EKF-based state estimation for nonlinear complex networks
详细信息 查看官网全文
- 英文论文题名：EKF-based state estimation for nonlinear complex networks
- 论文作者：Jian Sun ; Wenling Li ; Yingmin Jia ; Junping Du
- 英文论文作者：Jian Sun ; Wenling Li ; Yingmin Jia ; Junping Du ; The Seventh Research Division ; Beihang University (BUAA) ; Computer Science and Technology ; Beijing University of Posts and Telecommunications
- 年：2017
- 作者机构：The Seventh Research Division,Beihang University (BUAA);Computer Science and Technology,Beijing University of Posts and Telecommunications;
- 英文论文关键词：State estimation ; Complex networks ; Extended Kalman filter
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：O157.5;TN713
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：277k
- 原文格式：D
- 会议级别：全国
摘要
This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks. A recursive state estimator is developed by employing the structure of the extended Kalman filter(EKF) with coupling terms. By using the stochastic analysis technique, an upper bound is derived for the coupling strength to guarantee the boundedness of the estimation errors in the mean square sense. A numerical example involving localization of mobile robots is provided to illustrate the effectiveness of the proposed estimator.
This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks. A recursive state estimator is developed by employing the structure of the extended Kalman filter(EKF) with coupling terms. By using the stochastic analysis technique, an upper bound is derived for the coupling strength to guarantee the boundedness of the estimation errors in the mean square sense. A numerical example involving localization of mobile robots is provided to illustrate the effectiveness of the proposed estimator.
This paper studies the state estimation problem for a class of discrete-time nonlinear complex networks. A recursive state estimator is developed by employing the structure of the extended Kalman filter(EKF) with coupling terms. By using the stochastic analysis technique, an upper bound is derived for the coupling strength to guarantee the boundedness of the estimation errors in the mean square sense. A numerical example involving localization of mobile robots is provided to illustrate the effectiveness of the proposed estimator.
引文
[1]L.Wang,Z.D.Wang,G.L.Wei,and S.Yan.Event-based state estimation for a class of nonlinear discrete-time complex networks with stochastic noises,in Control Conference.IEEE,2015:1804–1809.
[2]M.V.Basin,P.Shi,and D.C.Alvarez.Joint state filtering and parameter estimation for linear stochastic time-delay systems,in Signal Processing,91(4):782–792,2011.
[3]A.La,and B.Re.Emergence of Scaling in Random Networks,in Science,286(5439):509–512,1999.
[4]L.C.Wang,G.L.Wei,and H.S.Shu.State estimation for complex networks with randomly occurring coupling delays,in Neurocomputing,122(25):513–520,2013.
[5]G.P.Jiang,K.S.Tang,and G.R.Chen.A State-ObserverBased Approach for Synchronization in Complex Dynamical Networks,in Circuits Systems I Regular Papers IEEE Transactions,53(12):2739–2745,2006.
[6]X.D.Yin.State Estimation of Output-coupling Complex Dynamical Networks under Noisy Transmission Channel,in Computer Technology Development,2013.
[7]D.A.Haughton,and G.T.Heydt.A linear state estimation formulation for smart distribution systems,in IEEE Transactions on Power Systems,28(2):1187–1195,2013.
[8]C.X.Fan,F.W.Yang,and Y.Zhou.State Estimation for Coupled Output Discrete-time Complex Network with Stochastic Measurements and Different Inner Coupling Matrices,in International Journal of Control,Automation,and Systems,10(3):498–505,2012.
[9]R.Caballero,Hermoso-Carazo A,and Jimnez-Lpez,and J.Linares-Prez.Signal estimation with multiple delayed sensors using covariance information.in Digital Signal Processing,20(2):528-540,2010.
[10]C.X.Fan,and G.P.Jiang.State estimation of complex dynamical network under noisy transmission channel,in Circuits and Systems(ISCAS),2012 IEEE International Symposium,57:2107–2110,2012.
[11]B.Shen,Z.D.Wang,D.R.Ding,and H.S.Shu.H∞State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements,in Neural Networks and Learning Systems,24(12):2027–2037,2013.
[12]J.L.Liang,Z.D.Wang,and X.H.Liu,State Estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays:The discrete-time case,in IEEE Transactions on Neural Networks,20(5):781–793,2009.
[13]J.L.Liang,Z.D.Wang,Y.L.Liu,and X.H.Liu,State estimation for two dimensional complex networks with randomly occurring nonlinearities and randomly varying sensor delays,in Int.J.Robust Nonlinear Control,24(1):18–38,2014.
