Distributed cubature Kalman filtering for nonlinear systems with stochastic communication protocol

Abstract

This paper is concerned with the distributed cubature Kalman filtering (DCKF) for a class of discrete time-varying nonlinear systems subject to stochastic communication protocol (SCP). In order to avoid the data collisions, the SCP is introduced to randomly schedule each sensor node information from one of the neighboring nodes to the filter which is presented via not only the information of itself sensor but also the information of neighboring sensors. The considered scheduling probability of the selected node is unknown. Therefore, the exact filtering error covariance is not available. The purpose of this paper is to design a DCKF under the spherical-radial cubature rule such that an upper bound of the filtering error covariance is guaranteed by utilizing fundamental inequality. Such an upper bound is dependent on known upper and lower bounds of the scheduling probabilities. Subsequently, the desired filter matrices are given by minimizing the upper bound of the filtering error. A sufficient condition is derived by using Riccati-like difference equations method. Besides, a recursive form of filter algorithm is designed for online computation. Finally, the usefulness of the DCKF is verified by utilizing a simulation example on the induction machines.

State estimation for a class of nonlinear non‐Gaussian cyber–physical systems under false data injection attacks

“…Recently, cyber-physical systems (CPSs) have witnessed an increasing application in many fields, such as smart cities, smart grids, smart transportation, and so on [1,2]. At the same time, the state estimation of CPSs by filtering methods has also been a hot issue, for example, the Kalman filter (KF) [3,4]. The KFs are initially proven optimal under Gaussian and linear assumptions.…”

On state estimation for nonlinear systems under random access wireless protocols

“…Stochastic protocols have been widely studied in the context of controller design of linear WNCS in [24][25][26][27][28], in [29] for a class of non-linear WNCS, and our previous works [21,23] for general non-linear WNCS. Recently, stochastic protocols have also been studied for the remote state estimation problem, see e.g., [30][31][32] for results on linear systems, and [33][34][35][36][37] for results on special classes of non-linear systems. Particularly, all these works focus on discretetime models (either linear or a special class of non-linear) for the underlying WNCS, and thus consider periodic and deterministic transmissions for the network.…”

“…Note that all available results are either for linear systems or specific classes of non-linear systems [30][31][32][33][34][35][36][37]. Moreover, we adopt a stochastic hybrid systems approach, as opposed to purely discrete like in the aforementioned works.…”