DCD-Based Recursive Adaptive Algorithms Robust Against Impulsive Noise

The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this brief, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formula. By employing different robust strategies against impulsive noise, we develop novel computationally efficient DCD-based robust recursive algorithms. Furthermore, to equip the proposed algorithms with the ability to track abrupt changes in unknown systems, a simple variable forgetting factor mechanism is also developed. Simulation results for channel identification scenarios in impulsive noise demonstrate the effectiveness of the proposed algorithms.

[1]  V. J. Mathews,et al.  Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm , 1987, IEEE Trans. Acoust. Speech Signal Process..

[2]  Dong-Jo Park,et al.  Fast tracking RLS algorithm using novel variable forgetting factor with unity zone , 1991 .

[3]  C. L. Nikias,et al.  Signal processing with alpha-stable distributions and applications , 1995 .

[4]  Paul Petrus,et al.  Robust Huber adaptive filter , 1999, IEEE Trans. Signal Process..

[5]  S. C. Chan,et al.  Robust M-estimate adaptive filtering , 2001 .

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

[7]  K. Dostert,et al.  Analysis and modeling of impulsive noise in broad-band powerline communications , 2002 .

[8]  Shing-Chow Chan,et al.  A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis , 2004, IEEE Transactions on Signal Processing.

[9]  Yuriy V. Zakharov,et al.  Low-Complexity Implementation of the Affine Projection Algorithm , 2008, IEEE Signal Processing Letters.

[10]  Jie Liu,et al.  Promoting Access to White Rose Research Papers Low-complexity Rls Algorithms Using Dichotomous Coordinate Descent Iterations , 2022 .

[11]  Jacob Benesty,et al.  A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification , 2008, IEEE Signal Processing Letters.

[12]  Jerónimo Arenas-García,et al.  Combination of Recursive Least $p$-Norm Algorithms for Robust Adaptive Filtering in Alpha-Stable Noise , 2012, IEEE Transactions on Signal Processing.

[13]  Yunlong Cai,et al.  Low-Complexity Variable Forgetting Factor Mechanism for Blind Adaptive Constrained Constant Modulus Algorithms , 2012, IEEE Transactions on Signal Processing.

[14]  Sheng Zhang,et al.  Enhancing the tracking capability of recursive least p-norm algorithm via adaptive gain factor , 2014, Digit. Signal Process..

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

[16]  Hadi Zayyani,et al.  Continuous Mixed $p$-Norm Adaptive Algorithm for System Identification , 2014, IEEE Signal Processing Letters.

[17]  Nanning Zheng,et al.  Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion , 2015, IEEE Signal Processing Letters.

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

[19]  Stefan Aachen,et al.  Signal Detection In Non Gaussian Noise , 2016 .

[20]  Junwoong Hur,et al.  A Variable Step-Size Normalized Subband Adaptive Filter With a Step-Size Scaler Against Impulsive Measurement Noise , 2017, IEEE Transactions on Circuits and Systems II: Express Briefs.

[21]  Yuriy V. Zakharov,et al.  Low-Complexity DCD-Based Sparse Recovery Algorithms , 2017, IEEE Access.

[22]  Mojtaba Hajiabadi,et al.  Recursive Maximum Correntropy Learning Algorithm With Adaptive Kernel Size , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[23]  Haiquan Zhao,et al.  Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks , 2018, IEEE Transactions on Signal Processing.

[24]  Lu Lu,et al.  Recursive Geman–McClure Estimator for Implementing Second-Order Volterra Filter , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[25]  Haiquan Zhao,et al.  An Improved Variable Kernel Width for Maximum Correntropy Criterion Algorithm , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.