Search

The normalized subband adaptive filtering algorithm provides fast convergence rate for colored input signals as compared to the normalized least mean square algorithm, but it suffers from a poor convergence issue in the α -stable noise. In light of this, the normalized subband p-norm (NSPN) algorithm, which is based on the least mean p-power error (MPE) criterion, is proposed in this study. This technique is not only robust against impulsive noise samples, but it also maintains a fast convergence rate when colored input signals are used. In addition to this, we develop both the steady-state and the transient models of the NSPN algorithm and provide some insights. Then, in order to solve the problem of making a choice regarding the order p in the NSPN algorithm, we design an autonomous system and come up with the NSPN algorithm with a variable p-norm (VP-NSPN). In addition, we offer the TFC-based VP-NSPN algorithm with a fast convergence rate and low steady-state misadjustment simultaneously by making use of the tap-weights feedback-based convex combination (TFC) scheme. In conclusion, simulation results on system identification and acoustic echo cancellation are undertaken in order to validate the superiority of the proposed algorithms and validate the usefulness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This article develops a subband Hammerstein spline adaptive filtering (SHSAF) algorithm with the exponential hyperbolic cosine (EHC) cost to acquire robustness against non-Gaussian noises, which is called the SHSAF-EHC algorithm. The delayless multiband-structured subband strategy is employed to enhance the convergence property in the Hammerstein-type nonlinear filtering structure. In addition, the statistical performance analysis of SHSAF-EHC is rigorously derived in the mean and mean-square senses. Numerical experiments under non-Gaussian noise scenarios corroborate the accuracy of theoretical findings and validate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]