Correntropy Induced Metric Penalized Sparse RLS Algorithm to Improve Adaptive System Identification

Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementati...

Full description

Saved in:

Bibliographic Details
Published in:	2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) pp. 1 - 5
Main Authors:	Guan Gui, Linglong Dai, Baoyu Zheng, Li Xu, Adachi, Fumiyuki
Format:	Conference Proceeding
Language:	English Japanese
Published:	IEEE 01.05.2016
Subjects:	Adaptive systems Algorithm design and analysis Filtering algorithms Kernel Measurement Signal to noise ratio System identification
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l_1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).
DOI:	10.1109/VTCSpring.2016.7504179