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

Seung Hun Kim, Gyogwon Koo, Jae Jin Jeong, Sang Woo Kim

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 languageEnglish
Pages (from-to)1-6
Number of pages6
JournalSignal Processing
Volume143
DOIs
StatePublished - Feb 2018

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.

Keywords

  • Adaptive filter
  • Echo cancellation
  • NLMS algorithm
  • Proportionate matrix
  • Zero-attracting penalty

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