Abstract
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 \(3\times 3\). Moreover, thanks to the residual connection, our network remarkably outperforms two other existing networks in terms of accuracy.
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