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
Amount:
$149,996.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
HQ0147-13-C-7306
Award Id:
n/a
Agency Tracking Number:
B122-005-0073
Solicitation Year:
2012
Solicitation Topic Code:
MDA12-005
Solicitation Number:
2012.2
Small Business Information
Beeches Technical Campus, 7902 Turin Road, Ste. 2-1, Rome, NY, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
883336190
Principal Investigator:
Richard Loe
Senior Research Scientist
(315) 334-1163
rloe@androcs.com
Business Contact:
Thomas Benjamin
Director of Contracts
(315) 334-1163
tbenjamin@androcs.com
Research Institute:
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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