burst environment. Nuclear effects on RF signal propagation are identified and defined. Models and their simulation results provide a look at the capability of the OPNET tool in evaluating the capability of current as well as proposed future space and terrestrial communications systems from both the operational and acquisition perspectives when the systems are subjected to nuclear disturbed environments.

During the past ten years, the Air Force Research Laboratory (AFRL) has been simultaneously developing high-fidelity spacecraft payload models as well as a robust distributed simulation environment for modeling spacecraft subsystems. Much of this research has occurred in the Distributed Architecture Simulation Laboratory (DASL). AFRL developers working in the DASL have effectively combined satellite power, attitude pointing, and communication link analysis subsystem models with robust satellite sensor models to create a first-order end-to-end satellite simulation capability. The merging of these two simulation areas has advanced the field of spacecraft simulation, design, and analysis, and enabled more in-depth mission and satellite utility analyses. A core capability of the DASL is the support of a variety of modeling and analysis efforts, ranging from physics and engineering-level modeling to mission and campaign-level analysis. The flexibility and agility of this simulation architecture will be used to support space mission analysis, military utility analysis, and various integrated exercises with other military and space organizations via direct integration, or through DOD standards such as Distributed Interaction Simulation. This paper discusses the results and lessons learned in modeling satellite communication link analysis, power, and attitude control subsystems for an end-to-end satellite simulation. It also discusses how these spacecraft subsystem simulations feed into and support military utility and space mission analyses.

A terrain severity measurement system utilizing non-contact optical scanning laser technologies employed in on-road profiling has been developed to make detailed measurements of the relative smoothness of all types of terrain from paved roads to extreme off-road conditions. The objectives included operation in all climatic conditions, simplified operation, and rapid availability of data. Accelerometers and inclinometers are used to measure laser sensor movement in order to eliminate measurement errors due to vehicle pitch and roll. A GPS receiver is used to correlate terrain profile information to position and elevation data. The end result is an accurate description of the longitudinal and lateral terrain profile that can be used to characterize the terrain and within vehicle modeling and simulation programs.

Load data representing severe customer usage is needed throughout a chassis development program; the majority of these chassis loads originate with the excitation from the road. These chassis loads are increasingly derived from vehicle simulations. Simulating a vehicle traversing long roads is simply impractical, however, and a greatly reduced set of characteristic roads must be found. In order to characterize a road, certain modeling assumptions must be made. Several models have been proposed making various assumptions about the properties that road profiles possess. The literature in this field is reviewed before focusing on two modeling assumptions of particular interest: the stationarity of the signal (homogeneity of the road) and the corresponding interval over which previous data points are correlated to the current data point. In this work, 2-D topographic road profiles are considered to be signals that are realizations of a stochastic process. The objective of this work is to investigate the stationarity assumption and the interval of influence for several carefully controlled sections of highway pavement in the United States. Two statistical techniques are used in analyzing these data: the autocorrelation and the partial autocorrelation. It is shown that the road profile signals in their original form are not stationary and have an extremely long interval of influence on the order of 25m. By differencing the data, however, it is often possible to generate stationary residuals and a very short interval of influence on the order of 250mm. By examining the autocorrelation and the partial autocorrelation, various versions of ARIMA models appear to be appropriate for further modeling. Implications to modeling the signals as Markov Chains are also discussed. In this way, roads can be characterized by the model architecture and the particular parameterization of the model. Any synthetic road realized from a particular model represents all profiles in this set. Realizations of any length can be generated, allowing efficient simulation and timely information about the chassis loads that can be used for design decisions. This work provides insights for future development in the modeling and characterization of 2-D topographic road profiles.

The purpose of this research is to investigate imaging-based methods to reconstruct 3D CAD models of real-world objects. The methodology uses structured lighting technologies such as coded-pattern projection and laser-based triangulation to sample 3D points on the surfaces of objects and then to reconstruct these surfaces from the dense point samples. This reverse engineering (RE) research presents reconstruction results for a military tire that is important to tire-soil simulations. The limitations of this approach are the current level of accuracy that imaging-based systems offer relative to more traditional CMM modeling systems. The benefit however is the potential for denser point samples and increased scanning speeds of objects, and with time, the imaging technologies should continue to improve to compete with CMM accuracy. This approach to RE should lead to high fidelity models of manufactured and prototyped components for comparison to the original CAD models and for simulation analysis. We focus this paper on the data collection and view registration problems within the RE pipeline.

