Block sparse reweighted zero-attracting normalised least mean square algorithm for system identification

To improve the performance for identifying the block sparse system, a block sparse reweighted zero-attracting normalised least mean square algorithm (NLMS) (BS-RZA-NLMS) is proposed in this Letter. The proposed algorithm is derived by applying block sparsity constraint on the cost function of the NLMS, which is a log-sum penalty of adaptive tap weights with equal block partition sizes. The convergence behaviour of the BS-RZA-NLMS is analysed in terms of the zero attraction and block partition. Simulation results demonstrate the performance advantage of the proposed algorithm in the context of block sparse system identification.