现代雷达2026，Vol.48Issue(1)：41-47,7.DOI:10.16592/j.cnki.1004-7859.20240801001
基于Dynamic GNN-MB网络的毫米波雷达人体动作识别方法
Human Action Recognition Method of Millimeter-wave Radar Based on Dynamic GNN-MB Network
摘要
Abstract
In the study of human motion recognition,millimeter-wave radar technology is regarded as a more effective alternative considering the limitations in video and image performance and the protection of privacy,as it can protect privacy and improve the accuracy of human motion feature recognition.For the sparse point clouds generated by millimeter-wave radar,a novel graph neural network named dynamic graph neural network with MLP and bi-directional gated recurrent units(Dynamic GNN-MB)is designed.A dynamic edge selection function is incorporated into the graph neural network,enabling it to autonomously learn the edge weights between point clouds and extract features.Furthermore,a complete human activity recognition framework is constructed by combi-ning Dynamic GNN with stacked bidirectional gated recurrent units.The effectiveness of the network is validated using a public dataset in experiments.The results show that the Dynamic GNN-MB network model achieves an accuracy of 97.05%in human action recognition,demonstrating a higher recognition rate compared to other network architectures.关键词
动作识别/毫米波雷达/动态边选择函数/图神经网络/双向门控循环单元Key words
action recognition/millimeter wave radar/dynamic edge selection function/graph neural network/bidirectional gated recurrent units分类
信息技术与安全科学引用本文复制引用
彭国梁,李浩然,胡芬,郑好,郑志鹏,郇战..基于Dynamic GNN-MB网络的毫米波雷达人体动作识别方法[J].现代雷达,2026,48(1):41-47,7.基金项目
江苏省研究生科研与实践创新计划资助项目(SJCX23_1595) （SJCX23_1595）
