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Proceedings Paper

Effects of fundamental frequency normalization on vibration-based vehicle classification
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Paper Abstract

Vibrometry offers the potential to classify a target based on its vibration spectrum. Signal processing is necessary for extracting features from the sensing signal for classification. This paper investigates the effects of fundamental frequency normalization on the end-to-end classification process [1]. Using the fundamental frequency, assumed to be the engine’s firing frequency, has previously been used successfully to classify vehicles [2, 3]. The fundamental frequency attempts to remove the vibration variations due to the engine’s revolution per minute (rpm) changes. Vibration signatures with and without fundamental frequency are converted to ten features that are classified and compared. To evaluate the classification performance confusion matrices are constructed and analyzed. A statistical analysis of the features is also performed to determine how the fundamental frequency normalization affects the features. These methods were studied on three datasets including three military vehicles and six civilian vehicles. Accelerometer data from each of these data collections is tested with and without normalization.

Paper Details

Date Published: 21 May 2015
PDF: 13 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94741A (21 May 2015); doi: 10.1117/12.2180636
Show Author Affiliations
Ashley Smith, Wright State Univ. (United States)
Steve Goley, Etegent Technologies, Ltd. (United States)
Karmon Vongsy, Air Force Research Lab. (United States)
Arnab Shaw, Wright State Univ. (United States)
Matthew Dierking, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)

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