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Distributed Pattern Detection and Classification in Sensor Networks

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-10-C-0139
Agency Tracking Number: F09B-T09-0319
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF09-BT09
Solicitation Number: 2009.B
Solicitation Year: 2009
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-04
Award End Date (Contract End Date): 2011-03-04
Small Business Information
14163 Furlong Way
Germantown, MD 20874
United States
DUNS: 809088599
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Genshe Chen
 (240) 481-5397
Business Contact
 Genshe Chen
Title: VP
Phone: (240) 481-5397
Research Institution
 Syracuse University
 Ruixin Niu
Department of Electrical Engin 4-206 Center for Science and T
Syracuse, NY 13244
United States

 (315) 443-4416
 Nonprofit College or University

In this proposal, DCM Research Resources (DCM), LLC, and Syracuse University propose a highly innovative distributed pattern detection and classification approach, called Compressive Sensing aided Sequential Pattern Detection and Classification (CSASPDC) in Distributed Sensor Network. Our goal is to develop sophisticated approaches that can effectively detect or classify very weak distributed patterns that are undetectable in the local signatures at individual nodes. At the mean time, any solution to pattern detection and classification needs to take into account the very limited energy and communication bandwidth. We propose a pattern detection/classification framework that combines sophisticated techniques in several areas, including compressive sensing, distributed detection, game theoretic sensor selection and management for detection/estimation, and sequential detection/classification, and secure cognitive radio, leveraging our previous experiences in these areas BENEFIT: The proposed compressive sensing aided sequential pattern detection and classification (CSASPDC) algorithm for distributed sensor network is very important in many military (DoD) applications including reconnaissance and surveillance, homeland security, etc. It can be directly used for developing of advanced mission planning and emergency preparedness decision support systems such as CB agent defense, Space Situational Awareness Fusion Intelligent Research Environment [SAFIRE] program, JSPOC Situational Awareness Response System (JSARS), BMDS system, Future Combat System (FCS), Joint Strike Fighter (JSF) program, and JSSEO program. During the Phase I, we will work closely with Lockheed Martin MS2, who is prime contractor on the Aegis weapon system, the Littoral Combat Ship, and C2 lead for the DDG-1000 program. We have developed a strong and realistic plan to transition our technology to their programs (support letter attached). In addition, DCM and Lockheed Martin are building a mentor-protégé program. We will leverage this relationship to identify the end customer, and work with these teams to transition our Phase 2 technology into their program. The DOD contact who knows the details of our work and who knows the above programs is Dr. Erik Blasch from AFRL. The market for military applications is quite large.Other potential commercial applications include air traffic control system, network security intrusion detection, the national weather service, physical security systems, law enforcement agency, emergency control center, border and coast patrol, pollution monitoring, remote sensing, robotics, medical applications, and global awareness. The size of this market is not small and hard to estimate. We expect the aggregate market size will be similar to that of military applications.

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

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