Multi-person micro-Doppler signal decomposition based on millimeter wave radar

Jiazhi Yu; Jun Zhang; Zhuo Li; Yan Zhou; Yue Geng

doi:10.1117/12.3023198

28 March 2024 Multi-person micro-Doppler signal decomposition based on millimeter wave radar

Jiazhi Yu, Jun Zhang, Zhuo Li, Yan Zhou, Yue Geng

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910W (2024) https://doi.org/10.1117/12.3023198
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

Aiming at the problem of spectrum aliasing of micro-Doppler signature generated by millimeter-wave radar when many people move at the same time, a multi-person micro-Doppler signal decomposition method based on millimeter-wave radar is proposed. By preprocessing the data collected by radar, the position information is obtained by using the time-distance map, and the range of single person's position is divided by using the range bin according to the position information, and the micro-Doppler signature is obtained by short-time Fourier transform of different range bins to extract human posture characteristics. The micro-Doppler signature obtained by decomposing micro-Doppler signals are used to extract different gesture features when multiple people act at the same time. The separated micro-Doppler signatures are recognized in VGG16, ResNet50, DenseNet121 and MobileNetV2 convolutional neural networks, and the recognition accuracy is as high as 99%, 98%, 99.5% and 98.5% respectively. Experiments verify the effectiveness of the multi-person micro-Doppler signal decomposition method based on millimeter-wave radar.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiazhi Yu, Jun Zhang, Zhuo Li, Yan Zhou, and Yue Geng "Multi-person micro-Doppler signal decomposition based on millimeter wave radar", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910W (28 March 2024); https://doi.org/10.1117/12.3023198

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KEYWORDS

Radar signal processing

Radar

Doppler effect

Feature extraction

Radar sensor technology

Target detection

Convolutional neural networks

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