To facilitate interoperability of models in a scalable environment, and provide a relevant virtual environment in which Survivability technologies can be evaluated, the US Army Research Development and Engineering Command (RDECOM) Modeling Architecture for Technology Research and Experimentation (MATREX) Science and Technology Objective (STO) program has initiated the Survivability Thread which will seek to address some of the many technical and programmatic challenges associated with the effort. In coordination with different Thread customers, such as the Survivability branches of various Army labs, a collaborative group has been formed to define the requirements for the simulation environment that would in turn provide them a value-added tool for assessing models and gauge system-level performance relevant to Future Combat Systems (FCS) and the Survivability requirements of other burgeoning programs. An initial set of customer requirements has been generated in coordination with the RDECOM Survivability IPT lead, through the Survivability Technology Area at RDECOM Tank-automotive Research Development and Engineering Center (TARDEC, Warren, MI). The results of this project are aimed at a culminating experiment and demonstration scheduled for September, 2006, which will include a multitude of components from within RDECOM and provide the framework for future experiments to support Survivability research. This paper details the components with which the MATREX Survivability Thread was created and executed, and provides insight into the capabilities currently demanded by the Survivability faculty within RDECOM.

In the analysis and design of military uniforms and body armor systems it is helpful to quantify the effects of the clothing/armor system on a wearer's physical performance capabilities. Toward this end, a clothing modeling framework for quantifying the mechanical interactions between a given uniform or body armor system design and a specific wearer performing defined physical tasks is proposed. The modeling framework consists of three interacting modules: (1) a macroscale fabric mechanics/dynamics model; (2) a collision detection and contact correction module; and (3) a human motion module. In the proposed framework, the macroscopic fabric model is based on a rigorous large deformation continuum-degenerated shell theory representation. The collision and contact module enforces non-penetration constraints between the fabric and human body and computes the associated contact forces between the two. The human body is represented in the current framework, as an assemblage of overlapping ellipsoids that undergo rigid body motions consistent with human motions while performing actions such as walking, running, or jumping. The transient rigid body motions of each ellipsoidal body segment in time are determined using motion capture technology. The integrated modeling framework is then exercised to quantify the resistance that the clothing exerts on the wearer during the specific activities under consideration. Current results from the framework are presented and its intended applications are discussed along with some of the key challenges remaining in clothing system modeling.

Computer models have been developed and used to predict the performance of vehicles equipped with advanced fuel and power train technologies such as hybrid electric or fuel cells. However, simulations that describe the interaction of the vehicle with the rest of the vehicle fleet and infrastructure are just emerging. This paper documents the results of an experiment to demonstrate the utility of these types of simulations. The experiment examined the business case of fielding hybrid electric, high-mobility multipurpose wheeled vehicles (HE HMMWVs) in a future Army organization. The hypothesis was that fielding HE vehicles would significantly reduce fuel consumption due to the economy offered by the HE technology and reducing the number of generators as a result of using the vehicles to generate electrical power. The Logistical and Combat Systems Simulation (LOCSS) was used to estimate differences in fuel consumption and associated equipment during a 72-hour operation with and without HE HMMWVs. There was a 25 percent reduction in fuel consumption over the systems examined. However, due to the relatively low density of the HE vehicles in the organization, the total difference in fuel consumption was not operationally significant; and the savings in fuel costs did not overcome the additional procurement costs over a twenty-year life cycle.

