An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo
cancellation in vehicle hands-free communication systems and video teleconferencing …

The Hilbert space embedding provides a powerful and flexible tool for dealing with the
nonlinearity and high-order statistics of random variables in a dynamical system. The kernel …

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces.
We consider both centralized and fully distributed implementations. We first define nonlinear …

Thanks to the ability of preventing the accumulation of errors, constrained adaptive filtering
(CAF) algorithms have been widely applied. However, in practice, non-Gaussian noise may …

As a nonlinear similarity measure defined in the kernel space, the correntropic loss (C-Loss)
can address the stability issues of second-order similarity measures thanks to its ability to …

To further improve the performance of the variable step size continuous mixed p-norm (VSS-
CMPN) adaptive filtering algorithm in the presence of impulsive noise, a generalized VSS …

In this paper, a new cooperative localization (CL) method for multiple autonomous
underwater vehicles (AUVs) is proposed to address the problem of measurement outliers …

The Cauchy loss (CL) is a high-order loss function which has been successfully used to
overcome large outliers in kernel adaptive filters. The squared error in the CL is then …

Electricity consumption forecasting (ECF) plays a crucial role in the new open electricity
market nowadays, and in order to pass the ECF test, the ECF error is required to be less …