Anomaly Detection in Ground Moving Target Indicating (GMTI) Radar

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,999.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
W15P7T-06-C-L203
Award Id:
73937
Agency Tracking Number:
A052-081-0616
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1900 S. Sepulveda Blvd, Suite 300, Los Angeles, CA, 90025
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
053885604
Principal Investigator:
Allan Corbeil
Corporate Senior Staff
(203) 268-1249
acorbeil@tsc.com
Business Contact:
Lee Moyer
VP Operations
(703) 383-3150
lee.moyer@tsc.com
Research Institution:
n/a
Abstract
UAV-based GMTI radar offers the capability to detect anomalous vehicle and personnel activity in real-time to cue narrow field of view EO/IR sensors for identifying enemy targets in the presence of normal vehicle and foot traffic. Data mining is the process of extracting useful information from large complex data sets, and is ideal for application to GMTI. TSC proposes to significantly enhance our existing GMTI Data Mining techniques, which have been successfully applied to detect convoys in dense background traffic in a classified Joint STARS GMTI database from Operation Iraqi Freedom. In Phase 1, TSC will develop innovative temporal, spatial and hybrid data mining techniques to analyze traffic patterns and detect suspicious vehicle behavior in challenging UAV surveillance scenarios of interest to the Army. We will employ extensive in-house target and background models and GMTI simulation tools to evaluate the feasibility of these algorithms on simulated data that is representative of near-term UAV systems. In Phase 2, TSC will refine the most promising techniques and develop a software architecture that will be utilized to fully test the algorithms on both simulated and collected GMTI data. TSC will also investigate methods to detect anomalous activities of personnel using GMTI measurements.

* information listed above is at the time of submission.

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