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 …

As a robust nonlinear similarity measure in kernel space, correntropy has received
increasing attention in domains of machine learning and signal processing. In particular, the …

Distributed estimation strategies over wireless sensor networks are one of the active areas
of research due to the wide range of applications in a variety of fields ranging from …

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract
higher order statistics of data and offer potentially significant performance improvement over …

In this paper, a least lncosh (Llncosh) algorithm is derived by utilizing the lncosh cost
function. The lncosh cost is characterized by the natural logarithm of hyperbolic cosine …

A new framework for designing robust adaptive filters is introduced. It is based on the
optimization of a certain cost function subject to a time-dependent constraint on the norm of …

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 …

An adaptive least mean M-estimate algorithm for censored regression (CR-LMM) is
presented for the robust parameter estimation of the censored regression system. To correct …

Air pollution can lead to a wide range of hazards and can affect most organisms on Earth.
Therefore, managing and controlling air pollution has become a top priority for many …

This work proposes diffusion normalized least mean M-estimate algorithm based on the
modified Huber function, which can equip distributed networks with robust learning …