Recursive State Estimation for Discrete-Time Nonlinear Complex Networks

This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks. The purpose is to design a recursive state estimator by using the variance-constrained approach such that the variance of the estimation error is not more than the prescribed upper bound. By adopting the structure of the extended Kalman filter (EKF), 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 is provided to illustrate the effectiveness of the proposed estimator.

[1]  Y. Yang.  Incentive and quality assurance: an agency theoretical perspective , 1993 .

[2]  Konrad Reif,et al.  Stochastic stability of the discrete-time extended Kalman filter , 1999, IEEE Trans. Autom. Control..

[3]  Guo-Ping Jiang,et al.  A State-Observer-Based Approach for Synchronization in Complex Dynamical Networks , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  Kai Xiong,et al.  Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  X. Kai,et al.  Robust extended Kalman filtering for nonlinear systems with multiplicative noises , 2011 .

[6]  Guo-Ping Jiang,et al.  State estimation of complex dynamical network under noisy transmission channel , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[7]  Jun Hu,et al.  Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements , 2012, Autom..

[8]  Fuwen Yang,et al.  State estimation for coupled output discrete-time complex network with stochastic measurements and different inner coupling matrices , 2012 .

[9]  Jun Hu,et al.  Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements , 2013, Int. J. Control.

[10]  Zidong Wang,et al.  $H_{\infty}$ State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Yin Xiao-don.  State Estimation of Output-coupling Complex Dynamical Networks under Noisy Transmission Channel , 2013 .

[12]  Guoliang Wei,et al.  State estimation for complex networks with randomly occurring coupling delays , 2013, Neurocomputing.

[13]  Yingmin Jia,et al.  Indoor localization for mobile robots using lampshade corners as landmarks: Visual system calibration, feature extraction and experiments , 2014 .

[14]  Jie Cao,et al.  State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements , 2014, Int. J. Syst. Sci..

[15]  Wenling Li,et al.  RSS-based joint detection and tracking in mixed LOS and NLOS environments , 2015, Digit. Signal Process..

[16]  Zidong Wang,et al.  Event-based state estimation for a class of nonlinear discrete-time complex networks with stochastic noises , 2015, 2015 34th Chinese Control Conference (CCC).

[17]  H. Fang,et al.  Recursive state estimation for discrete‐time nonlinear systems with event‐triggered data transmission, norm‐bounded uncertainties and multiple missing measurements , 2016 .

[18]  Yan Song,et al.  Recursive state estimation for discrete time-varying stochastic nonlinear systems with randomly occurring deception attacks , 2016, Int. J. Gen. Syst..

[19]  Shuai Liu,et al.  Extended Kalman filtering for stochastic nonlinear systems with randomly occurring cyber attacks , 2016, Neurocomputing.

[20]  Jun Hu,et al.  On co-design of filter and fault estimator against randomly occurring nonlinearities and randomly occurring deception attacks , 2016, Int. J. Gen. Syst..

[21]  Jun Hu,et al.  A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements , 2016, Autom..