2005
DOI: 10.1049/el:20057769
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Bayesian detection of Wi-Fi transmitter RF fingerprints

Abstract: A transient detection technique is presented for the detection of the turn-on transients of Wi-Fi radios. The turn-on transients are detected by a Bayesian change detector, which estimates the time instant when the transmitter starts to power up. The proposed technique is verified with the transient data collected from a number of Wi-Fi radios and it is shown that the ramp detector outperforms the abrupt change detector in detecting the turn-on transients of Wi-Fi transmitters.

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Cited by 97 publications
(51 citation statements)
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“…The data should be preprocessed by Hilbert transform to obtain the complex-valued data format. During the period of data preprocessing, the exact turn-on time instant of radio emitter can be detected with Bayesian ramp detector or variance trajectory detector [7][8]. The transient section and steady-state signal section can be truncated as ROI signals.…”
Section: Signal Collection and Detectionmentioning
confidence: 99%
See 1 more Smart Citation
“…The data should be preprocessed by Hilbert transform to obtain the complex-valued data format. During the period of data preprocessing, the exact turn-on time instant of radio emitter can be detected with Bayesian ramp detector or variance trajectory detector [7][8]. The transient section and steady-state signal section can be truncated as ROI signals.…”
Section: Signal Collection and Detectionmentioning
confidence: 99%
“…The accuracy of the detection and separation of the turnon transients is crucial to the overall identification performance. The Bayesian ramp detector, variance trajectory detector and correlation detector have been reported for the detection of the start instants of transients [7][8][9]. The RF fingerprint features are generated from instantaneous amplitude, phase, and frequency responses of transient signal [10].…”
Section: Introductionmentioning
confidence: 99%
“…However, the output power level of some transients change slowly, e.g., Wi-Fi. For the problem, a Bayesian ramp change detector is reported in [4]. Both approaches in [3,4] are based on the fact that amplitude features of the channel noise and transient are different, so they are sensitive to noise.…”
Section: Introductionmentioning
confidence: 99%
“…For the problem, a Bayesian ramp change detector is reported in [4]. Both approaches in [3,4] are based on the fact that amplitude features of the channel noise and transient are different, so they are sensitive to noise. Another approach is proposed in [5], based on the fact that the slope of the signal phase becomes and remains linear from the starting point.…”
Section: Introductionmentioning
confidence: 99%
“…Identifying wireless devices according to their radio frequency fingerprints (RFF) to control their access has been proposed to enhance the physical layer security of Wi-Fi networks [2]. RFF is the transformation of a received radio signal that carries hardware information of the transmitter part of the radio to be identified, and is comparable.…”
mentioning
confidence: 99%