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Novel human gesture recognition and classification technique is suggested and experimentally studied. Suggested strategy is based on exploiting the interactions of human gestures with high-frequency electromagnetic field. Extracting of classification features contained in the wireless radio signal modulated by human gestures is proposed by utilizing bispectrum-based processing of the signal envelope...
In the work presented here we propose a novel bicoherence-based method for the classification of aerial radar targets in automatic target recognition (ATR) systems. The possibility of classifying aerial targets using the micro-Doppler contributions caused by a jet engine or the rotor of a helicopter is studied. The method is based on classification features computed in the form of bicoherence estimates,...
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 required for estimation of period. Modeled...
In this report, two novel algorithms for ground moving target classification using additional information features related to the radial velocity variability are proposed and studied. These approaches are compared with feature extraction method based on linear prediction model and cepstrum coefficients. Results of computer simulations performed by exploiting real-life radar backscattering signal records...
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 and multi-frequency backscattered radar signals is defined and studied. Method aimed for extraction of frequency and phase coupling by using bicoherence estimation is suggested. Bicoherence-based...
The possibility of aerial target classification by extraction of micro-Doppler contributions contained in radar returns is studied. Novel classification features based on time-frequency distribution estimation are proposed in order to increase the aspect-independence property of classifier. Classification probability rates are computed for three different types of aerial targets including helicopter...
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 classification algorithm was tested by utilizing...
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