Recursive regularisation parameter selection for sparse RLS algorithm

In this Letter, the authors propose a recursive regularisation parameter selection method for sparse recursive least squares (RLS) algorithm. The proposed RLS algorithm is regularised by a convex function, which equals the linear combination of two convex functions, one to cope with random sparsity,...

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Veröffentlicht in:Electronics letters Jg. 54; H. 5; S. 286 - 287
Hauptverfasser: Sun, Dajun, Liu, Lu, Zhang, Youwen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 08.03.2018
Schlagworte:
adaptive filtering
adaptive filters
Circuits and systems
convex regularised penalty function
group sparsity
l0‐norm
l2,0‐norm
least squares approximations
random sparsity
recursive regularisation parameter selection method
sparse recursive least squares algorithm
sparse RLS algorithm
time‐varying sparse system
sparse recursive least squares algorithm
adaptive filters
least squares approximations
l0-norm
adaptive filtering
recursive regularisation parameter selection method
random sparsity
sparse RLS algorithm
l2,0-norm
group sparsity
convex regularised penalty function
time-varying sparse system
ISSN:0013-5194, 1350-911X, 1350-911X
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Zusammenfassung:In this Letter, the authors propose a recursive regularisation parameter selection method for sparse recursive least squares (RLS) algorithm. The proposed RLS algorithm is regularised by a convex function, which equals the linear combination of two convex functions, one to cope with random sparsity, and the other to cope with group sparsity. The normal equations corresponding to the RLS algorithm with the proposed convex regularised penalty function are derived, and a recursive algorithm to update the regularisation parameters (i.e. the coefficients of the linear combination) is proposed. As an example, by using the linear combination of an $l_0$l0-norm and an $l_{2\comma 0}$l2,0-norm as the penalty function, simulation results show that the proposed sparse RLS with recursive regularisation parameter selection can achieve better performance in terms of mean square error for a slowly time-varying sparse system with both random sparsity and group sparsity.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2017.4242