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Hand gesture classification using 24 GHz FMCW dual polarised radar

Ritchie, M; Jones, A; Brown, J; Griffiths, HD; (2017) Hand gesture classification using 24 GHz FMCW dual polarised radar. In: International Conference on Radar Systems (Radar 2017). IET Green open access

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All rights reserved. This paper evaluates the classification performance of a dual polarised on receive, 24 GHz Frequency Modulated Continuous Wave (FMCW) radar system to autonomously identify micro-Doppler signatures of unique hand gestures. We employ an Eigen subspace feature selection technique on the calculated signal subspace in order to classify each gesture. Measurements using the dual polarised radar, permitting simultaneous recording of both the co-pol and cross-pol returns, are evaluated with this processing technique and results are reported herein. Our analysis displays the challenges presented by the high variance in individuals gestures and the limited additional information the cross polarised returns have provided to the classifier. Classification performance comparisons are presented when co, cross and dual polarised data are provided to the classifier. With this technique we achieve autonomous classification performance of up to 84.6% when Eigenvalue derived features are used for classification.

Type: Proceedings paper
Title: Hand gesture classification using 24 GHz FMCW dual polarised radar
Event: International Conference on Radar Systems (Radar 2017)
ISBN: 978-1-78561-673-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1049/cp.2017.0482
Publisher version: https://doi.org/10.1049/cp.2017.0482
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: FMCW Radar, Micro-Doppler, Machine Learning, Classification, Autonomy
URI: http://discovery.ucl.ac.uk/id/eprint/10051758
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