Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm

We propose a novel dynamic reweighted ℓ₂ (DRℓ₂) algorithm in the regime of dynamic compressive sensing. Our analysis shows that aiming to solve a Type II optimization problem, DRℓ₂ is effectively minimizing a `convex-concave´ penalty in the coefficients that transitions from a convex region to a concave function using knowledge of past estimations. DRℓ₂ thus provides superior reconstruction performance compared with state-of-the-art dynamic CS algorithms.