Switching criterion for sub-and super-Gaussian additive noise in adaptive filtering

被引：19

作者：

Wang, Gang ^{[1
]}

Xue, Rui ^{[2
]}

Zhao, Ji ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Ctr Robot, Chengdu 611731, Sichuan, Peoples R China

[2] Beihang Univ, Sch Elect & Informat Engn, Beijing, Peoples R China

[3] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

SIGNAL PROCESSING | 2018年 / 150卷

基金：

中国国家自然科学基金;

关键词：

Adaptive filter; Least mean square; Maximum correntropy criterion; Least mean fourth; Normalized kurtosis; MEAN 4TH ALGORITHM; MAXIMUM CORRENTROPY CRITERION; PERFORMANCE ANALYSIS; CONVERGENCE; NETWORKS; ERROR;

D O I：

10.1016/j.sigpro.2018.04.017

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Additive noise distributions can be divided into three types: Gaussian, super-and sub-Gaussian. The existing algorithms for adaptive filtering do not provide a better performance than the least mean square (LMS) method for the super-and sub-Gaussian noise simultaneously. For example, the maximum corren-tropy criterion performs better (worse) than the LMS method for super-Gaussian (sub-Gaussian) noise, whereas the least mean fourth performs better (worse) than the LMS method for sub-Gaussian (super-Gaussian) noise. We propose a criterion for switching between sub-and super-Gaussian additive noise, that could be used to assess whether the error signal had a sub-or super-Gaussian profile, and thus determine which algorithm would work best in the iterative process. Simulations demonstrate that the switching criterion helps the proposed switching algorithm to produce a better performance than the LMS algorithm for sub and super-Gaussian noise simultaneously. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：166 / 170

页数：5

共 50 条

[31] Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise
Izanloo, Reza
Fakoorian, Seyed Abolfazl
Yazdi, Hadi Sadoghi
Simon, Dan
2016 ANNUAL CONFERENCE ON INFORMATION SCIENCE AND SYSTEMS (CISS), 2016,
[32] Adaptive window in the PWVD for the IF estimation of FM signals in additive Gaussian noise
Barkat, B
Boashash, B
Stankovic, LJ
ICASSP '99: 1999 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS VOLS I-VI, 1999, : 1317 - 1320
[33] Estimating the Number of Sinusoids in Additive Sub-Gaussian Noise With Finite Measurements
Qiao, Heng
IEEE SIGNAL PROCESSING LETTERS, 2020, 27 : 1225 - 1229
[34] Adaptive Gaussian Noise Image Removal Algorithm Using Filtering-Based Noise Estimation
Tuan-anh NGUYEN
Hong-son NGUYEN
Min-cheol HONG
JournalofMeasurementScienceandInstrumentation, 2011, 2 (03) : 230 - 234
[35] Blind source separation from hybrid mixture of sub- and super-Gaussian signals by using nonlinear InfoMax principle
Yang, Luxi
He, Zhenya
Chinese Journal of Electronics, 1999, 8 (03): : 292 - 297
[36] Block-based noise estimation using adaptive Gaussian filtering.
Shin, DH
Park, RH
Yang, S
Jung, JH
ICCE: 2005 INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, DIGEST OF TECHNICAL PAPERS, 2005, : 263 - 264
[37] An adaptive spatial filter for additive Gaussian and impulse noise reduction in video signals
Huang, Y
Hui, L
ICICS-PCM 2003, VOLS 1-3, PROCEEDINGS, 2003, : 523 - 526
[38] Additive Non-Gaussian Noise Channel Estimation by Using Minimum Error Entropy Criterion
Heravi, Ahmad Reza
Hodtani, Ghosheh Abed
PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE), 2017, : 181 - 186
[39] Average Capacity Maximization via Channel Switching in the Presence of Additive White Gaussian Noise Channels and Switching Delays
Sezer, Ahmet Dundar
Gezici, Sinan
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2016, 15 (09) : 6228 - 6243
[40] Property Investigation on the Additive White Gaussian Noise After Sub-Nyquist Sampling
Gong, Tierui
Liu, Zhifeng
Xie, Chuang
Yang, Zhijia
IEEE ACCESS, 2019, 7 : 122820 - 122826

← 1 2 3 4 5 →