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

This paper is the first approach combining the BP-NLMS and the BZA-NLMS.We propose a block activeness indicator for block wise convex combination.We propose a block adjustment algorithm to overcome disadvantage of previous works.Proposed algorithm converges as fast as the BP-NLMS.Proposed algorithm shows the low steady-state misalignment as the BZA-NLMS. 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 l0-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.

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