Research Article
Micro-motion Target Classification Based on FMCW Radar Using Extended Residual Neural Network
@INPROCEEDINGS{10.1007/978-3-030-77424-0_9, author={Hai Le and Van-Sang Doan and Dai Phong Le and Thien Huynh-The and Van-Phuc Hoang}, title={Micro-motion Target Classification Based on FMCW Radar Using Extended Residual Neural Network}, proceedings={Industrial Networks and Intelligent Systems. 7th EAI International Conference, INISCOM 2021, Hanoi, Vietnam, April 22-23, 2021, Proceedings}, proceedings_a={INISCOM}, year={2021}, month={5}, keywords={Convolution neural network Micro Doppler Moving target}, doi={10.1007/978-3-030-77424-0_9} }- Hai Le
Van-Sang Doan
Dai Phong Le
Thien Huynh-The
Van-Phuc Hoang
Year: 2021
Micro-motion Target Classification Based on FMCW Radar Using Extended Residual Neural Network
INISCOM
Springer
DOI: 10.1007/978-3-030-77424-0_9
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.
