In this paper, three Lorentzian based robust adaptive algorithms are proposed for identifying
systems in presence of impulsive noise. The first algorithm called Lorentzian adaptive …

In this brief, an affine-projection-like M-estimate (APLM) algorithm is proposed for robust
adaptive filtering. To eliminate the adverse effects of impulsive noise in case of the impulse …

As a widely used method in the errors-in-variables (EIV) model, total least square (TLS) can
work well for both input and output signals disturbed with noises. The TLS based adaptive …

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 …

As classical adaptive filtering algorithms, least mean-norm (LMP) and its variants have good
convergence performance in impulsive noise obeying-stable distribution with the …

An improved proportionate adaptive filter based on maximum correntropy criterion (IP-MCC)
is proposed for identifying systems with variable sparsity in an impulsive noise environment …

Constrained adaptive filtering algorithms have been extensively studied in many
applications. Most existing constrained adaptive filtering algorithms are developed under the …

This paper proposes a prefiltered observation-based adaptive filter algorithm that is robust
against impulsive noise. Previous impulsive noise rejection algorithms were based on …

This paper proposes two gradient-based adaptive algorithms, called the least mean M
estimate and the transform domain least mean M-estimate (TLMM) algorithms, for robust …

A new robust recursive least-squares (RLS) adaptive filtering algorithm that uses a priori
error-dependent weights is proposed. Robustness against impulsive noise is achieved by …