Probabilistic-constrained filtering for a class of time-varying systems with stochastic nonlinearities and sensor saturation
Xia Zhao, LIU Chunsheng, Kunyu Wang, TIAN Engang · 2020
This paper has investigated the probabilistic-constrained filtering problem for a class of time-varying systems in consideration of both stochastic nonlinearities and sensor saturation. Using a modified maximum-volume-inscribed-ellipsoid(MVIE) method, the probability constraint on estimation error has been converted into some tractable inequalities. Then, in terms of recursive linear matrix inequalities techniques(LMI), sufficient condition for existence of expected probability constraint filter can be derived. A recursive optimal algorithm has also been introduced to obtain the filter gain matrices. Finally, an example validates the effectiveness of developed method.