The article presents the results of a large scale design space exploration for the hybridization of two off-road vehicles, part of the Future Tactical Truck System (FTTS) family: Maneuver Sustainment Vehicle (MSV) and Utility Vehicle (UV). Series hybrid architectures are examined. The objective of the paper is to illustrate a novel design methodology that allows for the choice of the optimal values of several vehicle parameters. The methodology consists in an extensive design space exploration, which involves running a large number of computer simulations with systematically varied vehicle design parameters, where each variant is paced through several different mission profiles, and multiple attributes of performance are measured. The resulting designs are filtered to choose the design tradeoffs that better satisfy the performance and fuel economy requirements. At the end, few promising vehicle configuration designs will be selected that will need additional detailed investigation including neglected metrics like ride and drivability. Several powertrain architectures have been simulated. The design parameters include the number of axles in the vehicle (2 or 3), the number of electric motors per axle (1 or 2), the type of internal combustion engine, the type and quantity of energy storage system devices (batteries, electrochemical capacitors or both together). An energy management control strategy has also been developed to provide efficiency and performance. The control parameters are tunable and have been included into the design space exploration. The results show that the internal combustion engine and the energy storage system devices are extremely important for the vehicle performance.

Hardware-in-the-loop (HIL) simulation is rapidly evolving from a control prototyping tool to a system modeling, simulation, and synthesis paradigm synergistically combining many advantages of both physical and virtual prototyping. This paper provides a brief overview of the key enablers and numerous applications of HIL simulation, focusing on its metamorphosis from a control validation tool into a system development paradigm. It then describes a state-of-the art engine-in-the-loop (EIL) simulation facility that highlights the use of HIL simulation for the system-level experimental evaluation of powertrain interactions and development of strategies for clean and efficient propulsion. The facility comprises a real diesel engine coupled to accurate real-time driver, driveline, and vehicle models through a highly responsive dynamometer. This enables the verification of both performance and fuel economy predictions of different conventional and hybrid powertrains. Furthermore, the facility can both replicate the highly dynamic interactions occurring within a real powertrain and measure their influence on transient emissions and visual signature through state-of-the-art instruments. The viability of this facility for integrated powertrain system development is demonstrated through a case study exploring the development of advanced High Mobility Multipurpose Wheeled Vehicle (HMMWV) powertrains.

3D models of real world environments are becoming increasingly important for a variety of applications: Vehicle simulators can be enhanced through accurate models of real world terrain and objects; Robotic security systems can benefit from as-built layout of the facilities they patrol; Vehicle dynamics modeling and terrain impact simulation can be improved through validation models generated by digitizing real tire/soil interactions. Recently, mobile scanning systems have been developed that allow 3D scanning systems to undergo the full range of motion necessary to acquire such real-world data in a fast, efficient manner. As with any digitization system, these mobile scanning systems have systemic errors that adversely affect the 3D models they are attempting to digitize. In addition to the errors given by the individual sensors, these systems also have uncertainties associated with the fusion of the data from several instruments. Thus, one of the primary foci for 3D model building is to perform the data fusion and post-processing of the models in such a manner as to reconstruct the 3D geometry of the scanned surfaces as accurately as possible, while alleviating the uncertainties posed by the acquisition system. We have developed a modular scanning system that can be configured for a variety of application resolutions, as well as the algorithms necessary to fuse and process the acquired data. This paper presents the acquisition system and the tools utilized for constructing 3D models under uncertain real-world conditions, as well as some experimental results on both synthetic and real 3D data.

Optimization technology changes the design process into a process driven directly by computational analysis. Different techniques are discussed and classified with respect to their applicability in design. Gaps in transferring technology to practical application as well as missing solutions are identified.

This research describes the dynamic modeling and numerical simulation of an autonomous underwater vehicle (AUV) with five hydrodynamic control surfaces, necessary for the development of an autopilot algorithm, based solely upon analytical methodologies. The purpose of this research was to demonstrate the ability to develop a low order approximation of the dynamics and control characteristics of an underwater vehicle that is complete enough to validate a specific design before physical construction begins; therefore, allowing for a more cost effective virtual design, test, and evaluation process. The AUV model developed in this study takes into consideration inertia, hydrostatic forces, hydrodynamic forces, propulsion forces, control fin forces, added mass, and damping. The model assumes that the vehicle is sufficiently far enough away from the bottom and the surface so that their effects can be ignored. The necessary stability and control derivatives were determined through the use of engineering formulae. The mathematical model represents a general, nonlinear, six degrees of freedom model, and it is similar to those used to carry out atmospheric flight simulations. The non-linear model was linearized about the design (equilibrium) condition to obtain a linear state-space vehicle model.

