Phasor estimation under dynamic conditions has been under study recently by relaxing the
amplitude and phase of the static phasor. This paper will review some methods to estimate …

This paper presents a noise adaptive variational Bayesian cubature information filter based
on Wishart distribution. In the frame of recursive Bayesian estimation, the noise adaptive …

Nonlinear state estimation plays a major role in many real-life applications. Recently, some
sigma-point filters, such as the unscented Kalman filter, the particle filter, or the cubature …

Online robust sensor fault detection is a challenging problem in control engineering
systems. In this paper, a new method is proposed to design a Nonlinear Unknown Input …

In this paper, the high-dimensional distributed state estimation problem is investigated for a
class of sensor networks within the cubature Kalman filtering (CKF) framework. The network …

In this paper, the distributed state estimation (DSE) problem for a class of discrete-time
nonlinear systems over sensor networks is investigated. First, based on weighted average …

Nonlinear Estimation: Methods and Applications with Deterministic Sample Points focusses
on a comprehensive treatment of deterministic sample point filters (also called Gaussian …

The traditional consensus-based filters are widely used in distributed sensor networks.
However, they suffer from divergence when outliers occur. This paper proposes a robust …

This paper analyzes stochastic stability and performance of discrete-time Cubature Kalman
Filtering (CKF). The main contribution is (1) Boundedness analysis based on the constructor …

Data fusion for nonlinear systems is one of the challenging topics in state estimation and
target tracking recently. We study decentralized cubature Kalman fusion in this paper …