A Kind of Incremental Kalman Smoother Under Poor Observation Condition
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- 英文论文题名：A Kind of Incremental Kalman Smoother Under Poor Observation Condition
- 论文作者：Xiaojun Sun ; Guangming Yan ; Bo Zhang
- 英文论文作者：Xiaojun Sun ; Guangming Yan ; Bo Zhang ; Electrical Engineering Institute ; Heilongjiang University
- 年：2017
- 作者机构：Electrical Engineering Institute,Heilongjiang University;
- 英文论文关键词：Increment Kalman filtering ; poor observation condition ; Kalman smoother ; filtering accuracy
- 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：TP301.6
- 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：4
- 文件大小：117k
- 原文格式：D
- 会议级别：全国
摘要
When the observation equation of system has not been verified or corrected under certain environmental conditions,applying it will yield to unknown system errors and filtering errors.In this paper,a kind of incremental Kalman smoothing algorithm is presented,which can eliminate the unknown system errors and improve the accuracy of state estimators.This algorithm is simple in form and easy to be applied in engineering practice.The simulation results show its effectiveness and feasibility.
When the observation equation of system has not been verified or corrected under certain environmental conditions,applying it will yield to unknown system errors and filtering errors.In this paper,a kind of incremental Kalman smoothing algorithm is presented,which can eliminate the unknown system errors and improve the accuracy of state estimators.This algorithm is simple in form and easy to be applied in engineering practice.The simulation results show its effectiveness and feasibility.
When the observation equation of system has not been verified or corrected under certain environmental conditions,applying it will yield to unknown system errors and filtering errors.In this paper,a kind of incremental Kalman smoothing algorithm is presented,which can eliminate the unknown system errors and improve the accuracy of state estimators.This algorithm is simple in form and easy to be applied in engineering practice.The simulation results show its effectiveness and feasibility.
引文
[1]R.E.Kalman,A new approcha to linear filtering and prediction problems,ASME Journal of Basic Engineering,82D:35-45,1960.
[2]Y.Y.Qin,Kalman filtering and integrated navigation theory Northwestern Polytechnic University,2010.
[3]X.J.Sun,Y.Gao,Z.L.Deng,C.Li,J.W.Wang,Multi-model Information Fusion Kalman Filtering and White Noise Deconvolution,Information Fusion,11:163-173,2010.
[4]L.S.Liu,Orbit precision determinination&self-calibration technique of spacecraft,National Defense Industry Press,2005.
[5]H M Fu,Y.Z Wu,T.S.Lou,Incremental Kalman Filter method under poor observation condition,Journal of Mechanical Strength,34(1):43-47,2012.
[6]H.M.Fu,T.S.Lou,Y.Z.Wu.Extended incremental Kalman filter method under poor observation condition,Journal of Aerospace Power,27(4):777-781,2012.
[7]H.M.Fu,T.S.Lou,Y.Z.Wu,Incremental particle filter method,Journal of Aerospace Power,28(6):1201-1207,2013.
[2]Y.Y.Qin,Kalman filtering and integrated navigation theory Northwestern Polytechnic University,2010.
[3]X.J.Sun,Y.Gao,Z.L.Deng,C.Li,J.W.Wang,Multi-model Information Fusion Kalman Filtering and White Noise Deconvolution,Information Fusion,11:163-173,2010.
[4]L.S.Liu,Orbit precision determinination&self-calibration technique of spacecraft,National Defense Industry Press,2005.
[5]H M Fu,Y.Z Wu,T.S.Lou,Incremental Kalman Filter method under poor observation condition,Journal of Mechanical Strength,34(1):43-47,2012.
[6]H.M.Fu,T.S.Lou,Y.Z.Wu.Extended incremental Kalman filter method under poor observation condition,Journal of Aerospace Power,27(4):777-781,2012.
[7]H.M.Fu,T.S.Lou,Y.Z.Wu,Incremental particle filter method,Journal of Aerospace Power,28(6):1201-1207,2013.