Sparse bayesian methods and their applications - Zhao, Lifan (2016)
The theory of compressed sensing (CS) has been extensively investigated and successfully applied in various areas over the past several decades. The key ingredient in this technique is the proper exploitation of sparsity, which allows the recovery of high-dimensional signals from their low-dimensional projections. Although a large number of sparse signal recovery algorithms have been proposed in the literature to achieve this objective, there still exist various opportunities and challenges in developing new algorithms to obtain more accurate and robust signal recovery performances. Among these algorithms, sparse Bayesian methods are recently developed to achieve higher accuracy and more flexibility. This thesis focuses on various issues of sparse Bayesian methods, including developing new algorithms and applying them to practical applications, such as radar imaging and wireless communication.