期刊
SIGNAL PROCESSING
卷 140, 期 -, 页码 104-109出版社
ELSEVIER
DOI: 10.1016/j.sigpro.2017.05.017
关键词
Robust Bayesian compressed sensing; Variational Bayesian inference; Outlier detection
资金
- National Science Foundation of China [61522104, U1530154]
- National Science Foundation [ECCS-1408182, ECCS-1609393]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1408182] Funding Source: National Science Foundation
We consider the problem of robust compressed sensing where. the objective is to recover a high dimensional sparse signal from compressed measurements partially corrupted by outliers. A new sparse Bayesian learning method is developed for this purpose. The basic idea of the proposed method is to identify the outliers and exclude them from sparse signal recovery. To automatically identify the outliers, we employ a set of binary indicator variables to indicate which observations are outliers. These indicator variables are assigned a beta-Bernoulli hierarchical prior such that their values are confined to be binary. In addition, a Gaussian-inverse Gamma prior is imposed on the sparse signal to promote sparsity. Based on this hierarchical prior model, we develop a variational Bayesian method to estimate the indicator variables as well as the sparse signal. Simulation results show that the proposed method achieves a substantial performance improvement over existing robust compressed sensing techniques. (C) 2017 Elsevier B.V. All rights reserved.
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