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Sensor Data Fusion for Target Classification and Identification

Award Information

Agency:
Department of Defense
Branch:
Army
Award ID:
52902
Program Year/Program:
2001 / SBIR
Agency Tracking Number:
A002-1399
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Scientific Systems Company, Inc
500 West Cummings Park - Ste 3000 Woburn, MA 01801-6562
View profile »
Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2001
Title: Sensor Data Fusion for Target Classification and Identification
Agency / Branch: DOD / ARMY
Contract: DAAH01-01-C-R110
Award Amount: $120,000.00
 

Abstract:

The effectiveness of Non-Cooperative Target Recognition(NCTR) against air targets is limited by a lack of robustness in thefollowing respects: insufficient-fidelity signature simulation;statistically uncharacterizable signature variations; excessivelylargepose-model libraries; excessively large target-type libraries;misclassification of ``novel'' targets; insufficient target-identityresolvability using single sensors; and difficulties in fusing diversesources/sensors. Many of these difficulties arisefrom the fact thatconventional OPTIMAL techniques (e.g. Bayesian filtering andestimation) expect PERFECT models and, consequently, can behavevery non-optimally if the mismatch between model and reality is too great.Consequently, optimal techniques cannotbe applied as a ``cookbook''panacea---they must be augmented by ROBUSTNESS STRATEGIES thatcompensate for model-mismatch and other problems. Scientific SystemsCompany, Inc. (SSCI) and its subcontractor Lockheed Martin of Eagan MN(LM-E) propose theapplication of new ROBUST DATA FUSION ANDROBUST-BAYES FILTERING techniques to NCTR problems. In our approach we (1)use generalized likelihood functions to model the uncertainties as well asthe certainties in data; (2) fuse very disparate kinds of datausinggeneralized joint likelihood functions; (3) deal with ``novel'' targets byintroducing and modeling an ``unknown target'' type; (4) reduce the size ofthe pose-search library via fusion of kinematic data with target ID data toestimate pose; (5) ensureaccurate, stable NCTR by using a true JOINTstate-estimator; and (6) ensure computational efficiency and robustness byusing an approximate filtering scheme with efficiency O(n) or O(nlog n)and theoretically guaranteed divergence properties.Targetidentification is one of the key technologies for globalsurveillance, precision strike, air superiority and defense which arethree of the seven science and technology thrust areas identified bythe Director of Defense Research and Engineering.Commercialapplications of advanced tracking and identification systems exist inseveral areas such as: radar, biometric identification, industrialinspection, medical screening and diagnosis, failure detection andidentification, and remote sensing.

Principal Investigator:

Adel El-Fallah
Research Engineer
7819335355
adel@ssci.com

Business Contact:

Raman Mehra
President
7819335355
rkm@ssci.com
Small Business Information at Submission:

SCIENTIFIC SYSTEMS CO., INC.
500 West Cummings Park Woburn, MA 01801

EIN/Tax ID: 043053085
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No