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.

本文提出了一种数据增强的多特征学习模型（DEMCL），通过数据增强模块和多特征学习模块学习更多互补的行人微多普勒（m-D）信号，并通过实验证明该模型比其他研究的准确率高 3.33% 至 10.24%，且其对于一套 25 分钟的步行数据的短时运行时间为 0.9324 秒。

基于微多普勒特征的雷达数据增强深度学习方法的行人识别