Finite-Horizon Distributed State Estimation Under Randomly Switching Topologies and Redundant Channels

The distributed state estimation problem is examined for a kind of nonlinear time-varying stochastic systems through sensor networks (SNs) with randomly switching topologies as well as redundant channels. The random switches of the topologies for SNs are governed by Markovian jumping parameters and the redundant channels are introduced to help improve the capability of the network communication. We are interested in designing distributed state estimators so that the estimation error dynamics is confirmed to reach a prescribed level of average ${H}_{\infty }$ performance in terms of a finite horizon. Through intensive stochastic analysis, we acquire some sufficient conditions that guarantee the existence of the expected state estimators whose gain parameters are obtained recursively by means of the solution to a series of matrix inequalities. A numerical simulation is carried out to illustrate the validity of the developed state estimation algorithm.

[1]  Zidong Wang,et al.  Distributed Filtering for Fuzzy Time-Delay Systems With Packet Dropouts and Redundant Channels , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Fuad E. Alsaadi,et al.  Non-fragile state estimation for discrete Markovian jumping neural networks , 2016, Neurocomputing.

[3]  Zidong Wang,et al.  Finite-horizon H∞ fault estimation for linear discrete time-varying systems with delayed measurements , 2013, Autom..

[4]  Shaocheng Tong,et al.  Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Jinling Liang,et al.  Finite-horizon H∞ filtering for time-varying delay systems with randomly varying nonlinearities and sensor saturations , 2014 .

[6]  Fuad E. Alsaadi,et al.  Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels , 2017, Inf. Fusion.

[7]  Zhang Yi,et al.  Distributed fault detection in industrial system based on sensor wireless network , 2009, Comput. Stand. Interfaces.

[8]  Huijun Gao,et al.  On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels , 2016, IEEE Transactions on Automatic Control.

[9]  Gerard S. Schkolnik,et al.  Autonomous Formation Flight , 2004 .

[10]  Dan Zhang,et al.  Distributed H ∞ filtering for sensor networks with switching topology , 2013, Int. J. Syst. Sci..

[11]  Zidong Wang,et al.  Robust filtering with stochastic nonlinearities and multiple missing measurements , 2009, Autom..

[12]  Zheng You,et al.  Finite-horizon robust Kalman filtering for uncertain discrete time-varying systems with uncertain-covariance white noises , 2006, IEEE Signal Processing Letters.

[13]  C. Ahn,et al.  Unbiased Finite Impluse Response Filtering: An Iterative Alternative to Kalman Filtering Ignoring Noise and Initial Conditions , 2017, IEEE Control Systems.

[14]  Junping Du,et al.  Tobit Kalman filter with time-correlated multiplicative measurement noise , 2017 .

[15]  Donghua Zhou,et al.  A survey of fault diagnosis for swarm systems , 2014 .

[16]  Guoqiang Hu,et al.  Distributed Robust Fusion Estimation With Application to State Monitoring Systems , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[18]  Dan Ye,et al.  Fault-Tolerant Controller Design for General Polynomial-Fuzzy-Model-Based Systems , 2018, IEEE Transactions on Fuzzy Systems.

[19]  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.

[20]  Saeid Nahavandi,et al.  Robust Finite-Horizon Kalman Filtering for Uncertain Discrete-Time Systems , 2012, IEEE Transactions on Automatic Control.

[21]  Fuad E. Alsaadi,et al.  $H_{\infty }$ Fuzzy Fault Detection for Uncertain 2-D Systems Under Round-Robin Scheduling Protocol , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Fang-Xiang Wu,et al.  Estimating parameters of S-systems by an auxiliary function guided coordinate descent method , 2014 .

[23]  Fuwen Yang,et al.  Robust H∞ filtering with error variance constraints for discrete time-varying systems with uncertainty , 2003, Autom..

[24]  Naixue Xiong,et al.  An Efficient Intrusion Detection Approach for Visual Sensor Networks Based on Traffic Pattern Learning , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[25]  Yan Liang,et al.  Linear-minimum-mean-square-error observer for multi-rate sensor fusion with missing measurements , 2014 .

[26]  Dan Zhang,et al.  Distributed Filtering for Switched Linear Systems With Sensor Networks in Presence of Packet Dropouts and Quantization , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[27]  Peng Shi,et al.  Joint state filtering and parameter estimation for linear stochastic time-delay systems , 2011, Signal Process..

[28]  Zidong Wang,et al.  Distributed State Estimation for Discrete-Time Sensor Networks With Randomly Varying Nonlinearities and Missing Measurements , 2011, IEEE Transactions on Neural Networks.

[29]  Hamid Reza Karimi,et al.  Optimal residual generation for fault detection in linear discrete time-varying systems with uncertain observations , 2018, J. Frankl. Inst..

[30]  Yan Song,et al.  Local condition-based finite-horizon distributed H∞-consensus filtering for random parameter system with event-triggering protocols , 2017, Neurocomputing.

[31]  Yang Xiang,et al.  A survey on security control and attack detection for industrial cyber-physical systems , 2018, Neurocomputing.

[32]  Hongli Dong,et al.  Filter design, fault estimation and reliable control for networked time-varying systems: a survey , 2017 .

[33]  Guoliang Wei,et al.  Reliable H∞ state estimation for 2-D discrete systems with infinite distributed delays and incomplete observations , 2015, Int. J. Gen. Syst..

[34]  Behrooz Safarinejadian,et al.  Fault detection in non-linear systems based on GP-EKF and GP-UKF algorithms , 2014 .

[35]  Fei Liu,et al.  Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions , 2017, IEEE Transactions on Automatic Control.

[36]  Hongli Dong,et al.  Event-triggered distributed filtering over sensor networks with deception attacks and partial measurements , 2018, Int. J. Gen. Syst..

[37]  Qing-Long Han,et al.  Security control for a class of discrete-time stochastic nonlinear systems subject to deception attacks , 2016 .

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

[39]  Huijun Gao,et al.  Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems , 2014, Autom..

[40]  Xiaojie Su,et al.  Dissipativity-Based Filtering for Fuzzy Switched Systems With Stochastic Perturbation , 2016, IEEE Transactions on Automatic Control.

[41]  Fuad E. Alsaadi,et al.  On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays , 2017, Neural Computing and Applications.

[42]  Carlo Fischione,et al.  A distributed minimum variance estimator for sensor networks , 2008, IEEE Journal on Selected Areas in Communications.

[43]  Hongli Dong,et al.  Distributed filtering in sensor networks with randomly occurring saturations and successive packet dropouts , 2014 .

[44]  Yugang Niu,et al.  Filtering For Discrete Fuzzy Stochastic Systems With Sensor Nonlinearities , 2010, IEEE Transactions on Fuzzy Systems.

[45]  Shuai Liu,et al.  State estimation for stochastic discrete-time systems with multiplicative noises and unknown inputs over fading channels , 2018, Appl. Math. Comput..