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29 March 2023 SampleRadarNet: end-to-end deep convolutional neural networks using small filters for radar-based human activity classification
Bing Yu, Zixuan Ou, Yu Zhang, Wenbin Ye
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Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 1259422 (2023) https://doi.org/10.1117/12.2671274
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China
Abstract
Methods for classifying different human activities have been explored widely in recent years. In radar-based human activity classification, most deep learning-based techniques pay great attention to the frame-level structure. These approaches generally adopt short-time Fourier transform, or convolutional layers with a frame-level filter to process the raw radar data, while they seldom consider the overall efficiency. In this paper, we propose a sample-level convolutional neural network named SampleRadarNet. The proposed SampleRadarNet utilizes one-dimensional convolutional layers with small filters to learn more temporal information from the raw data, breaking through the frame-level model’s capacity limitation. In addition, the Squeeze-Inception-Mobile module is designed for the classification task, which involves the following three components: fire module, inception block, and depthwise convolution. The experimental results show that the proposed architecture can achieve better performance compared with the existing related methods in a seven-class radar-based human activity classification problem.
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Bing Yu, Zixuan Ou, Yu Zhang, and Wenbin Ye "SampleRadarNet: end-to-end deep convolutional neural networks using small filters for radar-based human activity classification", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 1259422 (29 March 2023); https://doi.org/10.1117/12.2671274
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KEYWORDS
Radar signal processing
Deep convolutional neural networks
Engineering
Signal filtering
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