Automated Target Characterization and Correlation with Heterogeneous Kinematic and Feature Data for Sensor Hand-Over

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7306
Agency Tracking Number: B122-005-0073
Amount: $149,996.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: MDA12-005
Solicitation Number: 2012.2
Small Business Information
ANDRO Computational Solutions, LLC
Beeches Technical Campus, 7902 Turin Road, Ste. 2-1, Rome, NY, -
DUNS: 883336190
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Richard Loe
 Senior Research Scientist
 (315) 334-1163
 rloe@androcs.com
Business Contact
 Thomas Benjamin
Title: Director of Contracts
Phone: (315) 334-1163
Email: tbenjamin@androcs.com
Research Institution
 Stub
Abstract
We propose to develop and implement a set of algorithms within a unified target characterization and correlation framework capable of operating in a multiple heterogeneous sensor environment where detection, classification, localization and track priority information is exchanged among multiple platforms. Our goal is to deliver an effective automated and autonomous information extraction and fusion system that can be incorporated in today"s operational systems. The primary focus will be on the development of algorithms for target characterization and correlation that can handle the difficult track handover between PTSS EO/IR boost phase detection sensors and weapon control sensors such as Aegis or AN/TPY-2. Information fusion with heterogeneous sensors is challenging because non-kinematic features are different for each sensor type making it is difficult to correlate features across sensors. Thus, it is necessary to develop meta-features that are sensor invariant and amenable to optimal track correlation across sensors. Once this target characterization is carried out effectively, the next task is to develop efficient fusion or correlation algorithms that can yield better common tracks and facilitate accurate track hand-over. In our approach, target characterization (information extraction) and correlation (information fusion) are tightly coupled problems that are addressed jointly to ensure optimal overall performance.

* information listed above is at the time of submission.

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