Pavlo O. Molchanov

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Overtaking on rural roads may cause severe accidents when oncoming traffic is detected by a driver too late, or its speed is underestimated. Recently proposed cooperative overtaking assistance systems are based on real-time video transmission, where a video stream captured with a camera installed at the windshield of a vehicle is compressed, broadcast(More)
A novel bicepstrum-based strategy is suggested for moving target recognition and classification by ground surveillance Doppler radars. Bicepstral coefficients extracted from nonstationary backscattered radar signals are used as the information features in automatic target recognition (ATR) system for solving a problem of moving human recognition and(More)
5 In this paper, we propose a novel bicoherence-based method for the classification of aerial radar targets in 6 automatic target recognition (ATR) systems. The possibility of classifying aerial targets using the micro-Doppler 7 contributions caused by a jet engine or the rotor of a helicopter is studied. The method is based on classification 8 features(More)
A novel approach dedicated to estimation of period in micro-Doppler radar signatures represented in the form of time-frequency distribution is suggested. The approach is based on the exploiting short-time Fourier transform performed with window sliding along the micro-Doppler spectrogram. No a priori information about radar object under classification is(More)
Higher-order statistics are proposed to compute and analyze micro-Doppler signatures of radar backscattering related to ground moving targets. The presence of frequency and phase coupling in non-stationary multi-frequency backscattered radar signals is defined and studied. Method aimed for extraction of frequency and phase coupling by using bicoherence(More)
In this paper, a novel bicepstrum-based approach is proposed for moving radar target classification. In our study, pattern features are extracted from short-time backscattering bispectrum estimates measured by using ground surveillance Doppler radar. Classifier performance is studied by Gaussian mixture model (GMM) and maximum likelihood (ML) making(More)
a novel approach to ground moving targets classification by using information features contained in microDoppler radar signatures is presented. Suggested approach is based on using discrete cosine transform (DCT) coefficients extracted from radar signature as a classification feature and multilayer perceptron (MLP) as a classifier. Proposed pattern(More)
In this article, a novel bicepstrum-based approach is suggested for ground moving radar target classification. Distinctive classification features were extracted from short-time backscattering bispectrum estimates of the micro-Doppler signature. Real radar data were obtained using surveillance Doppler microwave radar operating at 34 GHz. Classifier(More)
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