A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
This addresses pedestrian identification for radar-based systems, but appears incremental as it builds on existing micro-Doppler signature methods.
The paper tackles pedestrian identification using radar micro-Doppler signatures by proposing a multi-characteristic learning model that jointly learns discrepant signatures and fuses knowledge from clusters, achieving higher accuracy and stability compared to other studies.
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.