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Proceedings Paper

Automated anomaly detection processor
Author(s): James B. Kraiman; Scott L. Arouh; Michael L. Webb
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Paper Abstract

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, multi-sensor 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.

Paper Details

Date Published: 15 July 2002
PDF: 10 pages
Proc. SPIE 4716, Enabling Technologies for Simulation Science VI, (15 July 2002); doi: 10.1117/12.474940
Show Author Affiliations
James B. Kraiman, Dynamics Technology, Inc. (United States)
Scott L. Arouh, Dynamics Technology, Inc. (United States)
Michael L. Webb, Dynamics Technology, Inc. (United States)

Published in SPIE Proceedings Vol. 4716:
Enabling Technologies for Simulation Science VI
Alex F. Sisti; Dawn A. Trevisani, Editor(s)

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