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Innovative Filtering Techniques for Ground Target Tracking

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-05-C-1807
Agency Tracking Number: F041-204-1650
Amount: $749,949.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF04-204
Solicitation Number: 2004.1
Timeline
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2004-12-08
Award End Date (Contract End Date): 2006-12-08
Small Business Information
40 Lloyd Avenue, Suite 200
Malvern, PA 19355
United States
DUNS: 075485425
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Barry Belkin
 President
 (610) 644-3400
 bbelkin@pa.wagner.com
Business Contact
 John Eldridge
Title: Treasurer
Phone: (610) 644-3400
Email: GovtMktg@pa.wagner.com
Research Institution
N/A
Abstract

The project focus is the development of a ground target tracking algorithm to support the JSTARS radar operating in the GMTI mode. Multiple hypothesis tracking (MHT) algorithms maintain alternative data associations to represent report-to-track association ambiguities. In Phase 1 we developed a prototype MHT algorithm not requiring the Bayes’ posterior distribution for the target state to be Gaussian. State estimation is accomplished using the deformation method, a form of particle filtering (sequential Monte Carlo state estimation) that requires neither hypothesis pruning nor hypothesis regeneration. The deformation method transforms existing target state estimates into samples that closely approximate draws from the Bayes’ posterior distribution for the target state. Data association is treated as an assignment problem and is solved using the Munkres algorithm. The smooth deformation of the continuous target state variables is through the action of a stochastic differential equation. The discontinuous deformation of the discrete target state variables is through the application of the Metropolis algorithm. In Phase 2 we will incorporate the processing of target signature information from high range resolution (HRR) data. We also will model the effects of terrain on vehicle motion and observability. Certain computations will be parallelized to reduce the algorithm execution time.

* Information listed above is at the time of submission. *

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