An adaptive penalized maximum likelihood algorithm
Signal Processing
Journal of Signal Processing Systems
Journal of Signal Processing Systems
IEEE Transactions on Signal Processing
A fast robust recursive Least-Squares algorithm
IEEE Transactions on Signal Processing
On error-saturation nonlinearities in NLMS adaptation
IEEE Transactions on Signal Processing
Journal of Signal Processing Systems
Asymptotic mean and variance of Gini correlation for bivariate normal samples
IEEE Transactions on Signal Processing
New polynomial approach to myriad filter computation
Signal Processing
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Journal of Signal Processing Systems
Impulsive noise cancelation with simplified Cauchy-based p-norm filter
Signal Processing
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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) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. 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.