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Proceedings Paper

Exploiting vibration-based spectral signatures for automatic target recognition
Author(s): Lauren Crider; Scott Kangas
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Paper Abstract

Feature extraction algorithms for vehicle classification techniques represent a large branch of Automatic Target Recognition (ATR) efforts. Traditionally, vehicle ATR techniques have assumed time series vibration data collected from multiple accelerometers are a function of direct path, engine driven signal energy. If data, however, is highly dependent on measurement location these pre-established feature extraction algorithms are ineffective. In this paper, we examine the consequences of analyzing vibration data potentially contingent upon transfer path effects by exploring the sensitivity of sensor location. We summarize our analysis of spectral signatures from each accelerometer and investigate similarities within the data.

Paper Details

Date Published: 10 June 2014
PDF: 10 pages
Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790N (10 June 2014); doi: 10.1117/12.2052860
Show Author Affiliations
Lauren Crider, Arizona State Univ. (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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