重庆邮电大学学报（自然科学版）2024，Vol.36Issue(2)：268-276,9.DOI:10.3979/j.issn.1673-825X.202212120363
基于改进粒子群算法的UWB雷达人体动作识别研究
Research on UWB radar human motion recognition based on improved particle swarm optimization algorithm
摘要
Abstract
For the clutter interference in radar signals and the limitation of the number of samples on the accuracy of human motion recognition,this paper proposes an ultra-wideband(UWB)radar human motion recognition algorithm based on im-proved particle swarm optimization(PSO)to optimize the support vector machine(SVM)model.Moving target indication(MTI)and wavelet threshold filtering are used to preprocess the received UWB echo signals to eliminate the influence of clutter and noise in the echo signals on human motion recognition.Two-dimensional discrete wavelet packet decomposition(2D-DWPD)and singular value decomposition(SVD)are combined to extract features and reduce dimensions of the pre-processed radar signals.An improved particle swarm optimization algorithm is proposed to optimize relevant parameters of the SVM model for recognition and classification.Experimental results show that the accuracy of the proposed algorithm can reach 96.25%,and it has good recognition performance.关键词
超宽带雷达/人体动作识别/小波阈值滤波/改进粒子群算法Key words
ultra-wideband radar/human motion recognition/wavelet threshold filtering/improved particle swarm optimiza-tion algorithm分类
信息技术与安全科学引用本文复制引用
李新春,曾仕豪..基于改进粒子群算法的UWB雷达人体动作识别研究[J].重庆邮电大学学报（自然科学版）,2024,36(2):268-276,9.基金项目
国家自然科学基金项目(61971210) The National Natural Science Foundation of China(61971210) （61971210）
