Block‐sparse signal recovery based on truncated ℓ1 minimisation in non‐Gaussian noise

• Published in: IET communications Vol. 13; no. 2; pp. 251 - 258
• Main Authors: Feng, Qingrong, Wang, Jianjun, Zhang, Feng
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 18.01.2019
• Subjects: altemating direction method‐of‐multiplier; augmented Lagrangian methods; block‐sparse signal recovery; compressed sensing; compressive sensing; embedding Karush‐Kuhn‐Tucker system; extended block restricted isometry property; Gaussian noise; generalised ℓp‐norm noise constraint; induced optimisation problem; matrix algebra; measurement matrix; minimisation; nonGaussian measurement noise; nonGaussian noise; reconstruction error; signal reconstruction; truncated ℓ1 minimisation; truncated ℓ1 model; ℓp‐norm functions

This study addresses the issue of block‐sparse recovery in compressive sensing in the presence of non‐Gaussian measurement noise. By using the generalised ℓp‐norm noise constraint for 2≤p<∞ to replace the popular ℓ2‐norm, in this study, the authors put forward a truncated ℓ1 model for recovering...