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Eliminating ATR False Alarms in Complex Underwater Environments with Continuous Learning

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0649
Agency Tracking Number: N231-035-1521
Amount: $44,801.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N231-035
Solicitation Number: 23.1
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-08-07
Award End Date (Contract End Date): 2024-02-05
Small Business Information
1202 Eastern Ave
Charlton, NY 12019-2920
United States
DUNS: 117436677
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Christopher Law
 (518) 429-0081
Business Contact
 Christopher Law
Phone: (518) 429-0081
Research Institution

AI technologies, such as deep neural networks (DNN), have entered a rapid ascent phase during which they will make significant contributions to the economy and society. It is critical that machine learning is exploited fully for national defense. The easiest deployment of DNNs for the Navy is evaluating growing data sources for intelligence and actionable information. One challenge with this application is that DNNs hallucinate and generate false-positive detections. Even at 99% accuracy, false positives can greatly outnumber true positives if targets occur infrequently. This is best captured by the “mean average precision” (mAP) performance metric. We propose to eliminate false positives with several algorithms that collaborate to boost the mAP of the combined ATR classifier.

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

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