Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

Yang, Chunshan; Deng, Zili

doi:10.1002/acs.2833

Cited by 25 publications

(9 citation statements)

References 45 publications

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“…The proposed results expand the research of robust Kalman estimation problem for system with mixed uncertainties in the literature, such as the uncertain noise variances are not considered in References 29,30, the only single networked‐induced uncertainty is considered in References 31–33, the random measurement delay, 34 the packet dropouts, 35,36 and missing measurement 37 are not considered. The robust FCI fusion Kalman estimation problem is seldom considered.…”

Section: Discussionmentioning

confidence: 87%

“…A minimum variance estimator for multisensor system with random parameter, one‐step measurements delay and packet dropouts is proposed in Reference 30. Following Reference 16, the robust Kalman estimation problem for system with uncertain noise variance, multiplicative noise and single networked‐induced uncertainty are addressed in References 31–33, where packet dropouts 31,32 and one‐step measurements delay 33 are considered respectively. Further, the robust fusion Kalman estimation problem for system with two networked‐induced uncertainties are addressed in References 34–37, where missing measurements and packets dropouts, 34 random measurements delay and missing measurements, 35,36 random measurements delay and packets dropouts 37 are considered, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays

Yang

Zhao

Liu

et al. 2023

Optim Control Appl Methods

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For multisensor networked systems with uncertain noise variances, multiplicative noises and multiple networked‐induced uncertainties including missing measurements, packets dropouts and two‐step random measurement delays, the robust weighted fusion estimation problem is addressed in this article. More precisely, the system noise variances are assumed to be uncertain but bounded, the other four uncertainties are compensated into fictitious white noise by the proposed model transformation method, which includes the augmented method and extended fictitious noise technique. Then local multi‐model system is obtained, for which robust local Kalman estimator is obtained based on the minimax robust estimation principle and unified estimation method. Based on this, the six robust weighted fusion time‐varying Kalman estimators are presented in a unified form, which include robust weighted fusers weighted by matrices, diagonal, scalars, and a robust covariance intersection (CI) fuser and two fast CI (FCI) fusers. The robustness proving method, including the extended Lyapunov equation approach with two kinds of generalized Lyapunov equations, non‐negative matrix factorization and elementary transformation of matrix, is presented to prove that the actual estimation error variances are guaranteed to have minimal upper bounds for all admissible uncertainties. The accuracy relations are proved. Further, the robust local and fused steady‐state Kalman estimators are presented. Finally, a simulation example applied to Internet‐based three tank water system is given to demonstrate effectiveness of the proposed results.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays

Yang

Zhao

Liu

et al. 2023

Optim Control Appl Methods

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“…The system given in ( 1), ( 2), (12), and ( 14) can be equivalently written in an augmented form. Let us consider a new state vector as follows:…”

Section: Augmented Stochastic State-space Modelmentioning

confidence: 99%

“…From Figure 2, it is observed that there are 25 instants at which event have not occurred. Thus, sensor data are not transmitted for these instants and the estimator uses previous data available in the estimator node according to (12). This ensures that the bandwidth utilization and F I G U R E 2 Occurrence of event: "1" represents event has occurred, whereas "0" indicates event has not occurred.…”

Section: Consider the System With Matrix In (1) And (2) As Followsmentioning

confidence: 99%

“…It also takes care of fading measurements and noise in the S‐E channel. A robust estimation of networked system with packet dropouts in S‐E channel is presented in Reference 12, where the system is affected by uncertain variance multiplicative and linearly correlated additive white noise. Finally, robust steady state Kalman estimators are also presented.…”

Section: Introductionmentioning

confidence: 99%

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Event‐based optimal filter for a networked system with multiplicative and auto/cross‐correlated process and measurement noise

Roy

Kannan

2022

Adaptive Control & Signal

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Summary This article presents an event triggered based optimal filter for a networked system with multiplicative noise in state and measurement matrices. The process and measurement noise are auto/cross‐correlated, which is described as a moving average process. The network in both sensor to estimator (S‐E) and controller to actuator (C‐A) channel is affected by random sensor delay and packet dropouts. These effects are modeled using two random variables for both the S‐E and C‐A channels. Sensor data are transmitted to the estimator through the S‐E channel using an event‐based mechanism. It depends on a time varying threshold to improve trade‐off between tracking performance and utilization of network bandwidth. The process and measurement noise are two‐step auto/cross‐correlated. A comprehensive augmented stochastic model is developed for the networked system by combining the effects of event triggering, network constraints and noise correlation. The filter is designed using an innovation analysis approach based on orthogonal projection. The solution of the filter is given in terms of a Lyapunov and a Riccati equation. An algorithm is presented for the implementation of developed filter. The stability properties of the proposed optimal filter are analyzed. The effectiveness of the proposed filter is shown in the simulation platform and accumulated mean square error performance is evaluated based on Monte Carlo simulation.

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Weighted fusion robust steady‐state estimators for multisensor networked systems with one‐step random delay and inconsecutive packet dropouts

Liu

Deng

2019

Adaptive Control & Signal

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Summary In this paper, the weighted fusion robust steady‐state Kalman filtering problem is studied for a class of multisensor networked systems with mixed uncertainties. The uncertainties include same multiplicative noises in system parameter matrices, uncertain noise variances, as well as the one‐step random delay and inconsecutive packet dropouts, which modeled by sequences of Bernoulli variables with different probabilities. By defining a new observation vector and applying the augmented method, the system under study is converted into one with only uncertain noise variances. The sufficient conditions for the existence of steady‐state estimators are given. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are proposed. Applying the optimal fusion algorithm weighted by matrices and the covariance intersection fusion algorithm, the two kinds of robust fusion steady‐state Kalman estimators are derived in a unified framework. The robustness of the proposed fusion estimators is proved by applying the permutation matrices and the global Lyapunov equations method, such that, for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fusion steady‐state Kalman estimators are proved. An example with application to autoregressive moving average signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.

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Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

Cited by 25 publications

References 45 publications

Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays

Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays

Event‐based optimal filter for a networked system with multiplicative and auto/cross‐correlated process and measurement noise

Weighted fusion robust steady‐state estimators for multisensor networked systems with one‐step random delay and inconsecutive packet dropouts

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