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In this paper, an approach for ground-moving target classification with an FMCW radar is proposed. In particular, data are collected using a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi device for data acquisition and processing. An FFT-based processing scheme is then applied to obtain a sequence of range-Doppler maps, which are provided in input to different convolutional neural network (CNN) architectures for classifying the targets (cars, motorcycles, or pedestrians) eventually passing in front of the radar. Specifically, two approaches have been followed and compared. In the first one, single range-Doppler maps are processed alone using a convolutional neural network, and then a voting mechanism is applied to select the target classes. In the second approach, a sequence of range-Doppler maps is processed using a time-distributed layer feeding a recurrent neural network. The CNNs are deployed on the Raspberry Pi providing the target classification on a low-cost embedded device. The obtained results show that the proposed approaches allow for effectively detecting the different types of targets running on an embedded device in less than one second.

On the Edge Recurrent Neural Network Approach for Ground Moving FMCW Radar Target Classification