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Advanced Sensor Data Fusion

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: W9113M-07-C-0233
Agency Tracking Number: B074-004-0081
Amount: $99,972.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA07-T004
Solicitation Number: N/A
Timeline
Solicitation Year: 2007
Award Year: 2007
Award Start Date (Proposal Award Date): 2007-09-27
Award End Date (Contract End Date): 2008-03-26
Small Business Information
75 Aero Camino, Suite A
Goleta, CA 93117
United States
DUNS: 153927827
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Gary Key
 Principal Investigator
 (321) 277-8396
 garykey@aol.com
Business Contact
 Iva Heins
Title: Contracts Manager
Phone: (805) 685-6672
Email: iheins@fti-net.com
Research Institution
 UNIV. OF FLORIDA
 Roslyn Oleson
 
Office of Engineering Research POB 116550, 339 Weil Hall
Gainesville, FL 32611
United States

 (352) 392-9447
 Nonprofit College or University
Abstract

Frontier Technology, Inc. (FTI) and its research partner, University of Florida (UF), propose to develop designs for innovative discrimination algorithms for fusion of sensor (feature) and contextual information, to provide enhanced acquisition, tracking, and discrimination of threat objects in a cluttered multi-target environment. We propose to analyze the performance of the envisioned technology to support: (a) Dynamic acquisition of target state data (e.g., motion, spectral, spatial cues) from sensor output, (b) Application of multiple classifiers to target/background radar or EO/IR and target state data to identify probable target type/track/location, (c) Adaptation of classifiers to track targets given nonergodic (statistically changing) inputs, (d) Execution on small, low-power on-board processing systems Adaptive pattern selection, key to successful sensor fusion in mission- and threat-specific scenarios, will utilize FTI's TNE pattern recognition paradigm and UF's Morphological Neural Nets (MNN). Phase I will extend and analyze FTI and UF's successful, DoD-sponsored R&D for dynamic pattern recognition to develop target detection algorithms for multiple radar or EO/IR sensor data, to detect and discriminate threats from manmade or naturally-occurring clutter. Phase II will develop and test prototype image processing software to incorporate multiple sensors of differing wavebands using obscured moving and stationary targets.

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

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