Bayesian Learning For The Type-3 Joint Sparse Signal Recovery
IEEE International Conference on Communications
MetadataShow full item record
Chen, W., & Wassell, I. J. (2016). Bayesian Learning For The Type-3 Joint Sparse Signal Recovery. IEEE International Conference on Communications https://doi.org/10.1109/ICC.2016.7511061
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.
compressive sensing (CS), distributed compressive sensing (DCS), Bayesian learning, signal reconstruction
External DOI: https://doi.org/10.1109/ICC.2016.7511061
This record's URL: https://www.repository.cam.ac.uk/handle/1810/266089