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This study proposes a spline filtering algorithm-based generalized maximum correntropy criterion (GMCC), named the spline adaptive filter (SAF-)-GMCC algorithm. Compared with traditional spline algorithms, the SAF-GMCC can cope with impulsive interference effectively, because the GMCC has a low sensitivity to mutation signals. The GMCC-based variable step-size spline filtering algorithm (SAF-GMCC) is proposed to solve the limitation of the fixed step-size on the SAF-GMCC algorithm's performance and to improve the convergence rate and steady-state error performance. Combining these algorithms with the active noise control (ANC) model, this study proposes the filtered-c generalized maximum correntropy criterion (FcGMCC) and variable step-size filtered-c generalized maximum correntropy criterion (FcVGMCC) algorithms. Finally, the nonlinear system identification model simulates an experimental environment with impulsive interference. The SAF-GMCC and SAF-VGMCC algorithms offer better robustness than the existing algorithms. And the alpha-stable noise environment simulation with different impact strengths, in the ANC model verifies the FcGMCC and FcVGMCC algorithms' robustness in nonlinear and non-Gaussian noise environments. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

In this paper, a generalized maximum correntropy normalized subband adaptive filter (GMCNSAF) algorithm is proposed for enhancing the convergence behavior of generalized maximum correntropy criterion algorithm under correlated signals. To promote the convergence behavior of GMCNSAF when identifying the sparse system, the polynomial zero attraction constraint is incorporated into it to yield the PZAGMCNSAF algorithm. Furthermore, to alleviate the conflicting requirements with regard to convergence rate and steady-state misalignment, a variable step-size scheme is applied into the PZAGMCNSAF, obtaining the VSSPZAGMCNSAF algorithm. Also, we analyze the convergence condition of GMCNSAF and give the step-size bound. Simulations prove that the proposed algorithms obtain superior performance for sparse system identification and echo cancelation scenario under impulsive interference as compared to related algorithms. [ABSTRACT FROM AUTHOR]

When the distributed nodes are contaminated by the different types of noises, including Gaussian and generalized Gaussian distribution noises, the diffusion distributed least mean square algorithm suffers severely from performance degradation. To address this problem, this paper proposes background and impulsive noise indicators to identify the background noise non-Gaussianity and detect the impulsive noise, respectively. Then, according to the results of noise indicators, different distributed algorithms are selected for distributed in-network identification where different nodes share different noise types. The proposed algorithm is called distributed conditional selection algorithm, which is superior to the existing diffusion-based algorithms due to its combining the advantages of the related algorithms. Simulations with non-impulsive and impulsive interference confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies. [ABSTRACT FROM AUTHOR]

This paper proposes a bias-compensated normalized least-mean fourth (NLMF) algorithm to compensate for the bias caused by noisy input. In the proposed algorithm, we add a bias-compensation vector to the conventional NLMF algorithm, and the bias-compensation vector is then derived based on the unbiasedness criterion. Simulation results in system identification context show that the proposed algorithm provides lower steady-state misalignment as compared to the conventional NLMF algorithm. [ABSTRACT FROM AUTHOR]