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 lϵ0-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.
Original language | English |
---|---|
Pages (from-to) | 1-6 |
Number of pages | 6 |
Journal | Signal Processing |
Volume | 143 |
DOIs | |
State | Published - Feb 2018 |
Bibliographical note
Publisher Copyright:© 2017 Elsevier B.V.
Keywords
- Adaptive filter
- Echo cancellation
- NLMS algorithm
- Proportionate matrix
- Zero-attracting penalty