Variance‐constrained robust estimation for uncertain systems with multiple packet dropouts

Wang, Baofeng; Guo, Ge; Yue, Wei

doi:10.1002/oca.1027

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…This has promoted many studies on robust Kalman filtering. [5][6][7][8][9][10][12][13][14][15][16][17] The so-called robust Kalman filter is concerned with the design of a fixed filter for a family of system models formed by uncertainties, such that its actual filtering error variances yielded by all admissible uncertainties are guaranteed to have a minimal upper bound, 18 and such property is called the robustness.…”

Section: Introductionmentioning

confidence: 99%

Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates

Liu

et al. 2022

Optim Control Appl Methods

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This article is concerned with the centralized fusion (CF) robust steady-state Kalman filtering problem for a class of multisensor networked systems with mixed uncertainties including state-dependent and noise-dependent multiplicative noises, missing measurements, packet dropouts, and uncertain noise variances. By using a model transformation approach, the initial multisensor system under study is transformed into a multi-model multisensor system only with uncertain noise variances. By introducing an augmented state vector, the CF system is obtained. In the light of the minimax robust estimation method, and based on the worst-case fusion system with conservative upper bounds of uncertain noise variances, the CF robust steady-state Kalman estimators (predictor, filter, and smoother) are proposed in a unified form. By means of the permutation matrices and Lyapunov equation method, the robustness of CF estimators is proved. The accuracy relations among the robust local and fusion steady-state estimators are proved. An example applied to autoregressive moving average signal estimation is put forward, and the robust CF steady-state signal estimators are proposed. Simulation experiment shows the correctness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates

Liu

et al. 2022

Optim Control Appl Methods

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“…All these lead to the measurement mode uncertainty. Accordingly, the filtering problem with packet losses has stirred considerable research attention and many research results have been published recently; see, for example [32][33][34][35]. In most literatures, the packet loss is described as a random variable in the distribution of Bernoulli, which may not be available because of the existence of the different transmission process in multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

A Robust Recursive Filter for Nonlinear Systems with Correlated Noises, Packet Losses, and Multiplicative Noises

Qian

Huang

Liu³

et al. 2014

Mathematical Problems in Engineering

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A robust filtering problem is formulated and investigated for a class of nonlinear systems with correlated noises, packet losses, and multiplicative noises. The packet losses are assumed to be independent Bernoulli random variables. The multiplicative noises are described as random variables with bounded variance. Different from the traditional robust filter based on the assumption that the process noises are uncorrelated with the measurement noises, the objective of the addressed robust filtering problem is to design a recursive filter such that, for packet losses and multiplicative noises, the state prediction and filtering covariance matrices have the optimized upper bounds in the case that there are correlated process and measurement noises. Two examples are used to illustrate the effectiveness of the proposed filter.

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Robust fusion time‐varying Kalman estimators for multisensor networked systems with mixed uncertainties

Liu

Wang

Deng

2018

Intl J Robust & Nonlinear

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Summary This paper addresses the problem of designing robust fusion time‐varying Kalman estimators for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, missing measurements, packet dropouts, and uncertain‐variance linearly correlated measurement and process white noises. By the augmented approach, the original system is converted into a stochastic parameter system with uncertain noise variances. Furthermore, applying the fictitious noise approach, the original system is converted into one with constant parameters and uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case system with the conservative upper bounds of the noise variances, the five robust fusion time‐varying Kalman estimators (predictor, filter, and smoother) are presented by using a unified design approach that the robust filter and smoother are designed based on the robust Kalman predictor, which include three robust weighted state fusion estimators with matrix weights, diagonal matrix weights, and scalar weights, a modified robust covariance intersection fusion estimator, and robust centralized fusion estimator. Their robustness is proved by using a combination method, which consists of Lyapunov equation approach, augmented noise approach, and decomposition approach of nonnegative definite matrix, such that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The accuracy relations among the robust local and fused time‐varying Kalman estimators are proved. A simulation example is shown with application to the continuous stirred tank reactor system to show the effectiveness and correctness of the proposed results.

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Variance‐constrained robust estimation for uncertain systems with multiple packet dropouts

Cited by 5 publications

References 33 publications

Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates

Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates

A Robust Recursive Filter for Nonlinear Systems with Correlated Noises, Packet Losses, and Multiplicative Noises

Robust fusion time‐varying Kalman estimators for multisensor networked systems with mixed uncertainties

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