Interference motion removal for Doppler radar vital sign detection using variational encoder-decoder neural network

Czerkawski, Mikolaj and Ilioudis, Christos and Clemente, Carmine and Michie, Craig and Andonovic, Ivan and Tachtatzis, Christos; (2021) Interference motion removal for Doppler radar vital sign detection using variational encoder-decoder neural network. In: 2021 IEEE Radar Conference (RadarConf21). IEEE National Radar Conference - Proceedings . IEEE, USA, pp. 1-6. ISBN 9781728176093 (https://doi.org/10.1109/RadarConf2147009.2021.9454...)

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Abstract

The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their contributions. A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented. Results show that a convolutional encoder-decoder neural network with a variational objective is capable of learning a meaningful representation space of vital sign Doppler-time distribution facilitating their extraction from a mixture signal. The approach is tested on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. It is demonstrated that the application of the proposed network enhances the extraction of the micro-Doppler frequency corresponding to the respiration rate.

  • Item type:Book Section
    ID code:75252
    Dates:
    Date
    Event
    14 May 2021
    Published
    29 January 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:02 Feb 2021 16:26
    Last modified:11 Apr 2024 00:16
    URI:https://strathprints.strath.ac.uk/id/eprint/75252
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