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

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...

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Bibliographic Details
Published in:International journal of robust and nonlinear control 2018-09, Vol.28 (14), p.4139-4174
Main Authors: Liu, Wen‐Qiang, Wang, Xue‐Mei, Deng, Zi‐Li
Format: Article
Language:English
Subjects:
Continuously stirred tank reactors
Correlation analysis
Covariance
Estimators
fictitious noise technique
Lyapunov equation approach
Mathematical analysis
Matrix methods
Minimax technique
missing measurements
multiplicative noises
Noise
packet dropouts
Parameter uncertainty
robust fusion estimators
Robustness
uncertain multisensor networked system
uncertain noise variances
Upper bounds
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Summary: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.
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.4226