Laser vibrometry exploitation for vehicle identification

Adam Nolan; Andrew Lingg; Steve Goley; Kevin Sigmund; Scott Kangas

doi:10.1117/12.2053515

10 June 2014 Laser vibrometry exploitation for vehicle identification

Adam Nolan, Andrew Lingg, Steve Goley, Kevin Sigmund, Scott Kangas

Proceedings Volume 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V; 90790Q (2014) https://doi.org/10.1117/12.2053515
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

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.

Citation Download Citation

Adam Nolan, Andrew Lingg, Steve Goley, Kevin Sigmund, and Scott Kangas "Laser vibrometry exploitation for vehicle identification", Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790Q (10 June 2014); https://doi.org/10.1117/12.2053515

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