A robust statistics based adaptive lattice-ladder filter in impulsive noise

In this paper, a new robust adaptive lattice-ladder filter for impulsive noise suppression is proposed. The filter is obtained by applying the non-linear filtering technique in [l] and the robust statistic approach to the gradient adaptive lattice filter. A systematic method is also developed to determine the corresponding threshold parameters for impulse suppression. Simulation results showed that the performance of the proposed algorithm is better than the conventional RLS, N-RLS, the gradient adaptive lattice normalised-LMS (GAL-NLMS), RMN and ATNA algorithms when the input and desired signals are corrupted by individual and consecutive impulses. The initial convergence, steady-state error, computational complexity and tracking capability of the proposed algorithm are also comparable to the conventional GAL-NLMS algorithm.published_or_final_versio

Recursive Least M-estimate (RLM) adaptive filter for robust filtering in impulse noise

This paper proposes a recursive least M-estimate (RLM) algorithm for robust adaptive filtering in impulse noise. It employs an M-estimate cost function, which is able to suppress the effect of impulses on the filter weights. Simulation results showed that the RLM algorithm performs better than the conventional RLS, NRLS, and OSFKF algorithms when the desired and input signals are corrupted by impulses. Its initial convergence, steady-state error, computational complexity, and robustness to sudden system change are comparable to the conventional RLS algorithm in the presence of Gaussian noise alone.published_or_final_versio

Robust recursive bi-iteration singular value decomposition (SVD) for subspace tracking and adaptive filtering

IEEE International Symposium on Circuits and Systems Proceedings, Bangkok, Thailand, 25-28 May 2003The recursive bi-iteration singular value decomposition (Bi-SVD), proposed by Strobach, is en efficient and well-structured algorithm for performing subspace tracking. Unfortunately, its performance under impulse noise environment degrades substantially. In this paper, a new robust-statistics-based bi-iteration SVD algorithm (robust Bi-SVD) is proposed. Simulation results show that the proposed algorithm offers significantly improved robustness against impulse noise than the conventional Bi-SVD algorithm with slight increase in arithmetic complexity. For nominal Gaussian noise, the two algorithms have similar performance.published_or_final_versio

A Huber recursive least squares adaptive lattice filter for impulse noise suppression

This paper proposes a new adaptive filtering algorithm called the Huber Prior Error-Feedback Least Squares Lattice (H-PEF-LSL) algorithm for robust adaptive filtering in impulse noise environment. It minimizes a modified Huber M-estimator based cost function, instead of the least squares cost function. In addition, the simple modified Huber M-estimate cost function also allows us to perform the time and order recursive updates in the conventional PEF-LSL algorithm so that the complexity can be significantly reduced to O(M), where M is the length of the adaptive filter. The new algorithm can also be viewed as an efficient implementation of the recursive least M-estimate (RLM) algorithm recently proposed by the authors [1], which has a complexity of O(M 2). Simulation results show that the proposed H-PEF-LSL algorithm is more robust than the conventional PEF-LSL algorithm in suppressing the adverse influence of the impulses at the input and desired signals with small additional computational cost.published_or_final_versio

A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm1 is derived. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.published_or_final_versio