Dynamic Positioning Particle Filtering Method Based on the EAKF
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- 英文论文题名：Dynamic Positioning Particle Filtering Method Based on the EAKF
- 论文作者：Xiaogong Lin ; Chen Chen
- 英文论文作者：Xiaogong Lin ; Chen Chen ; Harbin Engineering University
- 年：2017
- 作者机构：Harbin Engineering University;
- 英文论文关键词：Dynamic positioning ; nonlinear system state estimation ; particle filter ; EAKF ; importance probability density function
- 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（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TN713;U664.82
- 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：463k
- 原文格式：D
- 会议级别：全国
摘要
In order to improve the positioning accuracy and reliability of ship dynamic positioning system, a method of combing Ensemble Adjustment Kalman Filter(EAKF) and Particle Filter was proposed. It's according to the use of the max of posterior probability density to generate the importance density function of particle. So that the importance probability density function could integrate into the latest observation information and accord with the posterior probability density distribution of the true state. We can use it to deal with Gaussian and nonlinear system state estimation problem effectively. The simulation results verify the effectiveness of the algorithm.
In order to improve the positioning accuracy and reliability of ship dynamic positioning system, a method of combing Ensemble Adjustment Kalman Filter(EAKF) and Particle Filter was proposed. It's according to the use of the max of posterior probability density to generate the importance density function of particle. So that the importance probability density function could integrate into the latest observation information and accord with the posterior probability density distribution of the true state. We can use it to deal with Gaussian and nonlinear system state estimation problem effectively. The simulation results verify the effectiveness of the algorithm.
In order to improve the positioning accuracy and reliability of ship dynamic positioning system, a method of combing Ensemble Adjustment Kalman Filter(EAKF) and Particle Filter was proposed. It's according to the use of the max of posterior probability density to generate the importance density function of particle. So that the importance probability density function could integrate into the latest observation information and accord with the posterior probability density distribution of the true state. We can use it to deal with Gaussian and nonlinear system state estimation problem effectively. The simulation results verify the effectiveness of the algorithm.
引文
[1]Bian Xinqian,Fu Mingyu,Wang Yunhui.Dynamic Positioning.Beijing:Science Press,2011.
[2]Cui,Y.Kavasseri.A Particle Filter for Dynamic State Estimation in Multi-Machine Systems With Detailed Models.IEEE Trans on Power Systems.2015:1-9.
[3]Baser,Mc Donald.A Joint Multitarget Estimator for the Joint Target Detection and Tracking Filter.IEEE Trans on Signal Processing.2015:3857-3871.
[4]S.Julier,J.Uhlmann.A New Extension of the Kalman Filter to Nonlinear Systems.Proceedings of Aero Sence:The 11th International Symposium on Aerospace/Defense Sensing,Simulation and Controls,Orlando,Florida,1997.
[5]Van der Merwe R,Doucet A,Freitas N,Wan E A.The Unscented Particle Filter.Technical Report CUEDPF-INFENGPTR 380,Cambridge University,2000.
[6]Arasaratnam I,Haykon S.Cubature Kalman Filters.IEEE Transactions on Automatic Control,2009,54(6):1254-1269.
[7]Michael Merrett,Mark Zwolinski.Monet Carlo Static Timing Analysis with statistical sampling,Science Direct,2014:464-474.
[8]Hu Shiqiang,Jing Zhongliang.“Review of particle filter algorithm,”Control and Decision 2005 20(4)361-371.
[9]Simutis,Grincas.State Estimation of a Biotechnological Process Using Extended Kalman Filter and Particle Filter Veterinary and Agricultural Engineering,2014:920-924.
[10]Anderson J L.2001.An ensemble adjustment Kalman filter for data assimilation.Monthly Weather Review,129(12):2884–2903.
[11]Prashant Kumara,Kim Kwang Ikb.Hydrodynamic Modeling of Moored Ship Motion in an Irregular Domain,Procedia Engineering,2015:598—604.
[2]Cui,Y.Kavasseri.A Particle Filter for Dynamic State Estimation in Multi-Machine Systems With Detailed Models.IEEE Trans on Power Systems.2015:1-9.
[3]Baser,Mc Donald.A Joint Multitarget Estimator for the Joint Target Detection and Tracking Filter.IEEE Trans on Signal Processing.2015:3857-3871.
[4]S.Julier,J.Uhlmann.A New Extension of the Kalman Filter to Nonlinear Systems.Proceedings of Aero Sence:The 11th International Symposium on Aerospace/Defense Sensing,Simulation and Controls,Orlando,Florida,1997.
[5]Van der Merwe R,Doucet A,Freitas N,Wan E A.The Unscented Particle Filter.Technical Report CUEDPF-INFENGPTR 380,Cambridge University,2000.
[6]Arasaratnam I,Haykon S.Cubature Kalman Filters.IEEE Transactions on Automatic Control,2009,54(6):1254-1269.
[7]Michael Merrett,Mark Zwolinski.Monet Carlo Static Timing Analysis with statistical sampling,Science Direct,2014:464-474.
[8]Hu Shiqiang,Jing Zhongliang.“Review of particle filter algorithm,”Control and Decision 2005 20(4)361-371.
[9]Simutis,Grincas.State Estimation of a Biotechnological Process Using Extended Kalman Filter and Particle Filter Veterinary and Agricultural Engineering,2014:920-924.
[10]Anderson J L.2001.An ensemble adjustment Kalman filter for data assimilation.Monthly Weather Review,129(12):2884–2903.
[11]Prashant Kumara,Kim Kwang Ikb.Hydrodynamic Modeling of Moored Ship Motion in an Irregular Domain,Procedia Engineering,2015:598—604.