Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach
نویسندگان
چکیده
منابع مشابه
Consensus based Decentralized Sparse Bayesian Learning for Joint Sparse Signal Recovery
This work proposes a decentralized, iterative, Bayesian algorithm called CB-DSBL for in-network estimation of multiple jointly sparse vectors by a network of nodes, using noisy and underdetermined linear measurements. The proposed algorithm exploits the network wide joint sparsity of the unknown sparse vectors to recover them from significantly fewer number of local measurements compared to sta...
متن کاملClustered Pattern Sparse Signal Recovery Using Hierarchical Bayesian Learning
Recently, we proposed a novel hierarchical Bayesian learning algorithm for the recovery of sparse signals with unknown clustered pattern for the general framework of multiple measurement vectors (MMVs). In order to recover the unknown clustered pattern we incorporated a parameter to learn the number of transitions over the support set of the solution. This parameter does not exist in other algo...
متن کاملClarify Some Issues on the Sparse Bayesian Learning for Sparse Signal Recovery
Sparse Bayesian learning (SBL) is an important family of algorithms for sparse signal recovery and compressed sensing. It has shown superior recovery performance in challenging practical problems, such as highly underdetermined inverse problems, recovering signals with less sparsity, recovering signals based on highly coherent measuring/sensing/dictionary matrices, and recovering signals with r...
متن کاملSimultaneous Block-Sparse Signal Recovery Using Pattern-Coupled Sparse Bayesian Learning
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize both the block-sparsity of the coefficients and the statistical dependency betw...
متن کاملSparse Signal Representation: Image Compression using Sparse Bayesian Learning
with Φ ∈ RN×M , M ≥ N , and some noise . The challenge is to determine the sparsest representation of reconstruction coefficients w = [w1, . . . , wM ] . Finding a sparse representation of a signal in an overcomplete dictionary is equivalent to solving a regularized linear inverse. For a given dictionary Φ, finding the maximally sparse w is an NP-hard problem [1]. A great deal of recent researc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal and Information Processing over Networks
سال: 2017
ISSN: 2373-776X,2373-7778
DOI: 10.1109/tsipn.2016.2612120