[14]J.L.Liu,J.Cao,Z.Wu,and Q.Qi.State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements,in International Journal of Systems Science,24:1233–1242,2005.
[15]B.Shen,Z.D.Wang,and X.H.Liu,Bounded H∞synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon,in IEEE Transactions on Neural Networks,22(1):145–157,2011.
[16]D.R.Ding,Z.D.Wang,B.Shen,and H.S.Shu,H∞state estimation for discretetime complex networks with randomly occurring sensor saturations and randomly varying sensor delays,in IEEE Transactions on Neural Networks and Learning Systems,23(5):725–736,2012.
[17]J.Hu,Z.D.Wang,S.Liu,and H.H.Gao.A varianceconstrained approach to recursive state estimation for timevarying complex networks with missing measurements,in Automatica,64:155–162,2016.
[18]K.Reif,and R.Unbehauen,Stochastic stability of the discrete-time extended Kalman filter,in IEEE Transactions on Automatic Control,44:714–728,1999.
[19]S.Kluge,K.Reif,and M.Brokate,Stochastic stability of the extended Kalman filter with intermittent observations,in IEEE Trans.Autom.Control,55(2):514–518,2010
[20]G.Wang,J.Chen,and J.Sun.Stochastic stability of extended filtering for non-linear systems with measurement packet losses,in IET Control Theory and Applications,7(17):2048–2055,2013.
[21]S.Liu,G.L.Wei,Y.Song,and Y.R.Liu.Extended Kalman Filtering for Stochastic Nonlinear Systems with Randomly Occurring Cyber Attacks,in Neurocomputing,2016.
[22]X.J.Zheng,and H.J.Fang.Recursive state estimation for discrete-time nonlinear systems with event-triggered data transmission,norm-bounded uncertainties and multiple missing measurements,in International Journal of Robust Nonlinear Control,doi:10.1002/rnc.3527.2016.
[23]K.Xiong,C.L.Wei,and L.D.Liu.Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties,in IEEE Transactions on Systems Man and Cybernetics-Part A Systems and Humans,40(2):399–405,2010.
[24]X.D.Liu,L.Y.Li,L.Zhen,and Ty.Fernando.Stochastic Stability Condition for the Extended Kalman Filter with Intermittent Observations,in IEEE Transactions on Circuits and Systems II:Express Briefs,DOI:10.1109/TCSII.2016.2578956,2016.
[25]R.Agniel,and E.Jury,Almost sure boundedness of randomly sampled systems,in SIAM J.Control Optim,DOI:10.1137/0309027.1971.
[26]T.Tarn,and Y.Rasis,Observers for nonlinear stochastic systems,in IEEE Trans.Autom.Control,21(4):441–448,1976.
[27]R.Brown and P.Hwang,in Introduction to Random Singals and Applied Kalman Filtering.New York:Wiley,1992.
[28]R.Unbehauen,in Systemtheorie 2,Munich,Germany:R.Oldenbourg Verlag,1998.
[29]X.H.Chen,and Y.M.Jia.Indoor localization for mobile robots using lampshade corners as landmarks:visual system calibration,feature extraction and experiments,in International Journal of Control,Automation,and Systems,12(6):1313–1322,2014.
[30]W.L.Li,Y.M.Jia,and J.P.Du.RSS-based joint detection and tracking in mixed LOS and NLOS environments,in Digital Signal Processing.43:38–46,2015.
[2]M.V.Basin,P.Shi,and D.C.Alvarez.Joint state filtering and parameter estimation for linear stochastic time-delay systems,in Signal Processing,91(4):782–792,2011.
[3]A.La,and B.Re.Emergence of Scaling in Random Networks,in Science,286(5439):509–512,1999.
[4]L.C.Wang,G.L.Wei,and H.S.Shu.State estimation for complex networks with randomly occurring coupling delays,in Neurocomputing,122(25):513–520,2013.
[5]G.P.Jiang,K.S.Tang,and G.R.Chen.A State-ObserverBased Approach for Synchronization in Complex Dynamical Networks,in Circuits Systems I Regular Papers IEEE Transactions,53(12):2739–2745,2006.
[6]X.D.Yin.State Estimation of Output-coupling Complex Dynamical Networks under Noisy Transmission Channel,in Computer Technology Development,2013.
[7]D.A.Haughton,and G.T.Heydt.A linear state estimation formulation for smart distribution systems,in IEEE Transactions on Power Systems,28(2):1187–1195,2013.
[8]C.X.Fan,F.W.Yang,and Y.Zhou.State Estimation for Coupled Output Discrete-time Complex Network with Stochastic Measurements and Different Inner Coupling Matrices,in International Journal of Control,Automation,and Systems,10(3):498–505,2012.
