Multi-Sensor Tracking and Fusion for Space Radar Application

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,962.00
Award Year:
2008
Program:
SBIR
Phase:
Phase I
Contract:
FA8750-08-C-0181
Award Id:
86864
Agency Tracking Number:
F081-029-0704
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
162 Genesee Street, Utica, NY, 13502
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
111305843
Principal Investigator:
Mark Kozak
Principal Investigator
(315) 732-7385
kozak@brsc.com
Business Contact:
Milissa Benincasa
Vice President
(315) 732-7385
benincasa@brsc.com
Research Institution:
n/a
Abstract
Black River proposes a solution for Space Radar Multi-Sensor Tracking and Fusion that considers the specific advantages and challenges offered by LEO and MEO constellations. Our approach to this research topic is three-fold in that we will characterize the expected yield of the radar modes given various scenarios, develop a tracking and fusion methodology, and develop a closed loop end-to-end simulation architecture for performance evaluation. The multi-sensor/mode, multi-target tracking and fusion problem is addressed with a fully featured state-vector that consists of kinematic and pose estimates as well as feature attributes derived from HRR, SAR, and ISAR products. The fully featured state-vector is ideally a blueprint of the target, but in reality it is a sub-sampled vector with more dimensionality than kinematics alone. The advantage of the fully featured state-vector is that it can be used in the track-to-measurement assignment process or in a track-to-track fusion process. Another key component is an architecture that includes a sensor resource manager that is necessary to schedule the specific radar modes while optimizing collections and radar energy over multiple areas of interest.

* information listed above is at the time of submission.

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