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In order to solve the problem of deteriorating performance of the conventional subband adaptive filtering algorithm when processing the EIV model with impulsive noise, this paper proposes the robust Total Least Mean M-Estimate normalized subband filter (TLMM-NSAF) adaptive algorithm based on the M-estimation function. In addition, we conduct a detailed theoretical performance analysis of the TLMM-NSAF algorithm, which allows us to determine the stable step size range and theoretical steady-state mean squared deviation of the algorithm. To further improve the algorithm's performance, we propose a new variable step size method. Finally, we compared the algorithm with other competition algorithms in applications of system identification and acoustic echo cancellation. Simulation results have demonstrated the superiority of our proposed algorithm, as well as the consistency between the theoretical values and the simulated values. [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]

• The TLS-APL algorithm is proposed by using the gradient descent method and the unconstrained optimization method, which solves the problem of overall performance degradation of the APL algorithm in the EIV model. • By using some common assumptions, the local extremum of the TLS-APL algorithm is analyzed. Therefore, the expected parameter is the optimal value of the iterative estimation and fully demonstrates the correctness of the iterative update. • The mean, mean square, and steady-state MSD performance of the TLS-APL algorithm is derived by using the Kronecker product and vec operator respectively. In addition, the fitting effects of theoretical steady-state MSD curves and actual steady-state MSD curves under different parameters are analyzed, which proves the rationality of theoretical derivation. • The VSS method is presented from the maximum decrease of MSD from the current iteration to the next iteration. Compared to the TLS-APL algorithm with a fixed step size, the VSS-TLS-APL algorithm with the VSS method has both fast convergence speed and low steady-state error. In the errors-in-variables (EIV) model, the steady state characteristics of the affine-projection-like (APL) algorithm are poor. Hence, the total least squares APL (TLS-APL) algorithm different from the bias-compensated is proposed, which can reduce the adverse influence of input and output noise on weight update. Then, the local extremum of the TLS-APL algorithm is verified, that is, the expected parameter is the optimal value of the iterative estimation. Moreover, the mean, mean square, and steady-state mean square deviation (MSD) performance of the proposed TLS-APL algorithm are deduced by using the Kronecker product and vec operator, respectively. To balance the convergence characteristics and steady-state characteristics, the step size optimization of the TLS-APL algorithm, namely the variable step size TLS-APL (VSS-TLS-APL) algorithm, is derived from the analysis of MSD. Considering the existence of gradient errors, the fitting effects of theoretical steady-state MSD curves and actual steady-state MSD curves under different parameters are analyzed separately, which proves the rationality of theoretical derivation. In addition, the VSS-TLS-APL algorithm with optimized step size is compared with the TLS-APL algorithm with fixed step size in the application of system identification, and the effect of the variable step size (VSS) is verified. The experiments of system identification and acoustic echo cancelation prove that the VSS-TLS-APL algorithm is superior to the existing known algorithms. [ABSTRACT FROM AUTHOR]

A bias-compensated normalized subband adaptive filter (BC-NSAF) algorithm is proposed for system identification. In the proposed algorithm, a bias-compensation vector is derived to eliminate the bias caused by the noisy input signals. To estimate the input noise variance, a new estimation method is proposed, which does not require the input–output variance ratio in advance. Simulation results show that the proposed algorithm obtains better convergence performance than the existing algorithms in the presence of noisy input signals. [ABSTRACT FROM PUBLISHER]

To improve the performance and reduce the computational complexity, a novel sign adaptation scheme for convex combination of adaptive filters is proposed. By using a sign adaptation scheme, the proposed scheme slightly reduces the computational complexity of the basic combination of mixing parameter, and improves the robustness of mixing parameter. Moreover, to obtain fast convergence property during the period of convergence transition in different environments, the instantaneous transfer scheme is applied to the proposed algorithm. The instantaneous transfer scheme has lower computational complexity than that of the basic combination of least mean square (basic-CLMS) algorithms. Simulation results in the context of system identification under time-invariant and time-variant systems demonstrate that the proposed algorithm based on the instantaneous transfer scheme has lower computational complexity and faster convergence rate than that of the basic-CLMS algorithm during period of convergence transitions. [ABSTRACT FROM AUTHOR]

• This paper summarizes several variable kernel width maximum correntropy criterion (MCC) algorithms, and discusses the basic principles of these algorithms. A close relationship between this algorithms and LMS algorithm is analyzed and established. • Then a new statistics variable kernel width MCC algorithm is proposed (SVKW-MCC) on the basis of previous variable kernel width algorithms. • SVKW-MCC algorithm is proposed to address the shortcomings of some well-known variable kernel width algorithm. The SVKW-MCC algorithm use statistics method to compute the kernel width and eliminates the abnormal errors caused by impulsive noise by statistical method. • The stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. Since the maximum correntropy criterion (MCC) algorithm with a constant kernel width leads to the trade-off problem between the convergence rate and steady-state misalignment, various adaptive kernel width MCC algorithms were derived to solve this problem. However, the superior performances of these algorithms depend mainly on specific data range, or have complicated calculation and parameter setting. Thus, this paper proposes a statistics variable kernel width MCC (SVKW-MCC) algorithm to overcome these problems. Specifically, the proposed algorithm calculates the mean and variances of the errors signal, and then the proposed algorithm removes these data that significantly deviate from the mean value of errors signal, moreover, the new mean and variance are recalculated after removing these abnormal data, subsequently, the new kernel width is calculated by the new variance and mean. Simulation results in system identification and echo cancellation scenarios show that the proposed algorithm outperforms the existing variable kernel width methods. Moreover, the stability and steady-state mean-square performance of the proposed algorithm is analyzed and verified by experiments. More importantly, the new method involves no extra free parameters and does not depend on the specific application data range, so the proposed algorithm has a very good application prospect. [ABSTRACT FROM AUTHOR]