Innovative Algorithms for the Categorization of Mine-Like Objects Using Standard Sonar Return Data.

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$79,965.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
N00024-13-P-4585
Award Id:
n/a
Agency Tracking Number:
N131-037-0569
Solicitation Year:
2013
Solicitation Topic Code:
N131-037
Solicitation Number:
2013.1
Small Business Information
P.O. Box 2607, Winnetka, CA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
082191198
Principal Investigator:
Jason Seely
Senior Scientist
(303) 651-6756
jseely@arete.com
Business Contact:
Dave Kane
EVP
(520) 770-6099
contracts2@arete.com
Research Institution:
Stub




Abstract
Mine countermeasures will be a critical mission of the Littoral Combat Ship. This capability will be fulfilled by existing and future advanced SONAR systems to detect surface and volume mines. The performance of these systems is degraded by the large numbers of false alarms due to surface, volume, and sea bottom clutter, which increase operator workload and reduce effective search rate. Arete Associates proposes to develop an integrated false alarm mitigation strategy to improve the detection performance of existing military and COTS SONAR systems. Our approach builds upon over 30 years exploiting powerful data analytic tools and techniques for detection of weak targets in highly cluttered environments. Arete's in-house automated tools will be used to identify optimally discriminating spatial and spectral feature sets and feature-based classifiers to improve the performance of individual sensors. Additionally, Arete-developed weak-target tracking techniques will be used to combine temporal data from multiple scans, or multiple sensors, to improve weak target detection and eliminate short-lived false alarms. The optimal combination of spatial, spectral and temporal information will provide a powerful and robust object classification algorithm that will probability of detection and reduce false alarms in SONAR MCM applications.

* information listed above is at the time of submission.

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