Abstract
Millimeter wave radar offers advantages in scene surveillance, traffic monitoring and health monitoring due to its penetrability and privacy. Abnormal human behaviors could be identified through the radar detection and classification process. In this paper, an abnormal human activity classification method based on micro-Doppler effect is proposed. The singular vector decomposition (SVD) and principle component analysis (PCA) are extracted from simulated radar echo and fed into a Generalized Regression Neural Network (GRNN) for classification.
Original language | English |
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Title of host publication | 2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728121680 |
DOIs | |
Publication status | Published - May 2019 |
Event | 11th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Guangzhou, China Duration: 19 May 2019 → 22 May 2019 |
Publication series
Name | 2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings |
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Conference
Conference | 11th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 |
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Country/Territory | China |
City | Guangzhou |
Period | 19/05/19 → 22/05/19 |
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Mao, T., Zhao, G., & Sun, H. (2019). Human Activity Classification Method Using a Generalized Recurrent Neural Network. In 2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings Article 8992580 (2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMMT45702.2019.8992580