Multibody Dynamic Simulation has been used to successfully simulate a wide variety of military vehicles and predict the safety, mobility, stability, and operating loads of the complete system. The objective is to predict accurate vehicle behavior under any operating condition and provide the basis for making engineering design changes to improve performance. Vehicles used for agriculture, construction, recreation, and military applications all involve similar modeling requirements and challenges to account for the wide range of operating conditions. Results of the multibody dynamic simulation process produce time-histories of the position, velocity, acceleration, and reaction forces on all parts. These results have been used to characterize the vehicle performance attributes.

A swept wing tailless vehicle platform is well known in the radio control (RC) and sailing aircraft community for excellent spiral stability during soaring or thermaling, while exhibiting no Dutch roll behavior at high speed. When an unmanned aerial vehicle (UAV) is subjected to fly a mission in a rugged mountainous terrain where air current or thermal up-drift is frequently present, this is great aerodynamic benefit over the conventional cross-tailed aircraft which requires careful balance between lateral and directional stability. Such dynamic characteristics can be studied through vehicle dynamic modeling and simulation, but it requires configuration aerodynamic data through wind tunnel experiments. Obtaining such data is very costly and time consuming, and it is not feasible especially for low cost and dispensable UAVs. On the other hand, the vehicle autonomy is quite demanding which requires substantial understanding of aircraft dynamic characteristics. In this study, flight dynamics of an UAV platform based on flying wing with a large winglet was investigated through analytical modeling and numerical simulation. Flight dynamic modeling software and experimental formulae were used to obtain essential configuration aerodynamic characteristics, and linear flight dynamic analysis was carried out to understand the effect of wing sweep angle and winglet size on the vehicle dynamic characteristics.

In the area of research on unmanned ground vehicles (UGV), one major problem is limited operating duration of robotics vehicles due to energy losses. There is a need for systematic analysis of locomotion and energy dynamics, which would enable an efficient design of the vehicle. For this purpose, a multifunction simulator tool is required which can read several input variables that describe the vehicle and compute detailed analysis of its energy dynamics. This research presents a generic locomotion simulator for a UGV (SimUGV). SimUGV's goal is to help vehicle designers develop efficient vehicles by optimizing design variables to minimize the energy losses for the vehicle. SimUGV has a powerful GUI interface which allows users to compare multiple test runs and visualize the data in a variety of ways. To illustrate the capabilities of the simulator, we present a case study conducted on the energy dynamics of a skid steering robotic vehicle. Two major constituent components of energy losses/consumption for a skid steering vehicle are - losses in skid steer turning, and losses in rolling. Using SimUGV, we present a detailed energy loss analysis of the vehicle's different turning modes; elastic mode steering, half-slip steering, skid turns, low radius turns, and zero radius turns. Each of the energy loss components is modeled from physics in terms of the design variables. The effect of design variables on the total energy losses/consumption is then studied using simulated data for different types of surfaces i.e. hard surfaces and muddy surfaces. Finally, we make suggestions about efficient vehicle design choices in terms of the design variables.

A behavior-based, simple interaction model inspired by molecular interaction field depicted by the Lennard-Jones function is examined for the averaged interaction in swarming. The modeled kinematic equation of motion contains only one variable, instead of a multiple state variable dependence a more complete dynamics entails. The model assumes a spatial distribution of the potential associate with the swarm. The model has been applied to examine the formation of swarm and the results are reported. The modeling can be reflected in an equilibrium theory for the operation of a swarm of mini-UAVs pioneered by Szu, where every member serves the mission while exploiting other's loss, resulting in a zero-sum game among the team members.

Light armoured vehicles will rely on sensors, on-board computing and digital wireless communications to achieve improved performance and survivability. Constrained by low latency response to threats, individual vehicles will share sensory information with other platoon vehicles benefiting from a flexible, dynamic, self-adapting network environment. As sensor and computing capability increases, network communications will become saturated. To understand the operational requirements for these future vehicle networks, the High Capacity Technical Communications Network (HCTCN) Low Bandwidth Testbed (LBTB) has been developed to provide a simulated environment for the radios and candidate database and transmission protocols selected. These concepts and approach to network communications will be discussed in the paper.

