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Protocol‐based extended Kalman filtering with quantization effects: The Round‐Robin case
Author(s) -
Liu Shuai,
Wang Zidong,
Hu Jun,
Wei Guoliang
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5205
Subject(s) - control theory (sociology) , quantization (signal processing) , kalman filter , mathematics , filtering problem , computer science , extended kalman filter , nonlinear system , algorithm , mathematical optimization , statistics , artificial intelligence , control (management) , physics , quantum mechanics
Summary This article investigates the extended Kalman filtering problem for a class of stochastic nonlinear systems with quantization effects and Round‐Robin (RR) communication protocols. The uniform quantization is considered and the resulting quantization error is characterized as an additive white noise sequence obeying the uniform distribution over certain intervals. For the sake of reducing communication traffic of the network as well as alleviating data collisions, the RR mechanism is introduced to schedule the data transmission from the sensors to the filter. By combining the periodic property of the RR protocol and the zero‐order holder strategy, the input signal of the filter is modeled by a sequence of delayed quantized measurements. The main purpose of this article is to design an extended Kalman filter for the stochastic nonlinear systems, in the simultaneous presence of quantization errors, stochastic nonlinearities, and stochastic noises, such that an optimized upper bound for the filtering error covariance is derived. By solving two coupled Riccati‐like difference equations, the filter gain matrix is explicitly formulated. An RR protocol‐based recursive filtering algorithm is developed for the online implementation. Furthermore, a sufficient condition is established to ensure the uniform boundedness of the filtering error in the mean‐square sense. Finally, a simulation example is given to demonstrate the practical validity of the designed filter algorithm.