This brief introduces the concept of a step-size scaler by investigating and modifying the
tanh cost function for adaptive filtering with impulsive measurement noise. The step-size …

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 …

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 …

A bias‐compensated error‐modified normalised least‐mean‐square algorithm is proposed.
The proposed algorithm employs nonlinearity to improve robustness against impulsive …

The popular least-mean-squares (LMS) algorithm for adaptive filtering is nonrobust against
impulsive noise in the measurements. The presence of this type of noise degrades the …

The conventional step-size scaler (SSS) normalized least-mean-square algorithm is robust
against impulsive noise. However, the constant parameter in the SSS needs to be controlled …

In this paper, a normalized least mean square (NLMS) adaptive filtering algorithm based on
the arctangent cost function that improves the robustness against impulsive interference is …

In almost all analyzes of the least mean-square (LMS) finite impulse response (FIR) adaptive
algorithm, it is assumed that the length of the adaptive filter is equal to that of the unknown …

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 …

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 …