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Advanced Ground Vehicle Classification Using Wavelet/Neural Network Processing of Acoustic Emissions

Award Information
Agency: Department of Defense
Branch: Army
Contract: N/A
Agency Tracking Number: 37092
Amount: $99,826.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1997
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
53 Wood Rd
Marlborough, MA 01752
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Jose E. Lopez
 (508) 481-8587
Business Contact
Phone: () -
Research Institution
N/A
Abstract

CYTEL proposes to develop a novel system for robust, high performance identification of ground vehicles from acoustic emissions. Hallmarks of this approach include the identification of an optimal wavelet decomposition for use in extracting robust multidimensional features from the acoustic emission. These wavelet-based features will be processed by additional feature post processing algorithms and coupled to a programmable, multilayer, neural network for vehicle identification. A series of algorithm variations are to be developed during the Phase I program. These variations will be used to construct a number of vehicle identification systems. All systems will be validated on a set of customer supplied ground vehicle acoustic emission data sets. Results of all algorithm combinations will be reported. The best performing set of algorithms will serve as the core for further development in the Phase II program addressing algorithm enhancements for lower signal to noise ratios and embedding in a easily updated / extendible system for real-time operations. Benefits include development of robust, high performance vehicle identification systems based on wavelet/neural network methods which represent the next generation of signal processing approaches for developing advanced systems. In addition, the Phase I effort will identify the specific combination of algorithms which will lend themselves to embedded real-time operation, leading to deployable, passive surveillance systems for both military and commercial markets.

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

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