Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

the proportionate recursive least squares (PRLS) algorithm has shown faster convergence and better performance than both proportionate updating (PU) mechanism based least mean squares (LMS) algorithms and RLS algorithms with a sparse regularization term. In this paper, we propose a variable forgetti...

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Bibliographic Details
Published in:China communications Vol. 21; no. 10; pp. 1 - 12
Main Authors: Zhen, Han, Fengrui, Zhang, Yu, Zhang, Yanfeng, Han, Peng, Jiang
Format: Journal Article
Language:English
Published: China Institute of Communications 01.10.2024
GNSS Research Center,Wuhan University,Wuhan 430072,China%School of Information Science and Technology,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
Subjects:
Adaptive filters
dichotomous coordinate descent
Filtering
proportionate matrix
RLS
Signal to noise ratio
Sparse matrices
sparse systems
Steady-state
System identification
variable forgetting factor
Vectors
RLS
propor-tionate matrix
variable forget-ting factor
sparse systems
dichotomous coordinate descent
ISSN:1673-5447
Online Access:Get full text
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Summary:the proportionate recursive least squares (PRLS) algorithm has shown faster convergence and better performance than both proportionate updating (PU) mechanism based least mean squares (LMS) algorithms and RLS algorithms with a sparse regularization term. In this paper, we propose a variable forgetting factor (VFF) PRLS algorithm with a sparse penalty, e.g., l 1 -norm, for sparse identification. To reduce the computation complexity of the proposed algorithm, a fast implementation method based on dichotomous coordinate descent (DCD) algorithm is also derived. Simulation results indicate superior performance of the proposed algorithm.
ISSN:1673-5447
DOI:10.23919/JCC.ja.2022-0367