A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification

• URL: http://arxiv.org/abs/2203.12236v1
• Date: Wed, 23 Mar 2022 07:12:39 GMT
• Title: A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
• Authors: Yu Xiang, Yu Huang, Haodong Xu, Guangbo Zhang, and Wenyong Wang
• Abstract summary: We propose a multi-characteristic learning model with clusters to jointly learn discrepant pedestrian micro-Doppler signatures. Our model achieves a higher accuracy rate and is more stable for pedestrian identification than other studies.
• Score: 14.878153838197353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model with clusters to jointly learn discrepant pedestrian micro-Doppler signatures and fuse the knowledge learned from each cluster into final decisions. Time-Doppler spectrogram (TDS) and signal statistical features extracted from FMCW radar, as two categories of micro-Doppler signatures, are used in MCL to learn the micro-motion information inside pedestrians' free walking patterns. The experimental results show that our model achieves a higher accuracy rate and is more stable for pedestrian identification than other studies, which make our model more practical.

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