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| Title: | Detection and identification of vehicles based on their unintended electromagnetic emissions | |
| Author (s): | Dong, Xiaopeng Weng, Haixiao Beetner, Daryl G. Hubing, Todd H. Wunsch, Donald C. Noll, Michael Goksu, Huseyin Moss, Benjamin | |
| Department/Lab Affiliations: | Applied Computational Intelligence Laboratory Electrical and Computer Engineering | |
| Keywords: | Detectors electromagnetic radiation identification neural networks vehicles | |
| Issue Date: | 2006-11 | |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | |
| Citation: | Dong, Xiaopeng., Haixiao Weng, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch, Michael Noll, Huseyin Goksu, and Benjamin Moss. "Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions." IEEE Transactions on Electromagnetic Compatibility, 48, (2006). | |
| Abstract: | 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. | |
| Type: | Article - Journal text | |
| In Title: | IEEE Transactions on Electromagnetic Compatibility | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Detection and identification of vehicles based on their unintended electromagnetic emissions | |
| contributor.author | Dong, Xiaopeng | |
| contributor.author | Weng, Haixiao | |
| contributor.author | Beetner, Daryl G. | |
| contributor.author | Hubing, Todd H. | |
| contributor.author | Wunsch, Donald C. | |
| contributor.author | Noll, Michael | |
| contributor.author | Goksu, Huseyin | |
| contributor.author | Moss, Benjamin | |
| contributor.deptlab | Applied Computational Intelligence Laboratory | |
| contributor.deptlab | Electrical and Computer Engineering | |
| subject | Detectors | |
| subject | electromagnetic radiation | |
| subject | identification | |
| subject | neural networks | |
| subject | vehicles | |
| date.issued | 2006-11 | |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
| identifier.citation | Dong, Xiaopeng., Haixiao Weng, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch, Michael Noll, Huseyin Goksu, and Benjamin Moss. "Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions." IEEE Transactions on Electromagnetic Compatibility, 48, (2006). | |
| identifier.pub.URI | ||
| description.abstract | 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. | |
| type | Article - Journal | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| relation.isPartOf | IEEE Transactions on Electromagnetic Compatibility | |
| date.accessioned | 2008-03-19T16:53:46Z | |
| date.available | 2008-03-19T16:53:48Z | |
| identifier.persist.URI | ||
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