Low-Rank and Joint-Sparse Signal Recovery for Spatially and Temporally Correlated Data Using Sparse Bayesian Learning

Yanbin Zhang; Yangqing Li; Changchuan Yin; Kesen He

ICASSP 2018accepted0 citations

Low-Rank and Joint-Sparse Signal Recovery for Spatially and Temporally Correlated Data Using Sparse Bayesian Learning

Yanbin Zhang, Yangqing Li, Changchuan Yin, Kesen He

Abstract

In order to meet the demands of data-intensive continuous monitoring in wireless body area network, we address a structured sparse signal recovery method to exploit both spatial and temporal correlations in data using compressive sensing (CS). Using a simultaneously low-rank and joint-sparse (L&S) signal model, we employ a Bayesian learning treatment by incorporating an L&S-inducing prior over the data and the appropriate hyperpriors over all hyperparameters, resulting in effective reconstruction of the L&S data. Simulation results suggest that the proposed L&S-bSBL is superior to the state-of-the-art recovery methods in terms of computation burden and runtime cost.

BibTeX

@inproceedings{icassp2018_lowrankandjoints,
  title = {Low-Rank and Joint-Sparse Signal Recovery for Spatially and Temporally Correlated Data Using Sparse Bayesian Learning},
  author = {Yanbin Zhang and Yangqing Li and Changchuan Yin and Kesen He},
  booktitle = {ICASSP 2018},
  year = {2018}
}