Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions
When running, vehicles with internal combustion engines radiate electromagnetic emissions that are characteristic of the vehicle. Emissions depend on the electronics, harness wiring, body type, and many other features. Since emissions are unique to each vehicle, these may be used for identification...
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ftmissouriunivst:oai:scholarsmine.mst.edu:ele_comeng_facwork-1968 2023-05-15T18:40:22+02:00 Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions Dong, Xiaopeng Weng, Haixiao Beetner, Daryl G. Hubing, Todd H. Wunsch, Donald C. Noll, Michael Goksu, Huseyin Moss, Benjamin 2006-11-01T08:00:00Z application/pdf https://scholarsmine.mst.edu/ele_comeng_facwork/969 https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1968&context=ele_comeng_facwork unknown Scholars' Mine https://scholarsmine.mst.edu/ele_comeng_facwork/969 https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1968&context=ele_comeng_facwork © 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved. Electrical and Computer Engineering Faculty Research & Creative Works Detectors Electromagnetic Radiation Identification Neural Networks Vehicles Electrical and Computer Engineering text 2006 ftmissouriunivst 2022-08-09T21:01:26Z When running, vehicles with internal combustion engines radiate electromagnetic emissions that are characteristic of the vehicle. Emissions depend on the electronics, harness wiring, body type, and many other features. Since emissions are unique to each vehicle, these may be used for identification purposes. This paper investigates a procedure for detecting and identifying vehicles based on their RF emissions. Parameters like the average magnitude or standard deviation of magnitude within a frequency band were extracted from measured emission data. These parameters were used as inputs to an artificial neural network (ANN) that was trained to identify the vehicle that produced the emissions. The approach was tested using the emissions captured from a Toyota Tundra, a GM Cadillac, a Ford Windstar, and ambient noise. The ANN was able to classify the source of signals with 99% accuracy when using emissions that captured an ignition spark event. Text Tundra Missouri University of Science and Technology (Missouri S&T): Scholars' Mine |
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Missouri University of Science and Technology (Missouri S&T): Scholars' Mine |
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Detectors Electromagnetic Radiation Identification Neural Networks Vehicles Electrical and Computer Engineering |
spellingShingle |
Detectors Electromagnetic Radiation Identification Neural Networks Vehicles Electrical and Computer Engineering Dong, Xiaopeng Weng, Haixiao Beetner, Daryl G. Hubing, Todd H. Wunsch, Donald C. Noll, Michael Goksu, Huseyin Moss, Benjamin Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions |
topic_facet |
Detectors Electromagnetic Radiation Identification Neural Networks Vehicles Electrical and Computer Engineering |
description |
When running, vehicles with internal combustion engines radiate electromagnetic emissions that are characteristic of the vehicle. Emissions depend on the electronics, harness wiring, body type, and many other features. Since emissions are unique to each vehicle, these may be used for identification purposes. This paper investigates a procedure for detecting and identifying vehicles based on their RF emissions. Parameters like the average magnitude or standard deviation of magnitude within a frequency band were extracted from measured emission data. These parameters were used as inputs to an artificial neural network (ANN) that was trained to identify the vehicle that produced the emissions. The approach was tested using the emissions captured from a Toyota Tundra, a GM Cadillac, a Ford Windstar, and ambient noise. The ANN was able to classify the source of signals with 99% accuracy when using emissions that captured an ignition spark event. |
format |
Text |
author |
Dong, Xiaopeng Weng, Haixiao Beetner, Daryl G. Hubing, Todd H. Wunsch, Donald C. Noll, Michael Goksu, Huseyin Moss, Benjamin |
author_facet |
Dong, Xiaopeng Weng, Haixiao Beetner, Daryl G. Hubing, Todd H. Wunsch, Donald C. Noll, Michael Goksu, Huseyin Moss, Benjamin |
author_sort |
Dong, Xiaopeng |
title |
Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions |
title_short |
Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions |
title_full |
Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions |
title_fullStr |
Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions |
title_full_unstemmed |
Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions |
title_sort |
detection and identification of vehicles based on their unintended electromagnetic emissions |
publisher |
Scholars' Mine |
publishDate |
2006 |
url |
https://scholarsmine.mst.edu/ele_comeng_facwork/969 https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1968&context=ele_comeng_facwork |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
Electrical and Computer Engineering Faculty Research & Creative Works |
op_relation |
https://scholarsmine.mst.edu/ele_comeng_facwork/969 https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=1968&context=ele_comeng_facwork |
op_rights |
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved. |
_version_ |
1766229694293737472 |