Detection and Identification of Vehicles based on their Spark-Free Unintended Electromagnetic Emissions

Abstract

Detection and identification of devices using their electromagnetic emissions is a widespread practice. Vehicles are among the most complex with emissions from a whole range of electrical and mechanical components. The authors in a previous work [11], detected and identified vehicles using their electromagnetic emissions by neural network analysis of data derived from the fast Fourier transform (FFT) of measurements. The method was successful provided there was an ignition spark event captured. In this letter, the authors focus on the no-spark case and instead of FFT, use wavelet packet analysis (WPA). WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and nonstationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are further analyzed to obtain feature vectors of log energy entropy. Similar to the previous work [11], training and testing is done on separate days. Emissions from three cars and ambient noise are analyzed and then classified using a multilayer perceptron. 100% detection and identification rate is accomplished when there is no ignition spark event present.

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research

Keywords and Phrases

Electric sparks; Electromagnetic dispersion; Electromagnetic wave emission; Entropy; Fast Fourier transforms; Fourier analysis; Multilayers; Personnel training; Vehicles; Wavelet analysis; Electromagnetic emissions; Electromagnetics; Multi layer perceptron; Time frequency analysis; Vehicle identification; Wavelet Packet; Wavelet Packet Analysis; Electromagnetic compatibility; Automotive EMC; Multilayer perceptron (MLP); Wavelet packet analysis (WPA)

International Standard Serial Number (ISSN)

0018-9375; 1558-187X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Oct 2018

Link to Full Text

Share

 
COinS