Sign-Normalized IIR Spline Adaptive Filtering Algorithms for Impulsive Noise Environments
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In this paper, a new sign-normalized least-mean-square adaptive filtering algorithm based on IIR spline adaptive filter (IIR-SAF-SNLMS) is proposed. By using the absolute value of the a posteriori error as the cost function and solving the optimization problem, the proposed algorithm achieves robustness against impulsive noise. Furthermore, to further improve the performance of the IIR-SAF-SNLMS, its variable step-size variant is proposed. Simulation results in the identification of the IIR-SAF nonlinear model show that the proposed algorithms provide better tracking and steady-state performance as compared to the existing spline algorithms.
KeywordsIIR spline adaptive filter Sign-adaptive algorithm Variable step-size
This research was supported by the National Natural Science Foundation of China under Grant 61501119.