Space Alternating Variational Estimation Based Sparse Bayesian Learning for Complex-value Sparse Signal Recovery Using Adaptive Laplace Priors

TL;DR

This paper introduces a novel space alternating variational estimation method with adaptive Laplace priors for complex-valued sparse signal recovery, improving performance and reducing computational complexity.

Contribution

It extends sparse Bayesian learning to complex signals using a hierarchical model and space alternating approach, which is a new combination for this problem.

Findings

Outperforms state-of-the-art methods in complex signal recovery

Effective for various complex signal types and dictionaries

Reduces computational complexity compared to existing algorithms

Abstract

Due to its self-regularizing 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 (SBL) 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.