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Bayesian learning for the Type-3 joint sparse signal recovery

Accepted version
Peer-reviewed

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Conference Object

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Authors

Chen, W 
Wassel, IJ 

Abstract

Compressed sensing (CS) is a signal acquisition paradigm that utilises the finding that a small number of linear projections of a sparse signal have enough information for stable recovery. This paper develops a Bayesian CS algorithm to simultaneously recover multiple signals that follow the Type-3 joint sparse model, where signals share a non-sparse common component and have distinct sparse innovation components. By employing the expectation-maximization (EM) algorithm, the proposed algorithm iteratively updates the estimates of the common component and innovation components. In particular, we find that the update rule for the non-sparse common component in the proposed algorithm, differs from all the other methods in the literature, and we provides an interpretation that gives a valuable insight into why the proposed algorithm is successful in estimating the non-sparse common component. The superior performance of the proposed algorithm is demonstrated by numerical simulation results.

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Keywords

compressive sensing (CS), distributed compressive sensing (DCS), Bayesian learning, signal reconstruction

Journal Title

2016 IEEE International Conference on Communications, ICC 2016

Conference Name

ICC 2016 - 2016 IEEE International Conference on Communications

Journal ISSN

1550-3607

Volume Title

Publisher

IEEE
Sponsorship
Engineering and Physical Sciences Research Council (EP/K033700/1)
This work is supported by EPSRC Research Grant EP/K033700/1; the Natural Science Foundation of China (61401018, U1334202); the Fundamental Research Funds for the Central Universities (No. 2014JBM14 9); the State Key Laboratory of Rail Traffic Control and Safety (RCS2016ZT014) of Beijing Jiaotong University.