  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. The application of the proposed network enhances the
extraction of the micro-Doppler frequency corresponding to the respiration rate
is demonstrated.
