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Proceedings Paper

Laser vibrometry exploitation for vehicle identification
Author(s): Adam Nolan; Andrew Lingg; Steve Goley; Kevin Sigmund; Scott Kangas
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Paper Abstract

Vibration signatures sensed from distant vehicles using laser vibrometry systems provide valuable information that may be used to help identify key vehicle features such as engine type, engine speed, and number of cylinders. Through the use of physics models of the vibration phenomenology, features are chosen to support classification algorithms. Various individual exploitation algorithms were developed using these models to classify vibration signatures into engine type (piston vs. turbine), engine configuration (Inline 4 vs. Inline 6 vs. V6 vs. V8 vs. V12) and vehicle type. The results of these algorithms will be presented for an 8 class problem. Finally, the benefits of using a factor graph representation to link these independent algorithms together will be presented which constructs a classification hierarchy for the vibration exploitation problem.

Paper Details

Date Published: 10 June 2014
PDF: 14 pages
Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790Q (10 June 2014); doi: 10.1117/12.2053515
Show Author Affiliations
Adam Nolan, Etegent Technologies, Ltd. (United States)
Andrew Lingg, Etegent Technologies, Ltd. (United States)
Steve Goley, Etegent Technologies, Ltd. (United States)
Kevin Sigmund, Etegent Technologies, Ltd. (United States)
Scott Kangas, Etegent Technologies, Ltd. (United States)

Published in SPIE Proceedings Vol. 9079:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V
Michael A. Kolodny, Editor(s)

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