Human Activities Classification in a Complex Space using Raw Radar Data

Yang, S. , Le Kernec, J. , Fioranelli, F. and Romain, O. (2020) Human Activities Classification in a Complex Space using Raw Radar Data. In: 2019 International Radar Conference, Toulon, France, 23-27 Sept 2019, ISBN 9781728126609 (doi: 10.1109/RADAR41533.2019.171367)

184464.pdf - Accepted Version



The classification of human activities through the utilisation of radar mainly focuses on the analysis of the time-frequency domain, typically through spectrograms obtained with Short-Time Fourier Transform. Commonly the original information format in the raw complex radar data is being ignored. In this work, we propose and evaluate a new recurrent neural network architecture to decode the time sequence of raw radar data over a longer time than previously attempted, through training in a complex space of in-phase and in-quadrature data. We explore a solution for sequence analysis problems when adapting a recurrent neural network; our best network architecture exhibits a significant accuracy in performance for over seven participants with six activities, collected over 60 seconds.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco and Yang, Dr Shufan and Le Kernec, Dr Julien
Authors: Yang, S., Le Kernec, J., Fioranelli, F., and Romain, O.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2020 IEEE
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3015260Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy