Paul G. Gonsalves

Situation assessment via Bayesian belief networks

We present here an approach to battlefield situation assessment based on a level 2 fusion processing of incoming information via probabilistic Bayesian Belief Network technology. A belief network (BN) can be thought of as a graphical program script representing causal relationships among various battlefield concepts represented as nodes to which observed significant events are posted as evidence. In our approach, each BN can be constructed in real-time from a library of smaller component-like BNs to assess a specific high-level situation or address mission-specific high-level intelligence requirements. Furthermore, by distributing components of a BN across a set of networked computers, we enhance inferencing efficiency and allow computation at various levels of abstraction suitable for military hierarchical organizations. We demonstrate them effectiveness of our approach by modeling the situation assessment tasks in the context of a battlefield scenario and implementing on our in-house software engine BNet 2000.

Fuzzy logic gain scheduling for flight control

Demonstrates the potential capabilities of fuzzy logic to augment the design of flight control systems for high performance aircraft. Specifically, the authors detail the procedure for developing a fuzzy logic gain scheduler for use over the full flight envelope. The fuzzy logic approach has been implemented and tested using a full-envelope nonlinear F/A-18 simulation. Comparisons are made with a conventional approach that uses numerical interpolation. Results demonstrate the feasibility and flexibility of the approach to provide adequate control performance across the flight envelope, outside the flight envelope, and in the presence of noisy air data measurements.<<ETX>>

Intelligent threat assessment processor (ITAP) using genetic algorithms and fuzzy logic

The explosive growth in the area of information technology provides a tremendous opportunity for enhancing military warfighting capabilities. The management and processing of military intelligence information, the requisite assessment of enemy capabilities, intent, and objectives, and the generation of appropriate response recommendations form a critical element of battlespace operations. We develop an Intelligent Threat Assessment Processor (ITAP) for enhancing tactical threat assessment. Our novel system integrates a genetic algorithm approach to predicting enemy courses of action (eCOAs), a fuzzy logic based analysis of predicted eCOAs to infer enemy intent and objectives, and in conjunction with our on-going development of an Intelligent Fusion and Asset Management Processor (IFAMP), provides the necessary functionality to support multi-level data fusion. We see considerable potential for this approach in enhancing existing tactical decision aiding systems and addressing future information dominated battlespace requirements.

Sensor scheduling using ant colony optimization

The basic problem of collection management is to schedule a group of sensor assets over a series of mission objectives in a way that minimizes resource usage and maximizes the likelihood that all the mission objectives will be completed. We present an approach to collection management, specifically sensor scheduling, that relies on Ant Colony Optimization (ACO), a biologically-inspired search algorithm. This approach offers agent-based modeling of the search resources and environment to ensure realism. We extend the traditional ACO algorithm, which relies on a single agent for search, to accommodate coordinated multi-sensor search teams made up of heterogeneous sensor assets.

Intelligent Fusion and Asset Management Processor

Owing to continual advances in sensor capabilities, avionics, and inter-service C/sup 4/I, the volume of battlefield intelligence data to which the modern-day military intelligence analyst has access is increasing at an exponential rate. This has created the need for more intelligent systems capable of scanning and extracting the tactically most useful information for presentation to the human analyst. The presence of a more extensive and flexible sensor asset infrastructure also mandates more intelligent and accountable asset deployment and management. Accordingly, we describe an effort to develop for the Air Force Research Laboratorys Information Directorate an Intelligent Fusion and Asset Management Processor (IFAMP) for enhancing tactical situation awareness and for providing needs-based sensor asset planning and scheduling information to assist the C/sup 2/ staff. The IFAMP architecture incorporates three distinct modules: a fuzzy logic-based level one fusion module responsible for low-level event detection, unit/echelon type discrimination, observation-to-track gating and assignment, and track database management; a belief network-based level two situation assessment module responsible for generating probabilistic hypotheses for high-level situational state descriptors; and a fuzzy logic-based level four collection management expert system responsible for mapping informational requirements and current state information into asset resource requests.

