Optimal Filtering of Multi-sensor Networked Systems with Unknown Channel Interferences and Compensation of Packet Losses
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- 英文论文题名：Optimal Filtering of Multi-sensor Networked Systems with Unknown Channel Interferences and Compensation of Packet Losses
- 论文作者：Bo Qi ; Shuli Sun
- 英文论文作者：Bo Qi ; Shuli Sun ; Department of Automation ; Heilongjiang University
- 年：2017
- 作者机构：Department of Automation,Heilongjiang University;
- 英文论文关键词：Optimal filter ; compensation of packet loss ; unknown channel interference ; multi-sensor networked systems ; stability
- 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：TN713;TP212
- 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：5
- 文件大小：160k
- 原文格式：D
- 会议级别：全国
摘要
The optimal filtering problem for multi-sensor networked systems with unknown channel interferences and packet losses is studied.There are possible packet dropouts and unknown channel interferences during the data transmissions from the sensors to the filter.The phenomena of packet losses are described by a set of random variables satisfying Bernoulli distributions.The prediction values of the lost measurements are used as the compensations when packet losses occur.In the absence of any information about the unknown channel interferences,an optimal filter independent of unknown channel interferences is designed based on the linear unbiased minimum variance estimation criterion.The stability of the proposed filter is analyzed.A simulation example verifies the effectiveness of the proposed algorithm.
The optimal filtering problem for multi-sensor networked systems with unknown channel interferences and packet losses is studied.There are possible packet dropouts and unknown channel interferences during the data transmissions from the sensors to the filter.The phenomena of packet losses are described by a set of random variables satisfying Bernoulli distributions.The prediction values of the lost measurements are used as the compensations when packet losses occur.In the absence of any information about the unknown channel interferences,an optimal filter independent of unknown channel interferences is designed based on the linear unbiased minimum variance estimation criterion.The stability of the proposed filter is analyzed.A simulation example verifies the effectiveness of the proposed algorithm.
The optimal filtering problem for multi-sensor networked systems with unknown channel interferences and packet losses is studied.There are possible packet dropouts and unknown channel interferences during the data transmissions from the sensors to the filter.The phenomena of packet losses are described by a set of random variables satisfying Bernoulli distributions.The prediction values of the lost measurements are used as the compensations when packet losses occur.In the absence of any information about the unknown channel interferences,an optimal filter independent of unknown channel interferences is designed based on the linear unbiased minimum variance estimation criterion.The stability of the proposed filter is analyzed.A simulation example verifies the effectiveness of the proposed algorithm.
引文
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[3]S.L.Sun,L.H.Xie,W.D.Xiao,Y.C.Soh,Optimal linear estimation for systems with multiple packet dropouts,Automatica,44(5):1333-1342,2008.
[4]S.L.Sun,Optimal Linear Filters for Discrete-Time Systems with Randomly Delayed and Lost Measurements with/without Time Stamps,IEEE Trans.Automatic Control,58(6):1551-1556,2013.
[5]S.L.Sun,G.H.Wang,Modeling and estimation for networked systems with multiple random transmission delays and packet losses,Systems&Control Letters,73(12):6-16,2014.
[6]R.Caballero-Aguila,I.Garcia-Garrido,and J.Linares-Perez,Information fusion algorithms for state estimation in multi-sensor systems with correlated missing measurements,Appl.Math.Comput.,226:548-563,2014.
[7]R.Caballero-Aguila,A.Hermoso-Carazo,and J.Linares-Perez,Fusion estimation using measured outputs with random parameter matrices subject to random delays and packet dropouts,Signal Processing,127:12-23,2016.
[8]C.Y.Pang,S.L.Sun,Fusion Predictors for Multisensor Stochastic Uncertain Systems With Missing Measurements and Unknown Measurement Disturbances,IEEE Sensors Journal,15(8):4346-4354,2015.
[9]E.I.Silva,M.A.Solis,An alternative look at the constant-gain Kalman filter for state estimation over erasure channels,IEEE Trans.Automatic Control,58(12):3259-3265,2013.
