Composit Tracking and Discrimination Module

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,979.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
W9113M-09-C-0178
Agency Tracking Number:
A091-012-0081
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
DANIEL H. WAGNE
40 Lloyd Avenue, Suite 200, Malvern, PA, 19355
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
075485425
Principal Investigator:
C.A. Butler/Dr. B. Belkin, Co-PIs
President
(757) 727-7700
Allen.Butler@va.wagner.com
Business Contact:
W. Reynolds Monach
Vice President
(757) 727-7700
GovtMktg@pa.wagner.com
Research Institution:
n/a
Abstract
The project objective is to develop a set of mathematically rigorous Composite Tracking and Discrimination Modules (CTDMs) for accurately fusing both kinematic and non-kinematic sensor information to contribute to a consistent Single Integrated Air Picture (SIAP) containing both Tactical Ballistic Missiles (TBMs) and Air Breathing Targets (ABTs). A distributed data fusion architecture is assumed. Local (sensor level) tracks are formed based on measurement-to-track fusion. Multi-sensor system tracks are formed based on track-to-track fusion. The target state vector includes both kinematic and feature states (such as radar cross section and color temperature). Non-linear state estimation methods employed include extended Kalman filtering, a Gaussian sum representation for the target state distribution, and the modified Euler method for approximating the solution to the target state SDE. Data association is formulated as a classical assignment problem. Data association hypotheses are generated using the Munkres algorithm. A graph-theoretic algorithm is used to form cluster tracks partitioning the data association problem into independent subproblems. The bandwidth required to communicate tracking data across the distributed network is reduced by sending pseudo-measurements that capture the information from multiple physical measurements. A Bayesian inference engine performs the target discrimination and classification function. Multi-sensor registration is performed using non-Gaussian methods.

* information listed above is at the time of submission.

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