Improved Low-Complexity Sparse Bayesian Learning With Embedded Bayesian Threshold

Yifei Yang

doi:10.1109/LSP.2025.3544536

Improved Low-Complexity Sparse Bayesian Learning With Embedded Bayesian Threshold

2025-01-01

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DOI：10.1109/LSP.2025.3544536

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Sparse Bayesian Learning (SBL) is recognized for its efficacy in sparse signal recovery, the computational demand escalates significantly with increasing data dimensionality due to the matrix inversion at each iteration. An Inverse-Free sparse Bayesian Learning (IF-SBL) approach has been introduced to mitigate computational complexity. However, IF-SBL converges easily to a sub-optimal solution with false peaks due to the neglect of the correlation between atoms. In this paper, we analyze causes of false peaks in IF-SBL. Subsequently, a novel dynamically updated embedded Bayesian threshold is designed to mitigate the interference caused by false peaks. This innovative approach retrieves the stability and reliability without significantly increasing signal recovery complexity compared with IF-SBL. Simulation experiments validate the results.

Keyword:

Bayes methods

Sparse matrices

Noise

Iterative methods

Vectors

Covariance matrices

Computational complexity

Testing

Signal to noise ratio

Signal processing algorithms

Bayesian threshold

compressd sensing

inverse-free sparse Bayesian learning

relaxed evidence lower bound

Improved Low-Complexity Sparse Bayesian Learning With Embedded Bayesian Threshold

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