[9]R.Caballero,Hermoso-Carazo A,and Jimnez-Lpez,and J.Linares-Prez.Signal estimation with multiple delayed sensors using covariance information.in Digital Signal Processing,20(2):528-540,2010.
[10]C.X.Fan,and G.P.Jiang.State estimation of complex dynamical network under noisy transmission channel,in Circuits and Systems(ISCAS),2012 IEEE International Symposium,57:2107–2110,2012.
[11]B.Shen,Z.D.Wang,D.R.Ding,and H.S.Shu.H∞State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements,in Neural Networks and Learning Systems,24(12):2027–2037,2013.
[12]J.L.Liang,Z.D.Wang,and X.H.Liu,State Estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays:The discrete-time case,in IEEE Transactions on Neural Networks,20(5):781–793,2009.
[13]J.L.Liang,Z.D.Wang,Y.L.Liu,and X.H.Liu,State estimation for two dimensional complex networks with randomly occurring nonlinearities and randomly varying sensor delays,in Int.J.Robust Nonlinear Control,24(1):18–38,2014.
[14]J.L.Liu,J.Cao,Z.Wu,and Q.Qi.State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements,in International Journal of Systems Science,24:1233–1242,2005.
[15]B.Shen,Z.D.Wang,and X.H.Liu,Bounded H∞synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon,in IEEE Transactions on Neural Networks,22(1):145–157,2011.
[16]D.R.Ding,Z.D.Wang,B.Shen,and H.S.Shu,H∞state estimation for discretetime complex networks with randomly occurring sensor saturations and randomly varying sensor delays,in IEEE Transactions on Neural Networks and Learning Systems,23(5):725–736,2012.
[17]J.Hu,Z.D.Wang,S.Liu,and H.H.Gao.A varianceconstrained approach to recursive state estimation for timevarying complex networks with missing measurements,in Automatica,64:155–162,2016.
[18]K.Reif,and R.Unbehauen,Stochastic stability of the discrete-time extended Kalman filter,in IEEE Transactions on Automatic Control,44:714–728,1999.
[19]S.Kluge,K.Reif,and M.Brokate,Stochastic stability of the extended Kalman filter with intermittent observations,in IEEE Trans.Autom.Control,55(2):514–518,2010
[20]G.Wang,J.Chen,and J.Sun.Stochastic stability of extended filtering for non-linear systems with measurement packet losses,in IET Control Theory and Applications,7(17):2048–2055,2013.
[21]S.Liu,G.L.Wei,Y.Song,and Y.R.Liu.Extended Kalman Filtering for Stochastic Nonlinear Systems with Randomly Occurring Cyber Attacks,in Neurocomputing,2016.
[22]X.J.Zheng,and H.J.Fang.Recursive state estimation for discrete-time nonlinear systems with event-triggered data transmission,norm-bounded uncertainties and multiple missing measurements,in International Journal of Robust Nonlinear Control,doi:10.1002/rnc.3527.2016.
[23]K.Xiong,C.L.Wei,and L.D.Liu.Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties,in IEEE Transactions on Systems Man and Cybernetics-Part A Systems and Humans,40(2):399–405,2010.
[24]X.D.Liu,L.Y.Li,L.Zhen,and Ty.Fernando.Stochastic Stability Condition for the Extended Kalman Filter with Intermittent Observations,in IEEE Transactions on Circuits and Systems II:Express Briefs,DOI:10.1109/TCSII.2016.2578956,2016.
[25]R.Agniel,and E.Jury,Almost sure boundedness of randomly sampled systems,in SIAM J.Control Optim,DOI:10.1137/0309027.1971.
[26]T.Tarn,and Y.Rasis,Observers for nonlinear stochastic systems,in IEEE Trans.Autom.Control,21(4):441–448,1976.
[27]R.Brown and P.Hwang,in Introduction to Random Singals and Applied Kalman Filtering.New York:Wiley,1992.
[28]R.Unbehauen,in Systemtheorie 2,Munich,Germany:R.Oldenbourg Verlag,1998.
[29]X.H.Chen,and Y.M.Jia.Indoor localization for mobile robots using lampshade corners as landmarks:visual system calibration,feature extraction and experiments,in International Journal of Control,Automation,and Systems,12(6):1313–1322,2014.
[30]W.L.Li,Y.M.Jia,and J.P.Du.RSS-based joint detection and tracking in mixed LOS and NLOS environments,in Digital Signal Processing.43:38–46,2015.