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 …

Most distributed Kalman filters are based on the cost function of the well-known minimum
mean square estimation criterion, which performs well in the presence of Gaussian noise …

This work revisits the problem of distributed adaptive filtering in multi-agent sensor networks.
In contrast to classical approaches, the formulation relaxes the Gaussian assumption on the …

Recently, robust adaptive filtering algorithms have attracted the interest of researchers and
have been extensively studied. However, most of these methods suffer from the non …

This paper presents weighted diffusion least mean p-power (LMP) algorithm for distributed
estimation of an unknown sparse vector in a sensor network. We consider a network, in …

Wireless sensor network (WSN) is an important part of the Internet of Things (IoT) and has
emerged in various new forms, such as smart home, smart city, and intelligent manufacturing …

The present study proposes the Robust DLMS (RDLMS) algorithm for a robust estimation
over adaptive networks. Instead of minimizing the mean square error (MSE), the RDLMS …

Among the most important approaches in the study of DOA estimation, ESPRIT-like methods
have received considerable attentions and have been widely applied in practical …

In an increasing number of modern filtering applications, the encountered signals consist of
frequent sharp spikes, that cannot be accurately modeled using Gaussian random …

This brief proposes a correntropy-based proportionate diffusion algorithm with low
computational complexity. The proportionate diffusion algorithms in the literature suffer from …