现代电子技术2026，Vol.49Issue(1)：8-14,7.DOI:10.16652/j.issn.1004-373x.2026.01.002
基于FAST网络的毫米波雷达端到端手势识别
FAST network based end-to-end gesture recognition using millimeter-wave radar
摘要
Abstract
In view of the complexities,inefficiencies,and low accuracy of current millimeter-wave radar gesture recognition methods,this paper proposes an FAST(Fourier-Attention-Swin Transformer)network model.Firstly,a complex-valued linear layer is utilized to construct a Fourier network,and the weights of the Fourier network are initialized with discrete Fourier transform values.Range-Doppler features are obtained after radar' raw data passing through the Fourier network.Secondly,the ECA(efficient channel attention)module is introduced to calculate frame-channel attention weights,enhancing the capability to extract gesture features.Finally,the Swin Transformer is employed to improve computational efficiency and recognition accuracy while expanding the receptive field,and a loss function is used for backpropagation and iterative updates of the model parameters.Experimental results demonstrate that the proposed FAST-based end-to-end gesture recognition algorithm using millimeter-wave radar achieves an accuracy rate of 96.46%,showcasing advanced performance in comparison with the other mainstream algorithms.This study offers a more streamlined and efficient solution for the application of millimeter-wave radar gesture recognition in smart homes and mobile devices.关键词
毫米波雷达/手势识别/人机交互/深度学习/神经网络/离散傅里叶变换Key words
millimeter-wave radar/gesture recognition/human-computer interaction/deep learning/neural network/discrete Fourier transform分类
信息技术与安全科学引用本文复制引用
郑好,李浩然,彭国梁,郑志鹏,胡芬,郇战..基于FAST网络的毫米波雷达端到端手势识别[J].现代电子技术,2026,49(1):8-14,7.基金项目
江苏省研究生科研与实践创新计划项目(SJCX23_1595) （SJCX23_1595）
