Efficient Clutter Suppression and Nonlinear Filtering Techniques for Tracking Dim Closely Spaced Objects in the Presence of Debris

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7415
Agency Tracking Number: B12B-004-0040
Amount: $100,000.00
Phase: Phase I
Program: STTR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: MDA12-T004
Solicitation Number: 2012.B
Small Business Information
6800 Cortona Drive, Goleta, CA, -
DUNS: 054672662
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Tim Fair
 (703) 674-0612
Business Contact
 Marcella Lindbery
Title: Director of Contracts
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
 University of Southern California
 Alexander Tartakovsky
 Information Sciences Institute
4676 Admiralty Way
Marina del Rey, CA, 90292-
 (213) 740-2450
 Nonprofit college or university
EO/IR elements of the Ballistic Missile Defense System (BMDS) responsible for detecting and tracking ballistic missile threats encounter extraordinarily challenging threat and scene phenomenology. Specifically, non-stationary clutter characteristic of airborne and satellite-based sensor systems, along with dim target signatures, closely-spaced objects, and dense debris clouds typical of ballistic threats in midcourse flight, present complications for accurately detecting, tracking, and engaging ballistic threats across the BMDS. Current Detect-then-Track algorithms are extremely vulnerable to high false alarm rates under these circumstances. At a system level, the problem is much more catastrophic; detections from multiple sensors overwhelm the system making multisensor integration difficult. Due to range deficiencies of EO/IR sensor technology, multisensor integration is vital for successful intercept of ballistic threats. To address these challenges, we propose a framework that leverages spatiotemporal image processing algorithms for non-stationary clutter estimation and rejection, and nonlinear filtering based Track-before-Detect algorithms for tracking dim targets. Our approach fuses information across sensors without loss of information due to detection thresholds. Our algorithms, when applied jointly, provide a near-optimal solution. In addition, our algorithms are capable of resolving dim closely-spaced objects and robustly handle nonlinearities from: threat trajectories, closely-spaced object/debris phenomenology, and 3D-to-2D projective nonlinearities typical of optical sensors.

* Information listed above is at the time of submission. *

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