Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions

Dong, Xiaopeng; Weng, Haixiao; Beetner, Daryl G.; Hubing, T.H.; Wunsch, Donald C.; Noll, Manuel; Göksu, Hüseyin; Moss, Benjamin

doi:10.1109/temc.2006.882841

Cited by 33 publications

(12 citation statements)

References 13 publications

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“…Despite of the complexity and variety of the emitted RF signal from the vehicles, this signal contains information about the vehicles' situation. For instance in [33,34], RF emission is used to detect various car models, but in the isolated test environment.…”

Section: Sources Of Rf Noise In the Vehiclesmentioning

confidence: 99%

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

Ding

Banitalebi

Miyaki

et al. 2012

J Wireless Com Network

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In this article, a new traffic sensing and monitoring technique is introduced which works based on the emitted RF noise from the vehicles. In comparison with the current traffic sensing systems, our light-weight technique has simpler structure in both terms of hardware and software. An antenna installed to the roadside or the inside of a car receives the signal generated during electrical activity of the vehicles' sub-systems. This signal feeds the feature extraction and classification blocks which recognize different classes of traffic situation in terms of density, flow and location. Different classifiers like naive Bayes, Decision Tree and k-Nearest Neighbor are applied in real-world scenarios and performances for instance of traffic situation detection are reported with higher than 95%. Although the electrical noises of the various vehicles do not have the same statistical characteristics, results from two experiments with an implementation on RF receiver illustrate that our approach is practically feasible for traffic monitoring goals. Due to the acceptable classification results and the differences between the proposed and current traffic monitoring techniques in terms of interfering factors, advantages and disadvantages, we propose it to work in parallel with the current systems to improve the coverage and efficiency of the traffic control network.

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Section: Sources Of Rf Noise In the Vehiclesmentioning

confidence: 99%

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

Ding

Banitalebi

Miyaki

et al. 2012

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Despite of the complexity and variety of the emitted RF signal from the vehicles, this signal contains information about the vehicles' situation (e.g. in [19], [20] RF emission is used to detect various car models).…”

Section: Sources Of Rf Noise In the Vehiclesmentioning

confidence: 99%

RFTraffic: Passive traffic awareness based on emitted RF noise from the vehicles

Ding

Banitalebi

Miyaki

et al. 2011

2011 11th International Conference on ITS Telecommunications

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Abstract-In this paper, a new traffic monitoring technique is introduced which works based on the emitted RF noise from the vehicles. In comparison with the current traffic sensing systems, our light-weight technique has simpler structure in both terms of hardware and software. An antenna installed to the roadside receives the signal generated during electrical activity of the vehicles' sub-systems. This signal feeds the feature extraction and classification blocks which recognize different classes of traffic situation in terms of density and flow. Different classifiers like Naive Bayes, Decision Tree and k-Nearest Neighbor are applied in real-world scenarios and performances higher than 95% are reported. Although the electrical noises of the various vehicles do not have the same statistical characteristics, experimental analysis shows that they are applicable for traffic monitoring goals. Due to the acceptable classification results and the differences between the proposed and current traffic monitoring techniques in terms of interfering factors, advantages and disadvantages, we propose it to work in parallel with the current systems to improve the coverage and efficiency of the traffic control network.

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“…Artificial neural networks (ANNs) have been exploited in different EMC problems such as detection and identification of vehicles based on their unintended radiated emissions [12], target discrimination [13,14], calculation of multilayer magnetic shielding [15], estimating PCB configuration from EMI measurements [16], characterization and modeling of the susceptibility of integrated circuits to conducted electromagnetic disturbances [17], recognition and identification of radiated EMI for shielding apertures [18], prediction of electromagnetic field in metallic enclosures [19], adaptive beamforming [20,21], PAD modeling [22], and detection of dielectric cylinders buried in a lossy half-space [23]. This paper takes advantage of MLP neural networks to model and estimate motorcycle's radiated emissions in terms of the registered velocity.…”

Section: Introductionmentioning

confidence: 99%

Modeling Radiated Electromagnetic Emissions of Electric Motorcycles in Terms of Driving Profile Using MLP Neural Networks

Wefky¹,

Espinosa²,

Santiago³

et al. 2013

PIER

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Abstract-Current automotive electromagnetic compatibility (EMC) standards do not discuss the effect of the driving profile on real traffic vehicular radiated emissions. This paper describes a modeling methodology to evaluate the radiated electromagnetic emissions of electric motorcycles in terms of the driving profile signals such as the vehicle velocity remotely controlled by means of a CAN bus. A time domain EMI measurement system has been used to measure the temporal evolution of the radiated emissions in a semi-anechoic chamber. The CAN bus noise has been reduced by means of adaptive frequency domain cancellation techniques. Experimental results demonstrate that there is a temporal relationship between the motorcycle velocity and the radiated emission power in some specific frequency ranges. A Multilayer Perceptron (MLP) neural model has been developed to estimate the radiated emissions power in terms of the motorcycle velocity. Details of the training and testing of the developed neural estimator are described.

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Detection and Identification of Vehicles Based on Their Unintended Electromagnetic Emissions

Cited by 33 publications

References 13 publications

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

RFTraffic: Passive traffic awareness based on emitted RF noise from the vehicles

Modeling Radiated Electromagnetic Emissions of Electric Motorcycles in Terms of Driving Profile Using MLP Neural Networks

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