This article is concerned with the consensus‐based distributed filtering problem for a class of general nonlinear systems over sensor networks with communication protocols. In order to avoid data collisions, the stochastic protocol and the Round‐Robin protocol are respectively introduced to schedule the data transmission between each node and its neighboring ones. A consensus‐based unscented Kalman filtering (UKF) algorithm is developed for the purpose of estimating the system states over sensor networks subject to communication protocols. Moreover, the exponential boundedness of estimation error in mean square is proved for the proposed algorithm. Finally, compared with the extended Kalman filtering, an experimental simulation example is provided to validate the effectiveness of the consensus‐based UKF algorithm.

In this paper, the filtering problem is investigated for nonlinear systems with multiple sensors. A federated strong tracking filter is designed to track state mutations by making full use of limited sensor information. Several subsystems are composed of different sensor combinations, and their states are independently estimated by using local filters in parallel. The strong tracking filter, as local filters, adaptively adjusts gain matrices of filters by introducing fading factors to track state mutations timely. Based on the theory of boundedness and inequality technique, the fusion estimation error is proved to be exponentially bounded in mean square. Finally, the feasibility and effectiveness of the proposed method is demonstrated by an experiment concerning the rotary steering drilling tool system.

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