Print Email Facebook Twitter Radar-based Human Activities Classification with Complex-valued Neural Networks Title Radar-based Human Activities Classification with Complex-valued Neural Networks Author Yang, Ximei (Student TU Delft) Guendel, Ronny (TU Delft Microwave Sensing, Signals & Systems) Yarovoy, Alexander (TU Delft Microwave Sensing, Signals & Systems) Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems) Date 2022 Abstract Human activities classification in assisted living is one of the emerging applications of radar. The conventional analysis considers micro-Doppler signatures as the chosen input for feature extraction or deep learning classification algorithms, or, less frequently, other radar data formats such as the range-time, the range-Doppler, or the Cadence Velocity Diagram. However, these data are typically used as real-valued images, whereas they are actually complex-valued data structures. In this paper, neural networks processing radar data as complex data structures are investigated, with a focus on spectrograms, range-time, and range-Doppler plots as the data formats of choice. Different network architectures are explored both in terms of complex numbers' representations and the depth/complexity of the architecture itself. Experimental data with 9 activities and 15 volunteers collected using an UWB radar are used to test the networks' performances. It is shown that for certain data formats and network architectures, there is an advantage in using complex-valued networks compared to their real-valued counterparts. Subject Micro-Doppler ClassificationDeep learningHuman Activity RecognitionComplex-valued Networks To reference this document use: http://resolver.tudelft.nl/uuid:63de4fa6-8570-4c0a-ae2a-a3becabaac23 DOI https://doi.org/10.1109/RadarConf2248738.2022.9763903 Publisher IEEE, Piscataway Embargo date 2022-11-03 ISBN 978-1-7281-5369-8 Source 2022 IEEE Radar Conference (RadarConf22) Proceedings Event 2022 IEEE Radar Conference , 2022-03-21 → 2022-03-25, New York City, United States Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2022 Ximei Yang, Ronny Guendel, Alexander Yarovoy, F. Fioranelli Files PDF Radar_based_Human_Activit ... tworks.pdf 1.57 MB Close viewer /islandora/object/uuid:63de4fa6-8570-4c0a-ae2a-a3becabaac23/datastream/OBJ/view