Techniques for Automatically Exploiting Passive Acoustic Sonar Data-CPP

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$1,499,970.00
Award Year:
2010
Program:
SBIR
Phase:
Phase II
Contract:
N00024-10-C-4152
Award Id:
n/a
Agency Tracking Number:
N062-138-0499a
Solicitation Year:
2006
Solicitation Topic Code:
N06-138
Solicitation Number:
2006.1
Small Business Information
13135 Lee Jackson Hwy, Suite 220, Fairfax, VA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
140785929
Principal Investigator:
Alex Rabinowitch
Senior Lead Engineer
(703) 956-6480
alex.rabinowitch@3phoenix.com
Business Contact:
Linda Leonard
Contracts Manager
(703) 956-6480
linda.leonard@3phoenix.net
Research Institute:
Stub




Abstract
The Navy reduced manning requirements drive the need for more sonar automation. Automatically detecting and classifying targets of interest meets the challenge of reduced manning and workloads required for passive anti-submarine warfare (ASW) and torpedo detection, classification and localization (TDCL). The signal processing technology is comprised of a system architecture and a suite of algorithms which have the potential to provide a robust, automatic detection, classification and localization system for torpedoes. The Phase II effort is focused on finalizing and prototyping the automated data exploitation architecture based on the design concepts developed in Phase I. Both the SQQ-89(V)15 Combat System and Torpedo Warning System (TWS) programs require TDCL capability to detect, classify, and localize threat torpedoes with sufficient range and accuracy to support effective countermeasure engagement. Both systems require high probability of target classification with very low false alert rate. Testing to date has indicated a specific need to reduce automated false alert rate while maintaining current probability of correct classification. The tasks included in this Phase II address the false alert rate issue in several areas: improved spectral classification, improved target state estimation to support spatial and kinematic classification, and improved multi-target tracking.

* information listed above is at the time of submission.

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