Paper Info
Title
Robust centralized and weighted measurement fusion Kalman estimators for uncertain multisensor systems with linearly correlated white noises.
Abstract
Multisensor systems with mixed uncertainties and linearly correlated noises.A fictitious noise technique to compensate multiplicative noises was presented.A unified approach to design the robust Kalman estimators was presented.A minimax robust fusion Kalman filtering theory was presented.Robust centralized and weighted measurement fusion Kalman estimators. This paper addresses the design of robust centralized fusion (CF) and weighted measurement fusion (WMF) Kalman estimators for a class of uncertain multisensor systems with linearly correlated white noises. The uncertainties of the systems include multiplicative noises, missing measurements, and uncertain noise variances. By introducing the fictitious noises, the considered system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the robust CF and WMF time-varying Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the Lyapunov equation approach, their robustness is proved in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Using the information filter, their equivalence is proved. Their accuracy relations are proved. The computational complexities of their algorithms are analyzed and compared. Compared with CF algorithm, the WMF algorithm can significantly reduce the computational burden when the number of sensors is larger. A robust weighted least squares (WLS) measurement fusion filter is also presented only based on the measurement equation, and it is proved that the robust accuracy of the robust CF or WMF Kalman filter is higher than that of robust WLS filter. The corresponding robust fused steady-state estimators are also presented, and the convergence in a realization between the time-varying and steady-state robust fused estimators is proved by the dynamic error system analysis (DESA) method. A simulation example shows the effectiveness and correctness of the proposed results.
Year
DOI
Venue
2017
10.1016/j.inffus.2016.08.002
Information Fusion
Keywords
Field
DocType
Weighted measurement fusion,Multiplicative noises,Missing measurements,Uncertain noise variances,Linearly correlated white noises,Minimax robust Kalman estimators
Least squares,Convergence (routing),Minimax,Lyapunov equation,Control theory,Robustness (computer science),Kalman filter,Mathematics,Information filtering system,Estimator
Journal
Volume
Issue
ISSN
35
C
1566-2535
Citations 
PageRank 
References 
6
0.51
27
Authors
3
Name
Order
Citations
PageRank
Wen-Qiang Liu1252.19
Xue-Mei Wang2252.19
Zi-li Deng351444.75