Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked
systems enable the employment of multiple sensors. Distributed estimation provides a …

Technological advances in ad hoc networking and the availability of low-cost reliable
computing, data storage, and sensing devices have made scenarios possible where the …

This article is concerned with the distributed recursive filtering issue for stochastic discrete
time-varying systems subjected to both state saturations and round-robin (RR) protocols …

In this paper, we investigate the state estimation problem of nonlinear systems with non-
Gaussian measurement noise. Based on a newly defined cost function which is obtained by …

This paper reviews forty years of distributed estimation research since the first papers on
decentralized filtering appeared in 1978. Starting with a formulation of the problem, it …

In this paper, a distributed diffusion unscented Kalman filtering algorithm based on
covariance intersection strategy (DDUKF-CI) is proposed for target tracking with intermittent …

We propose a new robust recursive fixed-interval smoother for nonlinear systems under non-
Gaussian process and measurement noises, ie, the nominal Gaussian noise is polluted by …

The maximum correntropy unscented Kalman filter (MCUKF) is a newly proposed non-linear
state estimation algorithm that is robust to non-Gaussian noise. In this paper, we propose …

A Kalman-like recursive distributed optimal linear fusion predictor (RDOLFP) without
feedback in the linear unbiased minimum variance sense is presented for multi-sensor …

In this paper, we consider the dynamic average consensus problem for a group of multiple
agents that cooperate to estimate the average of locally available time-varying reference …