This paper discusses how evolutionary algorithms are related to multi-agent systems and the possibility of military applications using the two disciplines. In particular, we present a game theoretic model for multi-agent resource distribution and allocation where agents in the environment must help each other to survive. Each agent maintains a set of variables representing actual friendship and perceived friendship. The model directly addresses problems in reputation management schemes in multi-agent systems and Peer-to-Peer distributed systems. We present algorithms based on evolutionary game process for maintaining the friendship values as well as a utility equation used in each agent's decision making. For an application problem, we adapted our formal model to the military coalition support problem in peace-keeping missions. Simulation results show that efficient resource allocation and sharing with minimum communication cost is achieved without centralized control.

This paper surveys the various approaches used to apply evolutionary algorithms to develop artificial neural networks that solve pattern recognition, classification, and other tasks. These approaches are classified into four groups, each addressing one aspect of an artificial neural network: (a) evolving connection weights; (b) evolving neural architectures; (c) evolving an ensemble of networks; and (d) evolving node functions. Hybrid approaches are also discussed.

This paper outlines our long-term vision for integrating robust machine learning as an approach to the modern battlefield. We will develop the architecture for an Integrated Learning System (ILS) that will enable representation tools to maximize the utility of data collected by distributed sensors. This project will suggest a system for data capture, processing, retrieval and analysis and focus on the development of semantic interoperability for ontology alignment and the ability to learn from experiences, so that performance improves as it accumulates knowledge resulting in the ability to learn new object/event classes and improve its classification accuracy. To illustrate the notion of robust learning from distinct representations of sensor data from a common source, we offer an application where a LCS addresses automatic target recognition (ATR) in extended operating conditions (EOCs). The LCS-based robust ATR system performed well, resulting in powerful ATR rules that generalize over multiple feature types, with accuracy over 99% and robustness over 80%. To illustrate the notion of ontology enabling learning, we outline preliminary experiments with a network of LCSs integrating ATR via a simple vehicle ontology.

Recent military operations have demonstrated the use by adversaries of non-traditional or asymmetric military tactics to offset US military might. Rogue nations with links to trans-national terrorists have created a highly unpredictable and potential dangerous environment for US military operations. Several characteristics of these threats include extremism in beliefs, global in nature, non-state oriented, and highly networked and adaptive, thus making these adversaries less vulnerable to conventional military approaches. Additionally, US forces must also contend with more traditional state-based threats that are further evolving their military fighting strategies and capabilities. What are needed are solutions to assist our forces in the prosecution of operations against these diverse threat types and their atypical strategies and tactics. To address this issue, we present a system that allows for the adaptation of a multi-resolution adversarial model. The developed model can then be used to support both training and simulation based acquisition requirements to effectively respond to such an adversary. The described system produces a combined adversarial model by merging behavior modeling at the individual level with aspects at the group and organizational level via network analysis. Adaptation of this adversarial model is performed by means of an evolutionary algorithm to build a suitable model for the chosen adversary.

State-of-the-art signal compression and reconstruction techniques utilize wavelets. However, recently published research demonstrated that a genetic algorithm (GA) is capable of evolving non-wavelet inverse transforms that consistently outperform wavelets when used to reconstruct one- and two-dimensional signals under conditions subject to quantization error. This paper summarizes the results of a series of three follow-on experiments. First, a GA is developed to evolve matched forward and inverse transform pairs that simultaneously minimize the compressed file size (FS) and the squared error (SE) in the reconstructed file. Second, this GA is extended to evolve a single set of coefficients that may be used at every level of a multi-resolution analysis (MRA) transform. Third, this GA is expanded to achieve additional SE reduction by evolving a different set of coefficients for each level of an MRA transform. Test results indicate that coefficients evolved against a single representative training image generalize to effectively reduce SE for a broad class of reconstructed images.

The computation or prediction of plane wave back scattered field is one of the major design considerations of future aircraft and weapon systems. The task of computing the electromagnetic backscattered field of an airframe structure is by no means a new endeavor. Whereas predicting a minimal backscattered field return under the manipulation of airframe geometry in the context of multidisciplinary design is considered the most prudent approach to obtain the optimal solution and is a new endeavor. The objective of this paper is to develop a mathematical method to couple the backscattered field with the defined aerodynamic performance constraints in the design process of future airframes. This paper will address the coupling of the electromagnetics discipline in a Multidisciplinary Design Optimization (MDO) scheme that includes the mathematical optimization of aerodynamics, or Aero discipline, including the coupling effects of aerodynamic performance (maximum value) with backscattered field return (minimum value) of a Zeroth (0^th) Order Mode wing planform.

