System identification problems are always challenging to address in applications that
involve long impulse responses, especially in the framework of multichannel systems. In this …

Adaptive beamforming using sparse arrays can alleviate system burden with a sub-Nyquist
sampling rate while achieving high resolution. To process multi-dimensional signals without …

The recursive least-squares (RLS) adaptive filter is an appealing choice in many system
identification problems. The main reason behind its popularity is its fast convergence rate …

Tensor-based signal processing methods are usually employed when dealing with
multidimensional data and/or systems with a large parameter space. In this paper, we …

Abstract System identification problems are very difficult in the scenario of long length
impulse responses, raising challenges in terms of convergence, complexity, and accuracy of …

In applications involving system identification problems, some characteristics of the impulse
response of the system to be identified are usually exploited to design adaptive algorithms …

A recently developed third-order tensor (TOT) decomposition-based method has proved to
be working very well in linear system identification problems that target the estimation of …

In linear system identification problems, it is important to reveal and exploit any specific
intrinsic characteristic of the impulse responses, in order to improve the overall performance …

Modern solutions for system identification problems employ multilinear forms, which are
based on multiple-order tensor decomposition (of rank one). Recently, such a solution was …

This work focuses on linear system identification problems in the framework of the Wiener
filter. Specifically, it addresses the challenging identification of systems characterized by …