Title
Variance-constrained state estimation for nonlinear complex networks with uncertain coupling strength.
Abstract
This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks with uncertain coupling strength. The purpose of this problem is to design a recursive state estimator such that, for all admissible coupling strength uncertainties and linearized errors of nonlinear functions, the estimation error is mean square bounded and the variance of the estimation error is not more than the prescribed upper bound. By adopting the structure of the extended Kalman filter, the gain matrix is determined by minimizing the trace of the prescribed upper bound matrix. It is shown that the estimator can be developed by solving two Riccati-like difference equations. A numerical example involving localization of mobile robots is provided to illustrate the effectiveness of the proposed estimator. Compared with the non-coupling estimator, simulation results show that the tracking accuracy has been improved by 82% using the proposed estimator.
Year
DOI
Venue
2017
10.1016/j.dsp.2017.02.014
Digital Signal Processing
Keywords
Field
DocType
State estimation,Complex networks,Uncertain coupling strength,Variance-constrained
Efficient estimator,Minimum-variance unbiased estimator,Extended Kalman filter,Mathematical optimization,Stein's unbiased risk estimate,Upper and lower bounds,Control theory,Minimax estimator,Minimum mean square error,Mathematics,Estimator
Journal
Volume
ISSN
Citations 
67
1051-2004
2
PageRank 
References 
Authors
0.36
18
5
Name
Order
Citations
PageRank
Wenling Li122718.83
Jian Sun220.36
Yingmin Jia31743135.37
Junping Du478991.80
Xiaoyan Fu522.05