The ability of many insects, especially moths, to locate either food or a member of the opposite sex is an amazing achievement. There are numerous scenarios where having this ability embedded into ground-based or aerial vehicles would be invaluable. This paper presents results from a 3-D computer simulation of an Unmanned Aerial Vehicle (UAV) autonomously tracking a chemical plume to its source. The simulation study includes a simulated dynamic chemical plume, 6-degree of freedom, nonlinear aircraft model, and a bio-inspired navigation algorithm. The emphasis of this paper is the development and analysis of the navigation algorithm. The foundation of this algorithm is a fuzzy controller designed to categorize where in the plume the aircraft is located: coming into the plume, in the plume, exiting the plume, or out of the plume.

Genetic and evolutionary optimization techniques have been used in military antenna research and design at many levels, ranging from electrically-small antenna element design to broadband applications and array-pattern control. In this paper, we summarize in-house work in these areas, conducted at the Antenna Technology Branch of the Air Force Research Laboratory Sensors Directorate. In particular, we highlight areas where differences in modeling and simulation techniques have proven crucial in avoiding premature convergence and obtaining a valid optimal solution.

We present a novel application of genetic algorithm (GA) to optimal feature set selection in supervised learning using support vector machine (SVM) for steganalysis. Steganalysis attempts to determine whether a cover object (in our case an image file) contains hidden information. This is a bivariate classification problem: the image either does or does not contain hidden data. Our SVM classifier uses a training set of images with known classification to "learn" how to classify images with unknown classification. The SVM uses a feature set, essentially a set of statistical quantities extracted from the image. The performance of the SVM classifier is heavily dependent on the feature set used. Too many features not only increase computation time but decrease performance, and too few features do not provide enough information for accurate classification. Our steganalysis technique uses entropic features that yield up to 240 features per image. The selection of an optimum feature set is a problem that lends itself well to genetic algorithm optimization. We describe this technique in detail and present a "GA optimized" feature set of 48 features that, for our application, optimizes the tradeoff between computation time and classification accuracy.

Signal processing problems including the speaker identification problem require processing of real-valued feature vectors. Traditional cepstral encoding combined with clustering algorithms handle the closed-set speaker identification problem quite well but when it comes to the open-set problem, clustering methods show lack of performance. Furthermore, many clustering algorithms lack adaptability and the ability to learn on-the-fly. Genetic classifier systems are adaptive and they have the ability for open-ended learning. We introduce a genetic classifier system approach to the speaker identification problem and several classifier knowledge representation methods for open-set speaker identification. Experimental results show that the new system works quite well for the open-set speaker identification problem.

The development of electronic countermeasures against target track radars is both an expensive and time-consuming process. One method of cutting development time is to use genetic algorithms to develop electronic countermeasures using integrated software models of the target track radar and the jammer. This paper demonstrates the feasibility of this idea by using a genetic algorithm to optimize the parameters of a range gate pull-off electronic countermeasure technique to break the target lock of a generic radar split gate range tracker.

We use case injected genetic algorithms to learn how to competently play computer strategy games that involve long range planning across complex dynamics. Imperfect knowledge presented to players requires them adapt their strategies in order to anticipate opponent moves. We focus on the problem of acquiring knowledge learned from human players, in particular we learn general routing information from a human player in the context of a strike force planning game. By incorporating case injection into a genetic algorithm, we show methods for incorporating general knowledge elicited from human players into future plans. In effect allowing the GA to take important strategic elements from human play and merging those elements into its own strategic thinking. Results show that with an appropriate representation, case injection is effective at biasing the genetic algorithm toward producing plans that contain important strategic elements used by human players.

