Contextually Adaptive False Alarm Mitigation

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-08-C-0122
Agency Tracking Number: 07SB2-0310
Amount: $99,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2007
Solicitation Year: 2007
Solicitation Topic Code: SB072-017
Solicitation Number: 2007.2
Small Business Information
SCIENTIFIC SYSTEMS CO., INC.
500 West Cummings Park - Ste 3000, Woburn, MA, 01801
DUNS: 859244204
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Tony Falcone
 Group Leader - Image Exploitation
 (781) 933-5355
 afalcone@ssci.com
Business Contact
 Jay Miselis
Title: Corporate Controller
Phone: (781) 933-5355
Email: jmiselis@ssci.com
Research Institution
N/A
Abstract
In aerial images collected by wide-area sensors, targets of interest such as vehicles and dismounts are extremely small compared to the footprint of the image. The limited information that can be extracted from few pixels on target often means the targets of interest can be easily confused with other objects. Commonly used target detection systems in Intelligence, Surveillance and Reconnaissance (ISR) platforms can provide high detection rates but at the cost of high false alarm rates. Fortunately, potential objects of interest need not be viewed in isolation. Rather, when they are considered in the context of other information, many of the false alarms can be mitigated. In this effort, we propose a Machine Learning based approach to Contextual Rule Discovery for False Alarm Mitigation. A Learning Classifier System will be used to discover contextual rules of the form IF THEN , which can then be used to determine if a potential target flagged by an unreliable low level detector is indeed a true target. The Learning Classifier System must be trained to derive these rules, but we propose a method to conduct on-line automated training of the LCS without any human-in-the-loop. Contextual information is modeled through the automated generation of background class information. The system will be adaptive, in that background classes and contextual rules can be updated to deal with changes in environment during a mission. Scientific Systems Company, Inc. will be joined in this effort by Mercury Computer Systems and Dr. Robert Smith.

* information listed above is at the time of submission.

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