雷达科学与技术2026，Vol.24Issue(3)：289-298,10.DOI:10.3969/j.issn.1672-2337.2026.03.006
基于点云数据与微多普勒谱图多模态特征的雷达步态识别
Radar Gait Recognition Based on Point Cloud and Micro-Doppler Multimodal Features
摘要
Abstract
Gait recognition based on radar is a promising technique for human identity recognition.However,tradi-tional methods relying solely on single features in a single modality or on intra-domain multi-feature fusion are insuffi-cient to support robust gait recognition.While Mamba networks have demonstrated superior performance in computer vi-sion,most existing multimodal gait recognition approaches lack advanced network architectures,leaving their recogni-tion capabilities underdeveloped.Furthermore,gait recognition methods based on dual-sensor modality feature fusion—such as those utilizing radar cameras—face practical challenges including high equipment costs and privacy concerns.To enhance this,a novel radar gait recognition approach based on the PLVM dual-stream network is introduced in this paper.Initially,gait echo data from human walking are acquired using millimeter-wave radar.After preprocessing,micro-Doppler spectrograms and radar point cloud data are obtained and fed into the PLVM network for training.The PLVM network comprises a PointNet branch with bidirectional long short-term memory network(BiLSTM)and a Vision Mamba(ViM)branch with state space model(SSM).It effectively extracts micro-Doppler features from the spectro-grams and spatiotemporal features from the point clouds.By complementing and fusing these two modalities,the method achieves human gait recognition.Experimental results demonstrate that this method significantly outperforms traditional single-modal recognition methods,achieving a test accuracy rate of up to 98.36%.关键词
步态识别/毫米波雷达/雷达点云/微多普勒特征/Vision Mamba网络/多模态特征融合Key words
gait recognition/millimeter-wave radar/radar point cloud/micro-doppler features/vision mamba network/multimodal feature fusion分类
信息技术与安全科学引用本文复制引用
孙延鹏,王爽,屈乐乐,李侠洋..基于点云数据与微多普勒谱图多模态特征的雷达步态识别[J].雷达科学与技术,2026,24(3):289-298,10.基金项目
国家自然科学基金(61671310) （61671310）
航空科学基金(2019ZC054004) （2019ZC054004）
辽宁省高校基本科研业务费(LJ222410143071) （LJ222410143071）
