Pavlo O. Molchanov

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I. INTRODUCTION This paper reports the results on research into the field of automatic moving radar target recognition and classification in time-frequency (TF) domain by using micro-Doppler radar signatures for extraction information features. The TF distribution is widely used now days for non-stationary signal analysis in ATR surveillance radar systems(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)
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)
—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 micro-Doppler 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)
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)
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