Radar Human Tangential Activity Recognition Based on Three-Channel CNN-GSAM-LSTFEM Network
In order to improve the performance of interferometric radar for human tangential activity recognition,a human tangential activity recognition method based on three-channel CNN-GSAM-LSTFEM network is proposed in this paper.Firstly,an interferometric radar platform is constructed using a frequency modulated continuous wave(FMCW)radar with one transmitter and two receivers to collect the human tangential motion echo data.Subsequently,the echo da-ta are preprocessed to obtain the Doppler time-frequency map(DTFM)for each receiving channel and the two-channel interferometric time-frequency map(ITFM).Then,the three obtained time-frequency maps are separately fed into three parallel CNN-GSAM-LSTFEM networks for training.The global spatial attention module(GSAM)and long-short time feature extraction module(LSTFEM)are used to enhance the feature extraction ability of convolutional neural network(CNN).Finally,the features extracted from the three channels are fused to achieve human tangential activity recogni-tion.The experimental results show that the proposed method can effectively improve the recognition accuracy of human tangential activities and the average accuracy is as high as 98.77%.
human activity recognitioninterferometric radarattention mechanismCNNfeature fusion
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