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Classification in C-band of Doppler signatures of human activities in indoor environment

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Abstract

This paper explains CentraleSupélec team's approach for the challenge of human activity classification with radar [1]. Our methodology is based on a convolutional network classifier processing the range-time and spectrogram representations. The preprocessing and the neural network architecture presented in this paper allowed us to reach 99% accuracy on challenge's test data of the challenge. A more advanced preprocessing using wavelets is also briefly discussed. Index Terms-Human activity classification, deep convolutional neural networks, spectrogram, Doppler-range diagram, rangetime diagram.
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Dates and versions

hal-03022656 , version 1 (24-11-2020)

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Cite

Pierre Cadart, Marin Merlin, Giovanni Manfredi, Jérémy Fix, Chengfang Ren, et al.. Classification in C-band of Doppler signatures of human activities in indoor environment. IET International Radar Conference 2020, 2021, Chongqing, China. ⟨10.1049/icp.2021.0597⟩. ⟨hal-03022656⟩
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