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.
@article{arxiv.2203.12236,
title = {A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification},
author = {Yu Xiang and Yu Huang and Haodong Xu and Guangbo Zhang and Wenyong Wang},
journal= {arXiv preprint arXiv:2203.12236},
year = {2022}
}