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

作　　者：Han Zhen Zhang Fengrui Zhang Yu Han Yanfeng Jiang Peng 

机构地区：[1]GNSS Research Center,Wuhan University,Wuhan 430072,China [2]School of Information Science and Technology,Shijiazhuang Tiedao University,Shijiazhuang 050043,China

出　　处：《China Communications》2024年第10期16-27,共12页中国通信（英文版）

基　　金：supported by National Key Research and Development Program of China(2020YFB0505803);National Key Research and Development Program of China(2016YFB0501700)。

摘　　要：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.

关 键 词：dichotomous coordinate descent proportionate matrix RLS sparse systems variable forgetting factor 

分 类 号：TN713[电子电信—电路与系统]