基于卷積神經(jīng)網(wǎng)絡(luò)的雷達(dá)人物身份識(shí)別研究

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關(guān)鍵詞：卷積神經(jīng)網(wǎng)絡(luò)；人體身份識(shí)別；微多普勒信號(hào)；雷達(dá)傳感器

中圖分類號(hào)：TN957 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1003-5168(2025)12-0024-05

DOI:10.19968/j.cnki.hnkj.1003-5168.2025.12.005

Convolutional Neural Network-Based Radar Human Identification Research

HE Shaofeng (Yantai Institute of Science and Technology,Yantai 264Ooo, China)

Abstract: [Purposes] Convolutional neural network is highly significant in achieveing human identification,mainly in terms of efficient feature extractionand adaptability to complexscenes.Therefore,the article proposes aradar-based human identification method based on convolutional neural network.[Methods]First,the walking gait echo signals of 1O subjects are preprocessed.Second,the Short-Time Fourier Transform (STFT) is performed to obtain the micro-Doppler time-frequency spectrogram of the human body walking.Finaly,nine types of convolutional neural networks are used to extract their features,and the extracted features are categorized using Softmax classifier to complete the human identification. [Findings] The test results show that,considering the accuracy,network parameters,test time and other factors,SqueezeNet network is more advantageous than the other 8 network in terms of network performance.In addition, the research method of using micro-Doppler signal separation resulted in a 2% increase in accuracy.[Conclusions] Therefore,both the use of micro-Doppler signal separation method and the selection of SqueezeNet network can improve the accuracy of human identification,providing valuable reference and insights for future research inthe field of identification.

Keywords: convolution neural network; human identification; micro-Doppler signal; radar sensors

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