Robust centralized and weighted measurement fusion white noise deconvolution estimators for multisensor systems with mixed uncertainties

Liu, Wenqiang; Wang, Xuemei; Deng, Zili

doi:10.1002/acs.2837

Cited by 14 publications

(14 citation statements)

References 42 publications

(99 reference statements)

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“…Some limitations in the existing references were overcome. For example, only handle the systems with uncertain noise variances,– only handle the systems without packet dropouts,– only handle the single‐sensor systems, and only handle the systems with exactly known noise variances –…”

Section: Discussionmentioning

confidence: 99%

“…Some limitations in the existing references were overcome. For example, only handle the systems with uncertain noise variances, [22][23][24][25] only handle the systems without packet dropouts, [26][27][28][29] only handle the single-sensor systems, 30 and only handle the systems with exactly known noise variances. 12,14,[35][36][37][38][39][40][41] As for future work, the robust fusion Kalman filtering theory proposed in this paper can be applied to design the robust fused white noise deconvolution estimators for the multisensor networked systems with mixed uncertainties including multiplicative noises, packet dropouts, random sensor delays, missing measurements, uncertain noise variances, and so forth.…”

Section: Discussionmentioning

confidence: 99%

“…Remark 7. Similar to other works, [26][27][28][29][30] by using the indirect approach of taking the limit operations to the corresponding robust local and fused time-varying Kalman estimators, the robust local and fused steady-state Kalman estimators can be easily obtained, which are omitted due to the limited space.…”

Section: Corollary 3 From the Definition X A (T) =mentioning

confidence: 92%

“…For multisensor systems with multiplicative noises in state matrix, missing measurements, and uncertain‐variance linearly correlated additive white noises, the robust centralized and weighted measurement fusion Kalman estimators are presented based on the minimax robust estimation principle and the fictitious noise technique . For multisensor systems with multiplicative noises in measurement matrix, missing measurements, and uncertain‐variance linearly correlated additive white noises, the robust centralized and weighted measurement fusion white noise deconvolution estimators are presented based on the minimax robust estimation principle and the fictitious noise technique . However, in the works of Liu et al, the packet dropouts are not considered.…”

Section: Introductionmentioning

confidence: 99%

“…For multisensor systems with multiplicative noises in measurement matrix, missing measurements, and uncertain‐variance linearly correlated additive white noises, the robust centralized and weighted measurement fusion white noise deconvolution estimators are presented based on the minimax robust estimation principle and the fictitious noise technique . However, in the works of Liu et al, the packet dropouts are not considered. For systems with multiplicative noises, packet dropouts, and uncertain‐variance linearly correlated additive white noises, the robust Kalman estimators are presented based on the minimax robust estimation principle and the fictitious noise technique .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Corollary 3 From the Definition X A (T) =mentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Robust integrated covariance intersection fusion Kalman estimators for networked systems with random measurement delays, multiplicative noises, and uncertain noise variances

RanChenjian

DENGZi-Li

2020

Adaptive Control & Signal

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In this article, the robust distributed fusion Kalman filtering problems are addressed for the networked mixed uncertain multisensor systems with random one-step measurement delays, multiplicative noises, and uncertain noise variances. A new augmented state approach with fictitious measurement noises modeled by the first-order moving average models is presented, by which the original system is transformed into a standard uncertain system only with uncertain-variance fictitious white noises. Based on the minimax robust estimation principle and Kalman filtering theory, a universal integrated covariance intersection (ICI) fusion approach is presented in the sense that first of all the robust local estimators and their conservative error variances and crosscovariances are presented, and then integrating the local estimation information yields ICI fusers. An extended Lyapunov equation approach with two kinds of Lyapunov equations is presented in order to prove the robustness and to compute fictitious noise statistics. Applying these approaches, the minimax robust local, ICI, and fast ICI fused Kalman estimators (predictor, filter, and smoother) are presented, such that for all admissible uncertainties, their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. Their robustness, accuracy relations, and convergence are also proved. The proposed ICI fusers improve the robust accuracies and overcome the drawbacks of the original covariance intersection fusers, such that the robust local estimators and their conservative variances are assumed to be known, and their conservative crosscovariances are ignored. Two simulation examples applied to the offshore platform system verify their correctness, effectiveness, and applicability. K E Y W O R D S integrated covariance intersection fusion approach, minimax robust Kalman estimators, mixed-uncertain networked systems, multiplicative noises, random measurement delays, uncertain noise variances

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Robust CAWOF Kalman predictors for uncertain multi‐sensor generalized system

Tao

Liu

Wang

et al. 2021

Adaptive Control & Signal

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Robust centralized and weighted observation fusion (CAWOF) prediction algorithm is addressed in this article for an uncertain multi-sensor generalized system with linear correlation between observation noises and an input white noise. This uncertainty in the generalized system primarily means that the variances of the aforementioned types of noise, as well as the multiplicative noise variances, are uncertain. Through singular value decomposition and virtual noise compensation, the original generalized system is changed to non-generalized reduced-order subsystems in which only noise variances are uncertain. Utilizing the minimax robustness estimation criterion, robust CAWOF Kalman predictors are put forward on account of the first subsystem with conservative upper bounds of noise variances. Eventually, robust observation fusion Kalman predictors of the original generalized system are proposed.The Lyapunov equation method is applied to verify two fusion predictors' robustness. With regard to all permissible uncertain practical noise variances, CAWOF predictors are robust, namely, the practical prediction error variances of two robust predictors will have minimum upper bounds. This equivalence between CAWOF Kalman predictors is proved by an information filter. In this article, the precision relationship of fusion predictors is given. Meanwhile, robust Kalman predictors for steady-state case are proposed, and the astringency of robust time-variant Kalman predictors is analyzed through the analysis of dynamic error system. The validity and correctness of proposed algorithm are proved by the simulation example of random dynamic input and output system in an economic system.

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Robust centralized and weighted measurement fusion white noise deconvolution estimators for multisensor systems with mixed uncertainties

Cited by 14 publications

References 42 publications

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

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

Robust integrated covariance intersection fusion Kalman estimators for networked systems with random measurement delays, multiplicative noises, and uncertain noise variances

Robust CAWOF Kalman predictors for uncertain multi‐sensor generalized system

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