[10]S.L.Sun,T.Tian,H.L.Lin,Optimal Linear Estimators for Systems with Finite-step Correlated Noises and Packet Dropout Compensations,IEEE Trans.Signal Processing,64(21):5672-5681,2016.
[11]P.K.Kitanidis,Unbiased minimum-variance linear state estimation,Automatica,23(6):775-778,1987.
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[13]S.Gillijn,B.D.Moor,Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough,Automatica,43(5):934-937,2007.
[14]C.S.Hsieh,On the global optimality of unbiased minimum-variance state estimation for systems with unknown inputs,Automatica,46(4):708-715,2010.
[15]B.Qi,S.L.Sun,Fusion estimation for multi-sensor systems with unknown communication disturbances and compensation of packet losses,J.Sys.Sci.&Math.Scis.,36(8):1094-1106,2016.
[16]B.R.Fang,J.D.Zhou,and Y.M.Li,Theory of Matrices.Beijing,China:Tsinghua Univ.Press,2004.
[17]Z.L.Deng,Information Fusion Estimation Theory with Application.Beijing,China:Science Press,2012.
[2]K.Y.You,L.H.Xie,Survey of Recent Progress in Networked Control Systems.Acta Automatica Sinica,39(2):101-118,2013.
[3]S.L.Sun,L.H.Xie,W.D.Xiao,Y.C.Soh,Optimal linear estimation for systems with multiple packet dropouts,Automatica,44(5):1333-1342,2008.
[4]S.L.Sun,Optimal Linear Filters for Discrete-Time Systems with Randomly Delayed and Lost Measurements with/without Time Stamps,IEEE Trans.Automatic Control,58(6):1551-1556,2013.
[5]S.L.Sun,G.H.Wang,Modeling and estimation for networked systems with multiple random transmission delays and packet losses,Systems&Control Letters,73(12):6-16,2014.
[6]R.Caballero-Aguila,I.Garcia-Garrido,and J.Linares-Perez,Information fusion algorithms for state estimation in multi-sensor systems with correlated missing measurements,Appl.Math.Comput.,226:548-563,2014.
[7]R.Caballero-Aguila,A.Hermoso-Carazo,and J.Linares-Perez,Fusion estimation using measured outputs with random parameter matrices subject to random delays and packet dropouts,Signal Processing,127:12-23,2016.
[8]C.Y.Pang,S.L.Sun,Fusion Predictors for Multisensor Stochastic Uncertain Systems With Missing Measurements and Unknown Measurement Disturbances,IEEE Sensors Journal,15(8):4346-4354,2015.
[9]E.I.Silva,M.A.Solis,An alternative look at the constant-gain Kalman filter for state estimation over erasure channels,IEEE Trans.Automatic Control,58(12):3259-3265,2013.
[10]S.L.Sun,T.Tian,H.L.Lin,Optimal Linear Estimators for Systems with Finite-step Correlated Noises and Packet Dropout Compensations,IEEE Trans.Signal Processing,64(21):5672-5681,2016.
[11]P.K.Kitanidis,Unbiased minimum-variance linear state estimation,Automatica,23(6):775-778,1987.
[12]M.Darouach,M.Zasadzinski,Unbiased minimum variance estimation for systems with unknown exogenous inputs,Automatica,33(4):717-719,1997.
[13]S.Gillijn,B.D.Moor,Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough,Automatica,43(5):934-937,2007.
[14]C.S.Hsieh,On the global optimality of unbiased minimum-variance state estimation for systems with unknown inputs,Automatica,46(4):708-715,2010.
[15]B.Qi,S.L.Sun,Fusion estimation for multi-sensor systems with unknown communication disturbances and compensation of packet losses,J.Sys.Sci.&Math.Scis.,36(8):1094-1106,2016.
[16]B.R.Fang,J.D.Zhou,and Y.M.Li,Theory of Matrices.Beijing,China:Tsinghua Univ.Press,2004.
[17]Z.L.Deng,Information Fusion Estimation Theory with Application.Beijing,China:Science Press,2012.