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2019 | OriginalPaper | Chapter

Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery

Authors : Lu Wang, Lifan Zhao, Guoan Bi, Xin Liu

Published in: Wireless and Satellite Systems

Publisher: Springer International Publishing

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Abstract

Clustered sparse signals recovery with unknown cluster sizes and locations is considered in this paper. An improved alternative extended block sparse Bayesian learning algorithm (AEBSBL) is proposed. The new algorithm is motivated by the graphic models of the extended block sparse Bayesian learning algorithm (EBSBL). By deriving the graphic model of EBSBL, an equivalent cluster structured prior for sparse coefficients is obtained, which encourages dependencies among neighboring coefficients. With the sparse prior, other necessary probabilistic modelings are constructed and Expectation and Maximization (EM) is applied to infer all the unknowns. The alternative algorithm reduces the unknowns of EBSBL. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.

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Metadata
Title
Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery
Authors
Lu Wang
Lifan Zhao
Guoan Bi
Xin Liu
Copyright Year
2019
Book
Wireless and Satellite Systems

Print ISBN: 978-3-030-19152-8

Electronic ISBN: 978-3-030-19153-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-19153-5_1

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