Micro-motion Target Classification Based on FMCW Radar Using Extended Residual Neural Network

Micro Doppler (m-D) effect is a phenomenon that provides signatures to discriminate different moving objects. Accordingly, this paper presents a novel residual convolutional neural network that can classify different moving targets based on m-D analysis of reflected frequency modulation continuous wave (FMCW) radar signals. The proposed network is optimized through the experiments of varying number of residual blocks. As a result, the proposed network yields the average classification accuracy of 93.48 % with five residual blocks, 64 filters per convolution layer, and the filter size of 3 × 3. Moreover, thanks to the residual connection, our network remarkably outperforms two other existing networks in terms of accuracy. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.