2019
DOI: 10.1016/j.inffus.2018.01.014
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Robust weighted state fusion Kalman estimators for networked systems with mixed uncertainties

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Cited by 64 publications
(79 citation statements)
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“…The optimal (minimum variance) filtering for worst‐case system is called minimax robust filtering. Designing the minimum variance estimator for the worst‐case system will yield the minimax robust estimator . The problem is to design the local and fusion robust steady‐state Kalman estimators (predictor, filter, and smoother) xtrue^θ()t|t+N()N=1,N=0,N>0 for uncertain multisensor networked system to , such that their actual steady‐state estimation error variances Ptrue‾θ()N, yielded by all admissible uncertainties, have the corresponding minimal upper bounds P θ ( N ), ie, Ptrue‾θ()NPθ()N, where θ = i , m ,CI denote the i th local robust estimator, the fusers weighted by matrices, and the CI fusers, respectively.…”
Section: Problem Formulationmentioning
confidence: 99%
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“…The optimal (minimum variance) filtering for worst‐case system is called minimax robust filtering. Designing the minimum variance estimator for the worst‐case system will yield the minimax robust estimator . The problem is to design the local and fusion robust steady‐state Kalman estimators (predictor, filter, and smoother) xtrue^θ()t|t+N()N=1,N=0,N>0 for uncertain multisensor networked system to , such that their actual steady‐state estimation error variances Ptrue‾θ()N, yielded by all admissible uncertainties, have the corresponding minimal upper bounds P θ ( N ), ie, Ptrue‾θ()NPθ()N, where θ = i , m ,CI denote the i th local robust estimator, the fusers weighted by matrices, and the CI fusers, respectively.…”
Section: Problem Formulationmentioning
confidence: 99%
“…For multisensor systems with multiplicative noises, uncertain noise variances, and correlated measurement and process noises, applying the minimax robust estimation principle and fictitious noise technique, the robust local and information fusion Kalman estimators have been proposed in other works . More concretely, for linear discrete‐time systems with same multiplicative noises in the state and measurement matrices, as well as the process and measurement noise transition matrices, and with the uncertain‐variance linearly correlated white noises, the robust time‐varying and steady‐state Kalman estimators were presented in the work of Liu et al However, it is limited to the single‐sensor systems.…”
Section: Introductionmentioning
confidence: 99%
“…For multisensor systems with multiplicative noises, uncertain noise variances, and correlated measurement and process noises, applying the minimax robust estimation principle and fictitious noise technique, the robust local and information fusion Kalman estimators have been proposed in some works . More concretely, for multisensor systems with same multiplicative noises in state and measurement matrices, and uncertain‐variance linearly correlated additive white noises, the weighted state fusion robust Kalman estimators are presented .…”
Section: Introductionmentioning
confidence: 99%
“…In the past few years, some important results have been reported concerned with robust filtering for systems with uncertain noise variances. 9,[21][22][23][24][25] For the linear discrete time-varying multisensor stochastic systems with uncertain noise variances, according to the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, the robust information fusion Kalman filters have been presented in the work of Qi et al 9 The actual estimation error variances or their traces of each estimator are guaranteed to have a minimal upper bound for all the admissible uncertainties of noise variances. However, the limitation of the aforementioned work 9 is that only the noise variance uncertainties are considered, whereas multiplicative noises, missing measurements, and packet dropouts are not considered, and it cannot handle the systems with correlated noises.…”
Section: Introductionmentioning
confidence: 99%
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