Unified Bayesian Cluster Target Tracking and Discrimination

Award Information
Agency:
Department of Defense
Branch:
Missile Defense Agency
Amount:
$0.00
Award Year:
2002
Program:
SBIR
Phase:
Phase I
Contract:
DASG60-03-C-0016
Agency Tracking Number:
99-0057
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Scientific Systems Co., Inc.
500 West Cummings Park, Suite 3000, Woburn, MA, 01801
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
859244204
Principal Investigator
 Adel El-Fallah
 Senior Research Engineer
 (781) 933-5355
 adel@ssci.com
Business Contact
 Raman Mehra
Title: President
Phone: (781) 933-5355
Email: rkm@ssci.com
Research Institution
N/A
Abstract
"Most single- and multi-target tracking algorithms are designed to track point targets.However, in many real-world applications the targets of interest are not point targets butEXTENDED TARGETS and GROUP TARGETS, e.g. missile re-entry clusters.Under this effort we propose a systematic, fully probabilistic, and theoretically rigorousapproach to cluster target tracking and discrimination. Our approach is a direct generalizationof BAYES-OPTIMAL RECURSIVE NONLINEAR FILTERING THEORY to the multisource-multitarget realm.The overall objective is to develop innovative, speculative, high-risk technologiesfor enhancing either the Theater Missile Defense (TMD) or National Missile Defense (NMD) capabilities.Phase I showed the feasibility assessment for multitarget tracking/discrimination and ``bulk'' clustertracking by utilizing a novel approximation of multi-target non-linear filtering based on the spectralcompression (SPECC) non-linear filter (NLF) implementation of Stein-Winter probability hypothesis densities (PHDs).The Phase II objectives are to further develop, analyze, and refine the PHD filtering approach by extending the resultsof Phase I. Specific Phase II tasks are: (1) Implement multipeak extraction algorithms, (2) Implement alternativeNLF implementations of PHD, (3) Extend approach to Joint Track & Non-Cooperative Target Identification (NCTI), (4) Developdiscrimination-based information about clusters to determine presence or non-pr

* information listed above is at the time of submission.

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