The proportionate updating (PU) and zero-attracting (ZA) mechanisms have been applied independently in the development of sparsity-aware recursive least squares (rls) algorithms. Recently, we propose an enhanced l1- p...
详细信息
The proportionate updating (PU) and zero-attracting (ZA) mechanisms have been applied independently in the development of sparsity-aware recursive least squares (rls) algorithms. Recently, we propose an enhanced l1- proportionate rls (l1-Prls) algorithm by combining the PU and ZA mechanisms. The l1-Prls employs a fixed step size which trades off the transient (initial convergence) and steady-state performance. In this letter, the l1- Prls is improved in two aspects: first, we replace the l1 norm penalty by a general convex regularization (CR) function to have the CR-Prls algorithm;second, we further introduce the variable step-size (VSS) technique to the CR-Prls, leading to the VSS-CR-Prls algorithm. Theoretical and numerical results were provided to corroborate the superiority of the improved algorithm.
暂无评论