论文标题
带有微型多普勒签名的多特征学习方法，用于行人身份证
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
论文作者
论文摘要
近年来，使用Radar Micro-Doppler签名的行人识别已成为一个热门话题。在本文中，我们提出了一个具有群集的多特征学习模型（MCL）模型，以共同学习差异的行人微型多普勒签名，并融合从每个集群中学到的知识中的最终决策。从FMCW雷达中提取的时间多普勒频谱图（TDS）和信号统计特征，作为微型多普勒签名的两类，在MCL中使用，以学习行人免费的步行模式中的微动信息。实验结果表明，与其他研究相比，我们的模型达到了更高的准确率，并且在行人识别方面更稳定，这使我们的模型更加实用。
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.
