基于残差网络的FMCW雷达人体行为识别

FMCW Radar Human Behavior Recognition Based on Residual Network

现有的基于调频连续波(Frequency Modulated Continuous Wave,FMCW)雷达的人体行为识别方法大多采用深度卷积神经网络完成,但随着网络加深,会出现网络训练难度增大或特征提取不充分的问题。针对此问题,提出一种基于残差网络的FMCW雷达人体行为识别方法。通过对雷达回波数据分析处理得到每种行为的微多普勒时域谱图,将其作为识别模型的分类特征。将卷积块注意模块(Convolutional Block Attention Module,CBAM)引入残差网络的残差块中构建识别模型,CBAM关注谱图的颜色变化情况和谱图中每种颜色的位置信息,同时引入自适配归一化和改变网络输入部分的卷积结构提高模型的特征提取能力。实验验证,该模型的平均识别准确率可达98.17%,对于微多普勒特征相似的行为,识别准确率可达95%,证明了该模型具有较好的识别性能。

For the existing FMCW radar human behavior recognition methods are mostly done by deep convolutional neural networks,however,with the deepening of the network,there will be problems such as the difficulty of network training will increase or the feature extraction will be insufficient.A method for FMCW radar human behavior recognition based on residual network is proposed.The micro-Doppler time-domain spectrogram of each behavior is obtained by analyzing and processing the radar echo data,which is used as the classification feature of the recognition model.The convolutional block attention module(CBAM)is introduced into the residual block of the residual network to build a recognition model.CBAM pays attention to the color change of the spectrogram and the position information of each color in the spectrogram,while introducing adaptive Matching normalization and changing the convolutional structure of the input part of the network improves the feature extraction ability of the model.Through experimental verification,the average recognition accuracy of the model can reach 98.17%,and for behaviors with similar micro-Doppler features,the recognition accuracy can reach 95%,which prove that the model has good recognition perfor-mance.