USA flag logo/image

An Official Website of the United States Government

Efficient Multitarget Particle Filters for Ground Target Tracking

Award Information

Agency:
Department of Defense
Branch:
Air Force
Award ID:
67732
Program Year/Program:
2004 / SBIR
Agency Tracking Number:
F041-204-0644
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Numerica Corporation
4850 Hahns Peak Drive Suite 200 Loveland, CO 80538-
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2004
Title: Efficient Multitarget Particle Filters for Ground Target Tracking
Agency / Branch: DOD / USAF
Contract: FA8650-04-M-1621
Award Amount: $99,941.00
 

Abstract:

Many factors make the ground target tracking problem decidedly nonlinear and non-Gaussian. Some of these factors include the relatively poor angular accuracy of GMTI sensors, the presence of persistent clutter and target obscuration, and the complexity of target maneuvers. Because these difficulties can lead to a multimodal posterior density, a Bayesian filtering solution is more appropriate than a point estimate. In the last decade, the particle filter has emerged as a Bayesian inference technique that is both powerful and simple to implement. The price for this flexibility is almost entirely computational; particle filter run-times can be two orders of magnitude longer than those of Kalman filter variants. In this work, we propose to investigate the design of efficient particle filters for multitarget ground tracking. Using simulated data, we will consider multitarget scenarios involving on-road targets, off-road targets, on-road/off-road transitions, and move-stop-move cycles. Instances of uncertain data association will be produced using closely-spaced targets, stopped targets, clutter, and road intersections. An efficient design will be achieved by adaptively managing the type of particle filters, the number of particles, and the enumeration of hypotheses during data association.

Principal Investigator:

Shawn Herman
Research Scientist II
9704198343
smherman@numerica.us

Business Contact:

Jeff Poore
Vice President
9704198343
jbpoore@numerica.us
Small Business Information at Submission:

NUMERICA CORP.
PO Box 271246 Ft. Collins, CO 80527

EIN/Tax ID: 841349484
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No