Non-Gaussian randomness widely exists in complex dynamical systems, in which the
traditional mean-variance index cannot fully reflect the systematic characteristics. To …

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 …

In recent years, correntropy-based algorithms which include maximum correntropy criterion
(MCC), generalized MCC (GMCC), kernel MCC (KMCC) and hyperbolic cosine function …

Kalman filters (KFs) are widely used for state-of-charge (SOC) estimation of Li-ion batteries
due to their excellent dynamic tracking capability. Especially the cubature KF (CKF), with the …

In the past years, the generalized maximum correntropy criterion (GMCC) has been widely
used in adaptive filters to provide robust behavior under non-Gaussian/impulsive noise …

With the development of wireless sensor networks, many distributed algorithms have been
studied by researchers. This paper considers the situation of distributed estimation with false …

This brief introduces a novel cost function framework for develo** robust algorithms for
adaptive filtering by embedding the standard cost function into the arctangent framework …

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 …

Robust adaptive filters utilizing hyperbolic cosine and correntropy functions have been
successfully employed in non-Gaussian noisy environments. However, these filters suffer …

In adaptive networks, the performance of distributed estimation is degraded in the presence
of non-Gaussian noises. A number of robust criteria for diffusion approaches, such as …