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

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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.
Original languageEnglish
Title of host publication2021 IEEE Radar Conference (RadarConf21)
Place of PublicationNew York, NY.
Number of pages6
ISBN (Electronic)9781728176093
ISBN (Print)9781728176109
Publication statusPublished - 14 May 2021
Event2021 IEEE Radar Conference - Virtual/Atlanta, GA, USA, Atlanta, United States
Duration: 10 May 202114 May 2021

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2021 IEEE Radar Conference
Abbreviated titleRadarConf 2021
Country/TerritoryUnited States
Internet address


  • Doppler radar
  • heart rate monitoring
  • respiration rate monitoring
  • vital signs
  • random body movement
  • variational autoencoder


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