  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.
