Radar-based Human Activity Classification with Cyclostationarity

Du, Y., Li, J., Li, Z., Yu, R., Napolitano, A., Fioranelli, F. and Le Kernec, J. (2023) Radar-based Human Activity Classification with Cyclostationarity. In: 2021 CIE International Conference on Radar (CIE Radar 2021), Haikou, Hainan, China, 15 - 19 December 2021, ISBN 9781665468893 (doi: 10.1109/Radar53847.2021.10027946)

Abstract

Human Activity Classification with radar has made significant progress in the past few years. In this article, we propose a cyclostationarity-based approach in this field of application. Feature extraction, selection, and activity classification as it detects micro-Doppler is made starting from complex-valued cyclostationary statistical functions of the reflected radar signal. The human activity can be recognized with up to 92.6% with the real part, 95.4% with the imaginary part and 95.4% by the combination of real and imaginary part.

• Item Type: Conference Proceedings
• Additional Information: The authors would like to thank the British Council 515095884 and Campus France 44764WK—PHC Alliance France-UK, and PHC Cai Yuanpei – 41457UK for their financial support. We would like to thank Glasgow College-UESTC for their financial support.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fioranelli, Dr Francesco and Le Kernec, Dr Julien
• Authors: Du, Y., Li, J., Li, Z., Yu, R., Napolitano, A., Fioranelli, F., and Le Kernec, J.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2640-7736
• ISBN: 9781665468893
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in Proceedings of the 2021 CIE International Conference on Radar (Radar)
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Related URLs

• Organisation
• Publisher