An adjusting-block based convex combination algorithm for identifying block-sparse system

Abstract

A novel block wise convex combination algorithm with adjusting blocks is proposed for block-sparse system identification. The proposed algorithm unifies the complementary advantages of different block induced algorithms, which are based on block proportionate matrix and block zero attracting penalty. A mixing parameter for block wise combination is designed as a block diagonal matrix. The mixing parameter is obtained using the conventional mixing parameter, which represents convergence state, and a block activeness indicator. The indicator for each block is derived from the l-norm measure of the block. Moreover, a block adjustment algorithm is developed using the indicator to overcome the main disadvantage of block-induced algorithms, i.e., the dependency on cluster location. The simulations for system identification are performed on several block-sparse systems including systems with single cluster and double clusters. The simulation results show that the proposed algorithm not only combines the different block-induced algorithms effectively but also improves the performance via the block adjustment algorithm. (C) 2017 Elsevier B.V. All rights reserved.