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Clutter Removal and Substantially Improved Submarine and Mine Detection Through Affordable

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-07-C-0096
Agency Tracking Number: N054-025-0216
Amount: $999,998.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N05-T025
Solicitation Number: N/A
Solicitation Year: 2005
Award Year: 2007
Award Start Date (Proposal Award Date): 2006-11-02
Award End Date (Contract End Date): 2007-11-02
Small Business Information
1081 Camino del Rio South, Suite 209
San Diego, CA 92108
United States
DUNS: 031243913
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Robert Jannarone
 President and Founder
 (619) 299-5139
Business Contact
 John Tyler Tatum
Title: CEO
Phone: (404) 783-0923
Research Institution
 Kurt Brintzenhofe
11100 Johns Hopkins Road
Laurel, MD 20723 6099
United States

 (240) 228-6160
 Nonprofit College or University

This proposal offers a System for improving decisive inferences based on Air Deployable Active Receive (ADAR) sonar, in a form that is suitable for deployment on ADAR sonobuoys. Anticipated near-term improvements include better target recognition and reduced operator fatigue, brought about through improved ping data filtering on ADAR aircraft. Anticipated long-term improvements include increased sonobuoy persistence and decreased telemetry cost, delivered through sonobuoy-based target detection and telemetry control. Sonar detection of submarines, mines and other threats has continued to improve in both sensitivity of the sensors and reduction in the complexity and cost of sensor devices. Sensors alone will not produce optimal improvements. In addition, solutions capable of quickly and cost-effectively highlighting threats within continuously changing clutter backgrounds will be required. Brainlike Surveillance Research, Inc, offers a novel estimation system that adapts sensor data for improved target identification -- automatically, efficiently, and adaptively. Output from the system shows anomalies clearly, removes background clutter effectively, adapts to changing conditions automatically, and improves results from complementary classifiers substantially. The proposed system will run a novel, efficient kernel process that is deployable on remote sensor arrays.

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

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