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Recently, the Bayesian decorrelation algorithms have gained attention because they can effectively avoid trade-off between learning rate and estimation accuracy by circumventing the problems associated with constant step-size and significantly enhance the convergence speed of adaptive filtering algorithms by decorrelation of signals. However, the present Bayesian decorrelation algorithms are constructed on the basis of the minimum-mean-square-error (MMSE) criterion, leading to deterioration in performance when confronted with non-Gaussian noise environments. Therefore, this paper introduces the maximum correntropy criterion in the decorrelation algorithm and develops a decorrelation recursive maximum correntropy criterion (DRMCC) algorithm based on the observation model to achieve robust performance. The proposed algorithm has a similar iterative equations as the Bayesian decorrelation algorithm. In addition, we present the convergence condition and analyze the transient behavior of the DRMCC algorithm. When the free parameters of the proposed algorithm are unknown, a common method of parameter estimation is provided. Finally, numerical simulations corroborate that the designed DRMCC algorithm can realize outstanding performance under system identification and acoustic echo cancellation (AEC) applications in non-Gaussian noise environments and the derived theoretical model can arrive at good agreement with simulation results for both stationary and nonstationary scenarios. • The decorrelation recursive maximum correntropy criterion algorithm is proposed. • The convergence condition and transient behavior of the DRMCC algorithm are analyzed. • The proposed algorithm has excellent performance in acoustic echo cancellation. [ABSTRACT FROM AUTHOR]

In this paper, an efficient implementation of the Memory Improved Proportionate Affine Projection Algorithm (MIPAPA) is developed, for the adaptive identification of linear in parameters’ systems. The proposed algorithm offers an exact implementation of the MIPAPA scheme, while maintaining the computational burden at an affordable level. The performance of the proposed approach is illustrated by means of computer simulation, in the context of adaptive identification of sparse systems. Moreover, it is shown that the proposed algorithm can be applied for the efficient estimation of the communication channel in the transmission over optical fiber links using intensity modulated direct detection ON/OFF signaling. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to derive norm-constrained adaptive algorithms for sparse system identification. In contrast to other similar approaches found in the literature, the proposed approach is focused primarily on keeping the a posteriori error equal to zero (which is a characteristic of the normalized least-mean-square algorithm) while seeking to satisfy a norm constraint. To this end, the proposed algorithms look directly for a vector belonging to the intersection of a zero-error hyperplane and a hyperplane resulting from a relaxed norm constraint. This somewhat simpler strategy leads to effective sparsity-promoting adaptive algorithms that exhibit low computational complexity and use parameters that are easy to adjust. In this context, a general framework that allows obtaining adaptive algorithms using different norm functions is devised. From this framework, two norm-constrained algorithms based on the ℓ 1 and ℓ 0 norms are obtained. Moreover, enhanced versions of these algorithms are developed aiming to make them independent of user-defined norm-bound parameters. Numerical simulation results corroborate the effectiveness of the proposed framework as well as the very good performance of the obtained algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new regularized kernel-based approach for the estimation of the second order moments of stationary stochastic processes. The proposed estimator is defined by a Tikhonov-type variational problem. It contains few unknown parameters which can be estimated by cross validation solving a sequence of problems whose computational complexity scales linearly with the number of noisy moments (derived from the samples of the process). The correlation functions are assumed to be summable and the hypothesis space is a reproducing kernel Hilbert space induced by the recently introduced stable spline kernel. In this way, information on the decay to zero of the functions to be reconstructed is incorporated in the estimation process. An application to the identification of transfer functions in the case of white noise as input is also presented. Numerical simulations show that the proposed method compares favorably with respect to standard nonparametric estimation algorithms that exploit an oracle-type tuning of the parameters. [Copyright &y& Elsevier]

Abstract: Parameter estimation methods have wide applications in signal processing, communication and system identification. This paper derives an iterative least squares algorithm to estimate the parameters of output error systems and uses the partitioned matrix inversion lemma to implement the proposed algorithm in order to enhance computational efficiencies. The simulation results show that the proposed algorithm works well. [Copyright &y& Elsevier]

Abstract: This paper focuses on identification problems for Hammerstein systems with saturation and dead-zone nonlinearities. An appropriate switching function is introduced to derive an identification model with fewer parameters and all the unknown parameters can be estimated by using an iterative method. A numerical simulation is carried out to show the effectiveness of the proposed method. [Copyright &y& Elsevier]