Algorithms for IR data

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9453-14-M-0141
Agency Tracking Number: F141-105-1848
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2014
Solicitation Year: 2014
Solicitation Topic Code: AF141-105
Solicitation Number: 2014.1
Small Business Information
6800 Cortona Drive, Goleta, CA, 93117-
DUNS: 054672662
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Christopher Agh
 Analyst
 (805) 968-6787
 cagh@toyon.com
Business Contact
 Marcella Lindbery
Title: Director of Contracts
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
N/A
Abstract
Persistent, autonomous monitoring from infrared surveillance and staring systems is necessary for early missile warning/defense, battlespace awareness, and technical intelligence. Specifically, the ability to accurately detect, track and geo-locate events of interest in known hostile regions enables the Air Force to provide countermeasures to potential threats from adversaries. Toyon Research Corporation is proposing development and feasibility demonstration of advanced real-time algorithms for exploitation of high-frame-rate overhead persistent infrared (OPIR) imagery. The algorithms being developed are for the purpose of detecting and geo-locating dim targets (due to system noise and background clutter), both moving and static, with a variety of potential signatures. The geo-location algorithms are based on the coupling of image registration to external geo-referenced satellite maps, frame-to-frame registration using feature point detection and tracking, and sensor orientation bias estimation. The algorithms for dim target detection are implemented via a nonlinear Track-before-Detect (TrbD) particle filter and are designed to work in conjunction with statistical clutter estimation and rejection algorithms to near-optimally integrate information to provide improved signal-to-noise ratio (SNR) at the detection stage. A variety of target/event signatures are modeled, and fusion of external geo-spatial information (if available) provides reduced false alarm rates over the entire field of view.

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

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