Software Toolkit for Optimizing Mission Plans (STOMP)

*† Recent military actions have demonstrated the need for addressing time-sensitive and time-critical targeting. The capabilities of precision guided munitions and the further development of strike warfare platforms and tactics portend a huge increase in effectiveness and lethality of air operations and achieving the vision of the US Air Force’s Global Strike Task Force concept. To fully realize the benefits of these strike capable assets and to address the challenges inherent in time-sensitive targeting, decision support systems are needed to assist warfighters in optimal allocation and near real-time re-deployment of air assets, and to support predictive battlespace awareness. While meeting a specific operational need, additional benefits can accrue through the employment of such decision support systems for virtual and constructive simulation based training, experimentation, and Command and Control (C2) system evaluation and acquisition. Here, we detail a Software Toolkit for Optimizing Mission Plans (STOMP). STOMP integrates a genetic algorithm-based mechanism to rapidly generate, analyze, and visualize mission plans in tandem with software interoperability to provide the requisite interface and connectivity with Air Force C2 systems and synthetic battlespace environments.

Architecture for genetic algorithm-based threat assessment

The determination and subsequent analysis of potential enemy course of actions (COAs) /oms a key element within the Intelligence Preparation of the Balllespace (IPB) process. Enemy COA prediction and analysis is also key element of threat assessment dafa fusion processing. Here weformulate an architecture that employs genetic algorithms (GAS) to generate and evaluate enemy COAs. 771;s formulation is derivedfrom experience we have garnered over several research and development efforts and across d@erent military domains. The oupul ofthis architecture is a set ofenemy COAs chat can be evaluated based on their potential effectiveness against friendly forces. These results can then be disfributed to higher-jdelip simulafions for detailed evaluation and/or lo decision support systems for friendlyforce mission planning.

Increasing agent autonomy in dynamic environments

1. ABSTRACT In this paper, we present an event based agent architecture for increased agent autonomy in dynamic environments. Our approach is based on an event description language called MDL that has been developed to facilitate application modeling by associating events with concepts such as objects and attributes. Events can model a wide variety of real-world scenarios ranging from database transactions to user interactions. Our architecture employs a novel learning service for the integration of event sequences to produce facts about the environment for increased agent autonomy. This generic architccturc has been successfully instantiated in a number of research and commercial application domains of which the following two arc described in this paper: 1) A spacecraft data analysis agent that identifies recurring patterns within spacecraft telemetry data to characterize normal spacecraft operations, 2) A Macintosh desktop interface agent that finds repetitive user patterns by observing user actions in the background and automating upon approval. 1.1

AUTOMATED DATA FUSION AND SITUATION ASSESSMENT IN SPACE SYSTEMS

Space systems are an important part of everyday life. They provide global positioning data, communications, and Earth science data such as weather information. All space systems require satellite operators to ensure high performance and continuous operations in the presence of off-nominal conditions due to space weather and onboard anomalies. Similar to other high-stress, time critical operations (e.g., piloting an aircraft or operating a nuclear power plant), situation awareness is a crucial factor in operator performance during these conditions. Because situation awareness is largely acquired by monitoring large numbers of parameters, it is difficult to rapidly and accurately fuse the data to develop an accurate assessment. To aid operators in this task, we have developed a prototype Multi-Agent Satellite System for Information Fusion (MASSIF) for automated data fusion and situation awareness. This system is based on human cognitive decision-making models and integrates a fuzzy logic system for semantic data processing, Bayesian belief networks for multi-source data fusion and situation assessment, and rule-bases for automatic network construction. This paper describes initial simulation-based results to establish feasibility and baseline performance. We describe knowledge engineering efforts, belief network construction, and operator-interfaces for automated data fusion and situation awareness for a hypothetical geosynchronous satellite.

Multi-objective optimization to support rapid air operations mission planning

Within the context of military air operations, Time-sensitive targets (TSTs) are targets where modifiers such, “emerging, perishable, high-payoff, short dwell, or highly mobile” can be used. Time-critical targets (TCTs) further the criticality of TSTs with respect to achievement of mission objectives and a limited window of opportunity for attack. The importance of TST/TCTs within military air operations has been met with a significant investment in advanced technologies and platforms to meet these challenges. Developments in ISR systems, manned and unmanned air platforms, precision guided munitions, and network-centric warfare have made significant strides for ensuring timely prosecution of TSTs/TCTs. However, additional investments are needed to further decrease the targeting decision cycle. Given the operational needs for decision support systems to enable time-sensitive/time-critical targeting, we present a tool for the rapid generation and analysis of mission plan solutions to address TSTs/TCTs. Our system employs a genetic algorithm-based multi-objective optimization scheme that is well suited to the rapid generation of approximate solutions in a dynamic environment. Genetic Algorithms (GAs) allow for the effective exploration of the search space for potentially novel solutions, while addressing the multiple conflicting objectives that characterize the prosecution of TSTs/TCTs (e.g. probability of target destruction, time to accomplish task, level of disruption to other mission priorities, level of risk to friendly assets, etc.).