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Techniques for Automatically Exploiting Passive Acoustic Sonar Data-CPP

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00024-10-C-4152
Agency Tracking Number: N062-138-0499a
Amount: $1,499,970.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N06-138
Solicitation Number: 2006.2
Timeline
Solicitation Year: 2006
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-07
Award End Date (Contract End Date): 2013-03-01
Small Business Information
13135 Lee Jackson Hwy Suite 220
Fairfax, VA -
United States
DUNS: 140785929
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Alex Rabinowitch
 Senior Lead Engineer
 (703) 956-6480
 alex.rabinowitch@3phoenix.com
Business Contact
 Linda Leonard
Title: Contracts Manager
Phone: (703) 956-6480
Email: linda.leonard@3phoenix.net
Research Institution
 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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