Steady-State Performance Analysis of Nonlinear Spline Adaptive Filter Under Maximum Correntropy Criterion

Correntropy has recently attracted great attention because of its robustness to non-Gaussian noise. Inspired by this robustness property, a spline adaptive filtering algorithm based on the maximum correntropy criterion (SAF-MCC) has been proposed. However, the steady-state convergence performance of the SAF-MCC has not been studied. This brief presents a steady-state performance analysis of the SAF-MCC for Gaussian and non-Gaussian noise. We derive and discuss the theoretical excess mean square error (EMSE) under these cases. Monte Carlo simulations are presented to corroborate the theoretical performance analysis results developed in this brief.

[1]  Paulo Sergio Ramirez,et al.  Fundamentals of Adaptive Filtering , 2002 .

[2]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[3]  Danilo Comminiello,et al.  Nonlinear spline adaptive filtering , 2013, Signal Process..

[4]  Danilo Comminiello,et al.  Hammerstein uniform cubic spline adaptive filters: Learning and convergence properties , 2014, Signal Process..

[5]  Nanning Zheng,et al.  Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion , 2014, IEEE Signal Processing Letters.

[6]  Zongze Wu,et al.  Nonlinear spline adaptive filtering under maximum correntropy criterion , 2015, TENCON 2015 - 2015 IEEE Region 10 Conference.

[7]  Nithin V. George,et al.  Nonlinear active noise control using spline adaptive filters , 2015 .

[8]  Danilo Comminiello,et al.  Novel Cascade Spline Architectures for the Identification of Nonlinear Systems , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  Danilo Comminiello,et al.  Nonlinear system identification using IIR Spline Adaptive Filters , 2015, Signal Process..

[10]  Zongze Wu,et al.  Kernel recursive maximum correntropy , 2015, Signal Process..

[11]  Danilo Comminiello,et al.  Steady-State Performance of Spline Adaptive Filters , 2016, IEEE Transactions on Signal Processing.

[12]  Danilo Comminiello,et al.  Diffusion spline adaptive filtering , 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

[13]  Nanning Zheng,et al.  Generalized Correntropy for Robust Adaptive Filtering , 2015, IEEE Transactions on Signal Processing.

[14]  Yikun Yang,et al.  Spline adaptive filter with fractional-order adaptive strategy for nonlinear model identification of magnetostrictive actuator , 2017 .

[15]  Xi Liu,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[16]  Gang Wang,et al.  Projected Kernel Recursive Maximum Correntropy , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[17]  Zhi Zhang,et al.  Set-membership normalised least M-estimate spline adaptive filtering algorithm in impulsive noise , 2018 .

[18]  Zhi Zhang,et al.  Sign normalised spline adaptive filtering algorithms against impulsive noise , 2018, Signal Process..

[19]  Badong Chen,et al.  Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications , 2019, IEEE Transactions on Circuits and Systems II: Express Briefs.

[20]  Shiyuan Wang,et al.  Mixture Complex Correntropy for Adaptive Filter , 2019, IEEE Transactions on Circuits and Systems II: Express Briefs.