This paper presents an Evolutionary Algorithms (EAs) based approach to identify effective courses of action (COAs) in Effects Based Operations. The approach uses Timed Influence Nets (TINs) as the underlying mathematical model to capture a dynamic uncertain situation. TINs provide a concise graph-theoretic probabilistic approach to specify the cause and effect relationships that exist among the variables of interest (actions, desired effects, and other uncertain events) in a problem domain. The purpose of building these TIN models is to identify and analyze several alternative courses of action. The current practice is to use trial and error based techniques which are not only labor intensive but also produce sub-optimal results and are not capable of modeling constraints among actionable events. The EA based approach presented in this paper is aimed to overcome these limitations. The approach generates multiple COAs that are close enough in terms of achieving the desired effect. The purpose of generating multiple COAs is to give several alternatives to a decision maker. Moreover, the alternate COAs could be generalized based on the relationships that exist among the actions and their execution timings. The approach also allows a system analyst to capture certain types of constraints among actionable events.

The behaviour of a complex adaptive system (CAS) cannot be predicted from the behaviour of its constituent components. Individual components of the system interacts with each other such that the behaviour at the aggregate level is not predictable from knowledge about the components. Software agents based on the 'Belief-Desire-Intention' (BDI) paradigm are used to model the various roles and actors in a military complex adaptive system. Each agent can sense some aspects of its environment, interprets its sensory perceptions, and reacts in a manner consistent with its intended task or goal. The design of the system entails setting down the internal rules for each agent, as well as the rules of interaction between the agents. During the simulation run, the agents are allowed to interact according to their programmed rule sets, and the emergent behaviour of the system as a whole is observed. The application of complex adaptive system theory is used to model the interaction between elements of a military command and control system and information operations/warfare core areas. The purpose with the investigation is to investigate the optimal integration of activities between the various information operations core areas.

To be able to predict the IR signature of an aircraft, the heat radiated, in the form of IR radiation, has to be calculated. A program package, SIGGE, has been developed at FOI for this purpose. In this paper, the code will be presented together with some results from verification, validation and one application.

The laser is an integrated part of many weapon systems, such as laser guided bombs, laser guided missiles and laser beam-riding missiles. These systems pose a significant threat to military assets on the modern battlefield. The lasers used in beam-riding missiles are particularly hard to detect as they typically use relatively low power lasers. Beamriders are also particularly difficult to defeat as current countermeasure systems have not been optimized against this threat. Some recent field trails conducted in the United Arab Emirates desert have demonstrated poor performance of both laser beam-riding systems and the LWRs designed to detect them. The aim of this research is to build a complete evaluation tool capable of assessing all the phases of an engagement of a main battle tank or armoured fighting vehicle with a laser based guided weapon. To this end a software model has been produced using Matlab & Simulink. This complete model has been verified using lab based experimentation and by comparison to the result of the mentioned field trials. This project will enable both the evaluation and design of any generic laser warning receiver or missile seeker and specific systems if various parameters are known. Moreover, this model will be used as a guide to the development of reliable countermeasures for laser beam-riding missiles.

The objective of this work is to explore the consequences of using present-time-only data for infrared signature predictions on tracked vehicles, and to quantify the error that is introduced by this steady state analysis approach. The paper will focus on the effects of three independent variables; spectral reflectance, thermal capacitance and the time of day that the simulation takes place. The error analysis will be deconstructed to the basic elements of sensor prediction; thermal and radiance analysis. The study was conducted parametrically in order to capture a range of applicability for tracked vehicles. The outcome of the study is a set of guidelines, outlining the period of time necessary to rid a model of equilibrium error.

The Advanced Multispectral Simulation Test Acceptance Resource (AMSTAR) is a suite of state-of-the-art hardware-in-the-loop (HWIL) simulation / test capabilities designed to meet the life-cycle testing needs of multi-spectral systems. This paper presents the major AMSTAR facility design concepts and each of the Millimeter Wave (MMW), Infrared (IR), and Semi-Active Laser (SAL) in-band scene generation and projection system designs. The emergence of Multispectral sensors in missile systems necessitates capabilities such as AMSTAR to simultaneous project MMW, IR, and SAL wave bands into a common sensor aperture.

