Bistatic Human micro-Doppler Signatures for Classification of Indoor Activities

Fioranelli, F. , Ritchie, M. and Griffiths, H. (2017) Bistatic Human micro-Doppler Signatures for Classification of Indoor Activities. In: 2017 IEEE Radar Conference (RadarConf), Seattle, WA, USA, 08-12 May 2017, 0610-0615. ISBN 9781467388238 (doi: 10.1109/RADAR.2017.7944276)

142143.pdf - Accepted Version



This paper presents the analysis of human micro- Doppler signatures collected by a bistatic radar system to classify different indoor activities. Tools for automatic classification of different activities will enable the implementation and deployment of systems for monitoring life patterns of people and identifying fall events or anomalies which may be related to early signs of deteriorating physical health or cognitive capabilities. The preliminary results presented here show that the information within the micro-Doppler signatures can be successfully exploited for automatic classification, with accuracy up to 98%, and that the multi-perspective view on the target provided by bistatic data can contribute to enhance the overall system performance.

Item Type:Conference Proceedings
Keywords:Bistatic radar, feature extraction and classification, machine learning, micro-Doppler.
Glasgow Author(s) Enlighten ID:Fioranelli, Dr Francesco
Authors: Fioranelli, F., Ritchie, M., and Griffiths, H.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Published Online:08 June 2017
Copyright Holders:Copyright © 2017 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
701101EPSRC 2015 DTPMary Beth KneafseyEngineering and Physical Sciences Research Council (EPSRC)EP/M508056/1RSI - RESEARCH STRATEGY & INNOVATION