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 …

Although adaptive filtering and adaptive array processing began with research and
development efforts in the late 1950's and early 1960's, it was not until the publication of the …

This paper studies a class of algorithms called natural gradient (NG) algorithms. The least
mean square (LMS) algorithm is derived within the NG framework, and a family of LMS …

We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with
data-selection mechanisms. Sparsity is promoted via some well-known nonconvex …

The field of Digital Signal Processing has developed so fast in the last five decades that it
can be found in the graduate and undergraduate programs of most universities. This …

The goal of this book is to provide, for the first time, a reference to the most relevant
applications of adaptive filtering techniques. Top researchers in the field contributed …

The authors propose a new approach for the adaptive identification of sparse systems. This
approach improves on the recursive least squares (RLS) algorithm by adding a sparsity …

This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based
on an alternating optimization strategy with shrinkage. The proposed scheme employs a two …

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 …

In this paper, a new framework of cost function for designing robust adaptive filtering
algorithms is developed. This new cost framework, called sigmoid cost function, results from …