| dc.creator |
Wunsch, Donald C. |
|
| dc.creator |
Dong, Xiaopeng |
|
| dc.creator |
Weng, Haixiao |
|
| dc.creator |
Beetner, Daryl G. |
|
| dc.creator |
Hubing, Todd H. |
|
| dc.creator |
Noll, Michael |
|
| dc.creator |
Goksu, Huseyin |
|
| dc.creator |
Moss, Benjamin |
|
| dc.date |
2006-11-01T01:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:15:04Z |
|
| dc.date.available |
2021-12-03T11:15:04Z |
|
| dc.identifier |
0dcfa4f5-8f10-463a-b5e8-e221daf1bbfb |
|
| dc.identifier |
10.1109/temc.2006.882841 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/0dcfa4f5-8f10-463a-b5e8-e221daf1bbfb/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/89933 |
|
| dc.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. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Detection and identification of vehicles based on their unintended electromagnetic emissions |
|
| dc.type |
info:eu-repo/semantics/article |
|