Model-Based Design streamlines system development by enabling engineers to model systems, perform fast simulations, manage timing behavior, migrate designs from floating-point to fixed-point data types, automatically generate code, and perform testing and verification. In Model-Based Design, a system model is at the center of the development process- from requirements capture and design to implementation and test. Through integrated simulation, the model becomes an executable specification that allows design trade-offs to be quickly explored before investing in costly hardware implementations. For example, in a video tracking system, is simple background subtraction sufficient or would optical flow be more robust? Which of the optical flow algorithms provides enough precision without exceeding processor performance headroom? Once the floating-point or golden reference model is complete, Model-Based Design eases the migration to fixed-point data types with built-in tools to control finite word effects. Tools for automatic code generation ease the implementation of embedded systems, saving time and avoiding the introduction of hand-coding errors. Model- Based Design enables test and verification to occur continuously throughout the development cycle. When errors and problems are discovered early, it is less expensive to fix them and it is less likely that they will represent a significant delay to a development schedule.

This research is part of a proposed shift in emphasis in decision support from optimality to robustness. Computer simulation is emerging as a useful tool in planning courses of action (COAs). Simulations require domain models, but there is an inevitable gap between models and reality - some aspects of reality are not represented at all, and what is represented may contain errors. As models are aggregated from multiple sources, the decision maker is further insulated from even an awareness of model weaknesses. To realize the full power of computer simluations to support decision making, decision support systems should support the planner in exporing the robustness of COAs in the face of potential weaknesses in simulation models. This paper demonstrates a method of exploring the robustness of a COA with respect to specific model assumptions about whose accuracy the decision maker might have concerns. The domain is that of peacekeeping in a country where three differenct demographic groups co-exist in tension. An external peacekeeping force strives to achieve stability, an improved economy, and a higher degree of democracy in the country. A proposed COA for such a force is simluated multiple times while varying the assumptions. A visual data analysis tool is used to explore COA robustness. The aim is to help the decision maker choose a COA that is likely to be successful even in the face of potential errors in the assumptions in the models.

Future battlespaces will contain large numbers of varied sensors deployed on the ground, in the air, and in space. Military commanders will make more effective decisions if sensor data is fused to provide a cohesive picture of their battlespace environment. The Air Force Research Laboratory Information Directorate (AFRL/IF) has developed a testbed within which to integrate, evaluate, and demonstrate fusion and information technologies to support and facilitate the sharing and exploitation of data from a variety of sensors. The Fusion Testbed is used to support analytical studies, on-site and network distributed simulation exercises, and the processing of real-world, multiple source intelligence (multi-INT) data. Varied scenario simulation tools, platform and sensor models (including JSTARS, U2, and Global Hawk), data simulators for GMTI, ELINT and MASINT along with operational systems (including MTIX and KAST), and highly developed multi-INT data fusion systems are available for application to the problem of ground target identification and tracking against a variety of operational scenarios. Scenario animations display simulation environment activities and unique automated analytical tools quantify established Measures of Performance (MOPs). In total, the Fusion Testbed facilitates a broad range of command, control, intelligence, surveillance, reconnaissance (C2ISR), and fusion technology developments. This paper describes the AFRL Fusion Testbed component capabilities and operationally-focused applications.

The military planning process utilizes simulation to determine the appropriate course of action (COA) that will achieve a campaign end state. However, due to the difficulty in developing and generating simulation level COAs, only a few COAs are simulated. This may have been appropriate for traditional conflicts but the evolution of warfare from attrition based to effects based strategies, as well as the complexities of 4^th generation warfare and asymmetric adversaries have placed additional demands on military planners and simulation. To keep pace with this dynamic, changing environment, planners must be able to perform continuous, multiple, "what-if" COA analysis. Scenario management and generation are critical elements to achieving this goal. An effects based scenario generation research project demonstrated the feasibility of automated scenario generation techniques which support multiple stove-pipe and emerging broad scope simulations. This paper will discuss a case study in which the scenario generation capability was employed to support COA simulations to identify plan effectiveness. The study demonstrated the effectiveness of using multiple simulation runs to evaluate the effectiveness of alternate COAs in achieving the overall campaign (metrics-based) objectives. The paper will discuss how scenario generation technology can be employed to allow military commanders and mission planning staff to understand the impact of command decisions on the battlespace of tomorrow.