Simulation framework for activity recognition and benchmarking in different radar geometries

Zhou, B., Lin, Y., Le Kernec, J. , Yang, S. , Fioranelli, F. , Romain, O. and Zhao, Z. (2021) Simulation framework for activity recognition and benchmarking in different radar geometries. IET Radar, Sonar and Navigation, 15(4), pp. 390-401. (doi: 10.1049/rsn2.12049)

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Radar micro‐Doppler signatures have been proposed for human monitoring and activity classification for surveillance and outdoor security, as well as for ambient assisted living in healthcare‐related applications. A known issue is the performance reduction when the target is moving tangentially to the line of sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric (IF) radar. A simulator is presented to generate synthetic data representative of eight radar systems (monostatic, circular multistatic and in‐line multistatic [IM] and IF) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of different radar systems. Six human activities are considered with signatures originating from motion‐captured data of 14 different subjects. The classification performances are analysed as a function of aspect angles ranging from 0° to 90° per activity and overall. It demonstrates that IF configurations are more robust than IM configurations. However, IM performs better at angles below 55° before IF configurations take over.

Item Type:Articles
Additional Information:Funding information: British Council, Grant/Award Number: 515095884, 514739586; Campus France. Grant Number: 44764WK.
Glasgow Author(s) Enlighten ID:Romain, Professor Olivier and Fioranelli, Dr Francesco and Yang, Dr Shufan and Le Kernec, Dr Julien
Authors: Zhou, B., Lin, Y., Le Kernec, J., Yang, S., Fioranelli, F., Romain, O., and Zhao, Z.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IET Radar, Sonar and Navigation
ISSN (Online):1751-8792
Published Online:10 March 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in IET Radar, Sonar and Navigation 15(4): 390-401
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301526Intelligent RF Sensing for Fall and Health PredictionFrancesco FioranelliEngineering and Physical Sciences Research Council (EPSRC)EP/R041679/1ENG - Systems Power & Energy