Tracking Analysis of Maximum Versoria Criterion Based Adaptive Filter

被引：1

作者：

Khalili, Azam ^{[1
]}

Rastegarnia, Amir ^{[1
]}

Farzamnia, Ali ^{[2
]}

Sanei, Saeid ^{[3
]}

Alghamdi, Thamer A. H. ^{[4
,5
]}

机构：

[1] Malayer Univ, Dept Elect Engn, Malayer 6571995863, Iran

[2] Univ Malaysia Sabah, Fac Engn, Kota Kinabalu 88400, Malaysia

[3] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England

[4] Al Baha Univ, Fac Engn, Comp Engn Dept, Al Bahah 65779, Saudi Arabia

[5] Cardiff Univ, Wolfson Ctr Magnet, Sch Engn, Cardiff CF24 3AA, England

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Adaptive filters; Steady-state; Filtering algorithms; Cost function; Information filters; Symbols; Gaussian noise; Robustness; Performance analysis; Tracking; Adaptive filter; non-stationary; performance analysis; tracking; Versoria; RISK-SENSITIVE LOSS; STEADY-STATE; CORRENTROPY; ALGORITHM;

D O I：

10.1109/ACCESS.2024.3370471

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, maximum Versoria criterion-based adaptive algorithms have been introduced as a new solution for robust adaptive filtering. This paper studies the steady-state tracking analysis of an adaptive filter with maximum Versoria criterion (MVC) in a non-stationary (Markov time-varying) system. Our analysis relies on the energy conservation method. Both Gaussian and general non-Gaussian noise are considered, and for both cases, the closed-form expression for steady-state excess mean square error (EMSE) is derived. Regardless of noise type, unlike the stationary environment, the EMSE curves are not increasing functions of step-size parameter. The validity of the theoretical results is justified via simulation.

引用

页码：30747 / 30753

页数：7

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