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Radar Application of Deep Neural Networks for Recognizing Micro-Doppler Radar Signals by Human Walking and Background Noise

The purpose of this paper is to show the radar application of the deep neural networks for recognizing the micro-Doppler radar signals generated by human walking and background noises. We collected various signals considering the actual human walking motion and background noise characteristics. In this paper, unlike the previous researches that required complicated feature extractions, we directly use the FFT results of the input signal as the feature vectors. This technique helps not to use heuristic approaches to get meaningful feature vectors. We designed and analyzed MLP (Multilayer perceptron) and DNN for multiclass classifiers. According to the experimental result, the classification accuracy of MLP was measured as 89.8% for the test dataset. The classification accuracy of DNN was analyzed as 97.2% for the test dataset.