Machine Learning for Robust Automatic Target Recognition

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-04-M-1660
Agency Tracking Number: F041-230-2048
Amount: $99,980.00
Phase: Phase I
Program: SBIR
Awards Year: 2004
Solicitation Year: 2004
Solicitation Topic Code: AF04-230
Solicitation Number: 2004.1
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, 01801
DUNS: 859244204
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 B. Ravichandran
 Manager, R & D
 (781) 933-5355
Business Contact
 Raman Mehra
Title: President
Phone: (781) 933-5355
Research Institution
The primary issue faced by ATR systems center around the large "variability of the ATR problem space spanned by the targets, sensors, and the environment and the associated challenge is to develop "robust" approaches to improve ATR system performance. Addressing this challenge, the objectives of this project (Phases I and II) are an investigation and demonstration of prototype algorithms to improve ATR robustness. A common theme underlying robust decision making algorithms is learning from real data which can be accomplished by propagation, interaction, and switching amongst multiple models leading to composite or generalized global models that provide robust and reliable estimates and ATR decisions. We will develop both statistical learning and machine learning based approaches for robust ATR This project will also involve a series of demonstrations, where generalization is conducted over EOC conditions at both the raw data (pixel/intensity) level, and the feature level. Testing at these various levels will account for EOC factors, including clutter and noise variations, minor design differences, pose, revetment, partial obscurations, and articulation of movable parts, etc. The project team consists of Scientific Systems Company, Inc. (SSCI), Woburn MA, and its subcontractor Lockheed Martin Tactical Systems (LMTS), Eagan MN.

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

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