Work

Adaptive and Self-Tuning SBL With Total Variation Priors for Block-Sparse Signal Recovery

Year: 2025

Type: article

Abstract: This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by applying an adaptive total variation (TV) penalty on the hyper-parameter space of the sparse signal. The main contributions are two-fold. 1) We extend the TV penalty beyond the immediate neighbor, thus enabling better capture of the signal structure. 2) A dynamic framework is provided to learn the regularization weights for the TV penalty based on the statistical dependencies between the entries of tentative blocks, thus eliminating the need for fine-tuning. The superior performance of the proposed method is empirically demonstrated by extensive computer simulations with the state-of-art benchmarks. The proposed solution exhibits both excellent performance and robustness against sparsity model mismatch.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2025.3556790

Authors: Hamza Djelouat, Reijo Leinonen, Mikko J. Sillanpää, Bhaskar D. Rao, Markku Juntti

Institutions: University of Oulu, University of California San Diego

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: Academy of Finland