A Robust Affine Projection Algorithm Against Impulsive Noise

This letter proposes a prefiltered observation-based affine projection algorithm (APA) to achieve robustness against outliers. The conventional robust algorithm for correlated input signal, which is called the affine projection sign algorithm (APSA), was developed by using the $\mathcal {L}_1$-norm of the error signal. However, it suffers from slow tracking speed for a system change suddenly, because it can not distinguish between the time-varying system and occurrence of the impulsive noise. To overcome this problem, the proposed algorithm is induced from the matrix inequalities in no impulsive interference. The proposed algorithm determines the necessity for updating the weight vector in the noise. Hence, it has robustness and tracking ability because it discriminates between the time-varying system and occurrence of the impulsive noise. Simulations in a system identification scenario show that the proposed algorithm surpasses the APA and APSA in terms of convergence rate and tracking performance under the impulsive noise environment.

[1]  Sheng Zhang,et al.  Combined-Step-Size Affine Projection Sign Algorithm for Robust Adaptive Filtering in Impulsive Interference Environments , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.

[2]  Jae Jin Jeong,et al.  Robust Adaptive Filter Algorithms Against Impulsive Noise , 2019, Circuits, Systems, and Signal Processing.

[3]  Seung Hun Kim,et al.  Robust convex combination of affine projection-type algorithms using an impulsive noise indicator , 2016, Signal Process..

[4]  Ying-Ren Chien,et al.  Variable Regularization Affine Projection Sign Algorithm in Impulsive Noisy Environment , 2019, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[5]  J. Chambers,et al.  A robust mixed-norm adaptive filter algorithm , 1997, IEEE Signal Processing Letters.

[6]  Chang Hee Lee,et al.  Scheduled-Step-Size Affine Projection Algorithm , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  E. Erzin,et al.  Adaptive filtering for non-Gaussian stable processes , 1994, IEEE Signal Processing Letters.

[8]  Jacob Benesty,et al.  A robust variable step-size affine projection algorithm , 2010, Signal Process..

[9]  Jacob Benesty,et al.  A New Robust Variable Step-Size NLMS Algorithm , 2008, IEEE Transactions on Signal Processing.

[10]  Ying-Ren Chien,et al.  Convex Combined Adaptive Filtering Algorithm for Acoustic Echo Cancellation in Hostile Environments , 2018, IEEE Access.

[11]  Suleyman Serdar Kozat,et al.  A Novel Family of Adaptive Filtering Algorithms Based on the Logarithmic Cost , 2013, IEEE Transactions on Signal Processing.

[12]  K. Loparo,et al.  Inequalities for the trace of matrix product , 1994, IEEE Trans. Autom. Control..

[13]  T. Ng,et al.  A recursive least M-estimate (RLM) adaptive filter for robust filtering in impulse noise , 2000, IEEE Signal Processing Letters.

[14]  PooGyeon Park,et al.  A variable step-size affine projection algorithm with a step-size scaler against impulsive measurement noise , 2014, Signal Process..

[15]  Sang Woo Kim,et al.  Consistent normalized least mean square filtering with noisy data matrix , 2005, IEEE Transactions on Signal Processing.

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

[17]  Jacob Benesty,et al.  A Nonparametric VSS NLMS Algorithm , 2006, IEEE Signal Processing Letters.