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OA

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%.