Enhancement of vital signals based on low-rank, sparse representation for UWB through-wall radar

The life detection radar plays an important role in earthquake rescue, but the vital signal acquired in practice is often submerged in noise. Improving the signal-to-noise ratio (SNR) of through-wall vital signals is still a challenging task. In this paper, we propose a new vital signal enhancement algorithm based on low-rank, sparse representation. The proposed algorithm decomposes the acquired time-frequency image of echo signals into low-rank and sparse components. The low-rank component captures vital signals, and the environmental noise is contained in the sparse component. The proposed algorithm is compared with the traditional Fast Fourier transform (FFT) algorithm and singular value decomposition (SVD) enhanced algorithm through simulation and experimental data verification. Visual images and quantitative data prove that the output SNR of the proposed vital enhancement algorithm is better than that of the FFT and SVD algorithms.