Influence of Radar Signal Processing on Deep Learning-based Classification
2023 IEEE RADAR CONFERENCE, RADARCONF23(2023)
摘要
As radar technology becomes more readily available to researchers and users, it is thus being explored how to better process this data for real-time implementations. To process this radar data, the short time Fourier transform (STFT) has been implemented to then find the micro-Doppler spectrogram. When computing the STFT, there are parameters which can be adjusted to alter the size of the resulting micro-Doppler spectrogram. In this work, these parameters were adjusted to find the optimal representation of micro-Doppler radar returns of human activities, which were recorded using a 77 GHz Frequency Modulated Continuous Wave (FMCW) millimeter wave radar. To determine these optimal combinations, the resulting micro-Doppler spectrograms were used to train and test a Convolutional Autoencoder (CAE). The t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-Nearest Neighbor Classification (kNN) were also utilized to find the nearest representations in a low-dimensional space of the spectrograms.
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关键词
radar,micro-Doppler,deep learning,time-frequency transform
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