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Proceedings Paper

An end-to-end vechicle classification pipeline using vibrometry data
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Paper Abstract

This paper evaluates and expands upon the existing end-to-end process used for vibrometry target classification and identification. A fundamental challenge in vehicle classification using vibrometry signature data is the determination of robust signal features. The methodology used in this paper involves comparing the performance of features taken from automatic speech recognition, seismology, and structural analysis work. These features provide a means to reduce the dimensionality of the data for the possibility of improved separability. The performances of different groups of features are compared to determine the best feature set for vehicle classification. Standard performance metrics are implemented to provide a method of evaluation. The contribution of this paper is to (1) thoroughly explain the time domain and frequency domain features that have been recently applied to the vehicle classification using laser-vibrometry data domain, (2) build an end-to-end classification pipeline for Aided Target Recognition (ATR) with common and easily accessible tools, and (3) apply feature selection methods to the end-to-end pipeline. The end-to-end process used here provides a structured path for accomplishing vibrometry-based target identification. This paper will compare with two studies in the public domain. The techniques utilized in this paper were utilized to analyze a small in-house database of several different vehicles.

Paper Details

Date Published: 10 June 2014
PDF: 11 pages
Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790O (10 June 2014); doi: 10.1117/12.2053087
Show Author Affiliations
Ashley Smith, Wright State Univ. (United States)
Olga Mendoza-Schrock, Air Force Research Lab. (United States)
Scott Kangas, Etegent Technologies, Ltd. (United States)
Matthew Dierking, Air Force Research Lab. (United States)
Arnab Shaw, Wright State Univ. (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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