Space alternating variational estimation based sparse Bayesian learning for complex‐value sparse signal recovery using adaptive Laplace priors

Autor: Zonglong Bai, Liming Shi, Jinwei Sun, Mads Græsbøll Christensen
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IET Signal Processing, Vol 17, Iss 3, Pp n/a-n/a (2023)
Druh dokumentu: article
ISSN: 1751-9683
1751-9675
DOI: 10.1049/sil2.12179
Popis: Abstract Due to its self‐regularising nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most existing methods are based on the real‐value signal model, with the complex‐value signal model rarely considered. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning framework, a hierarchical model with adaptive Laplace priors is proposed in this paper for recovery of complex sparse signals. Moreover, the space alternating approach is integrated into the algorithm to reduce the computational complexity of the proposed method. In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals. These experiments show that the proposed algorithm offers better recovery performance for different types of complex signals than state‐of‐the‐art methods.
Databáze: Directory of Open Access Journals
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