Description
We present a variational message passing (VMP)-based approach to detect the presence of a person based on their respiratory chest motion using multistatic ultra-wideband (UWB) radar. In the process, the respiratory motion is estimated for contact-free vital sign monitoring. The received signal is modeled as a backscatter channel and the respiratory motion and propagation channels are estimated using VMP. We use the evidence lower bound (ELBO) to approximate the model evidence for the detection. Numerical analyses and measurements demonstrate that the proposed method leads to a significant improvement in the detection performance compared to a fast Fourier transform (FFT)-based detector or an estimator-correlator in low-signal-to-noise ratio (SNR) conditions, since the multipath components (MPCs) are better incorporated into the detection procedure. Specifically, the proposed method has a detection probability of 0.95 at −20dB SNR, while the estimator-correlator and FFT-based detector have 0.32 and 0.05, respectively.Period | 7 Jun 2023 |
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Event title | 48th IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2023 |
Event type | Conference |
Location | Rhodos, GreeceShow on map |
Degree of Recognition | International |
Related content
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Publications
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Variational Message Passing-based Respiratory Motion Estimation and Detection Using Radar Signals
Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Projects
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SEAMAL Front - Securely Applied Machine Learning
Project: Research project