Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220800247-6.doi: 10.11896/jsjkx.220800247

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

FMCW Radar Human Behavior Recognition Based on Residual Network

LUO Jinyan1, CHANG Jun1,2, WU Peng1, XU Yan1, LU Zhongkui1   

  1. 1 College of Information Science & Engineering,Yunnan University,Kunming 650500,China
    2 University Key Laboratory of Internet of Things Technology and Application,Kunming 650500,China
  • Published:2023-11-09
  • About author:LUO Jinyan,born in 1996.Her main research interests include radar human behavior recognition and so on.
    CHANG Jun,born in 1970,associate professor,postgraduate’s supervisor,is a member of China Computer Federation.His main research interests include intelligent wireless perception and wireless communication.

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Abstract: 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.

Key words: FMCW radar, Micro-Doppler spectrograms, Behavior recognition, Residual networks, Convolutional block attention module

CLC Number: 

  • TN959
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