Open AccessBlock‐sparse non‐uniform norm constraint normalised subband adaptive filter
Author(s) -
Wang Wenyuan,
Zhao Haiquan
Publication year - 2019
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2018.5153
Subject(s) - algorithm , a priori and a posteriori , constraint (computer aided design) , norm (philosophy) , block (permutation group theory) , computer science , system identification , adaptive filter , filter (signal processing) , mathematical optimization , mathematics , data mining , philosophy , geometry , epistemology , political science , law , computer vision , measure (data warehouse)
This study proposes a block‐sparse non‐uniform norm constraint normalised subband adaptive filter (BS‐NNCNSAF) for the block‐sparse system identification problem, which is obtained by minimising a novel cost function involving the non‐uniform mixed l 2, p norm like a constraint. It can achieve better performance compared with the existing algorithms in the block‐sparse system identification. To further enhance the performance of the algorithm, the shrinkage BS‐NNCNSAF (SH‐BS‐NNCNSAF) algorithm is proposed. The proposed SH‐BS‐NNCNSAF algorithm is derived by taking the priori and the posteriori subband errors to achieve the time‐varying subband step sizes. Finally, simulations have been carried out to verify the performance of proposed algorithms. The simulation results verify that the proposed algorithms improve the performance of the filter, in terms of system identification in sparse systems.