Bayesian compressed sensing with new sparsity-inducing prior

Understand

Sparse Bayesian learning (SBL) is a popular approach to sparse signal recovery in compressed sensing (CS).

• In SBL, the signal sparsity information is exploited by assuming a sparsity-inducing prior for the signal that is then estimated using Bayesian inference.
• In this paper, a new sparsity-inducing prior is introduced and efficient algorithms are developed for signal recovery.
• The main algorithm is shown to produce a sparser solution than existing SBL methods while preserving their desirable properties.