Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach
  Based on Micro-Doppler Signatures
Haoming Li Yu Xiang Haodong Xu Wenyong Wang
As a hot topic in recent years, the ability of pedestrians identification
based on radar micro-Doppler signatures is limited by the lack of adequate
training data. In this paper, we propose a data-enhanced multi-characteristic
learning (DEMCL) model with data enhancement (DE) module and
multi-characteristic learning (MCL) module to learn more complementary
pedestrian micro-Doppler (m-D) signatures. In DE module, a range-Doppler
generative adversarial network (RDGAN) is proposed to enhance free walking
datasets, and MCL module with multi-scale convolution neural network (MCNN) and
radial basis function neural network (RBFNN) is trained to learn m-D signatures
extracted from enhanced datasets. Experimental results show that our model is
3.33% to 10.24% more accurate than other studies and has a short run time of
0.9324 seconds on a 25-minute walking dataset.