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A Novel Acoustic Pattern Recognition System for Wireless Sensor Networks

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-06-M-0201
Agency Tracking Number: N064-036-0443
Amount: $70,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N06-T036
Solicitation Number: N/A
Solicitation Year: 2006
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-08-01
Award End Date (Contract End Date): 2007-05-31
Small Business Information
15400 Calhoun Drive Suite 400
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Leonard Haynes
 (301) 294-5250
Business Contact
 Mark James
Title: Contracts and Proposals M
Phone: (301) 294-5221
Research Institution
 Lane M Owsley
1013 NE 40th Street Box 355640
Seattle, WA 98105
United States

 (206) 543-1300
 Nonprofit College or University

Wireless Sensor Networks (WSNs) have demonstrated their effectiveness in threat detection and localization. However, there are two critical issues related to signal processing in WSNs. First, energy-efficiency, memory-consumption, bandwidth-utilization, and computation-complexity are of main concern. Second, the application environments contain highly colored noise, multi-path echoes, and simultaneous emission sources. To address these issues, Intelligent Automation, Inc. (IAI) and its subcontractor, the University of Washington, propose a novel acoustic threat recognition system. The proposed system architecture is distributed and hierarchical. The functions of threat detection, classification and source localization are organized in multiple levels. At each level, the information processing task is performed in a distributed manner. On the other hand, the proposed system architecture allows cooperation among sensing nodes to collaboratively detect target signatures, reduce false alarms, classify target types, and estimate the acoustic source location. The system combines recent advances in Wavelet Analysis, intelligent learning and sensor fusion. In particular, the proposed Discrete Wavelet Packet Transform-based power-law detection algorithm is robust to environmental noise, yet computationally efficient. The advantages of the proposed threat recognition system include energy efficiency, reliable detection and classification, low detection and classification latency, reduced false alarms, efficient bandwidth utilization, and accurate source location estimation. BENEFITS: In addition to acoustic threat recognition, the proposed system along with the wireless sensor network architecture can be used in a widely range of other applications, such as moving vehicle tracking, speaker identification, firefighter/military personnel tracking, etc. The resulting technology can be readily used in law enforcement, border protection, etc. Moreover, the proposed classification and data fusion algorithms can also find other applications such as health monitoring of electro-mechanical systems, and intrusion detection in computer networks.

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

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