Robust exploitation of tracking and surveillance data will provide an early warning and cueing capability for military and civilian Law Enforcement Agency operations. This will improve dynamic tasking of limited resources and hence operational efficiency. The challenge is to rapidly identify threat activity within a huge background of noncombatant traffic. We discuss development of an Automated Anomaly Detection Processor (AADP) that exploits multi-INT, multisensor tracking and surveillance data to rapidly identify and characterize events and/or objects of military interest, without requiring operators to specify threat behaviors or templates. The AADP has successfully detected an anomaly in traffic patterns in Los Angeles, analyzed ship track data collected during a Fleet Battle Experiment to detect simulated mine laying behavior amongst maritime noncombatants, and is currently under development for surface vessel tracking within the Coast Guard's Vessel Traffic Service to support port security, ship inspection, and harbor traffic control missions, and to monitor medical surveillance databases for early alert of a bioterrorist attack. The AADP can also be integrated into combat simulations to enhance model fidelity of multi-sensor fusion effects in military operations.

We describe our simulation of the Intelligence, Surveillance and Reconnaissance -Tasking, Processing, Exploitation & Dissemination chain. Model formulation is based on analytical descriptions of ISR-TPED processes, which allows evaluation of the statistical variability in model output within a single computational pass. Significant gains in model execution speed are achieved with this approach, especially when compared to the more commonly used technique of discrete event simulation. This allows the simulation user to rapidly identify major performance drivers in novel TPED configurations.The model evaluates the effects of integrated TPED design/management on sensor-to-shooter performance, with an emphasis on information throughput (quality and quantity) and timeliness. The M&S structure handles both discrete Air Tasking Order cycles (e.g., planning over 24 hour periods) and continuous ATO cycles with adaptive C2. Model highlights include its ability to: (1) encompass the functional elements of both current and emerging TPED architectures and systems, (2) support rapid parametric tradeoff/sensitivity studies, (3) incorporate statistical variability to identify best/worst cases scenarios and potential "choke points," and (4) complement and enhance legacy force allocation models and large discrete event simulations.

We describe our approach to model the Tasking, Processing, Exploitation, and Dissemination (TPED) process that accounts for multi-sensor fusion while characterizing and optimizing TPED architecture performance across multiple mission objectives. The method would address the inability of current models to assess the valued added by multisensor fusion techniques to ISR mission success, while providing a means to translate detailed output of sensor fusion techniques to higherlevel information that is relevant to ISR planning and analysis. The technical approach incorporates treatment of ISR sensor performance, dynamic sensor tasking and multi-sensor fusion within a probability modeling framework to allow rapid evaluation of TPED information throughput and latency. This would permit characterization/optimization of TPED architecture performance against time critical/sensitive targets (TCTs/TSTs), while simultaneously supporting other air-toground targeting missions within the Air Tasking Order cycle. TPED architecture performance metrics would include the probability of achieving operational timeliness requirements while providing requisite target identification and localization.