An Augmented Cubature Kalman Filter for Nonlinear Dynamical Systems with Random Parameters
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- 英文论文题名：An Augmented Cubature Kalman Filter for Nonlinear Dynamical Systems with Random Parameters
- 论文作者：Xiaomei Qu ; Lei Mu
- 英文论文作者：Xiaomei Qu ; Lei Mu ; College of Computer Science and Technology ; Southwest University for Nationalities
- 年：2017
- 作者机构：College of Computer Science and Technology,Southwest University for Nationalities;
- 英文论文关键词：Cubature Kalman filter ; augmented system ; cubature point ; random parameters
- 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：O19;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：5
- 文件大小：211k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter(CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more computational complexity. However, the estimation accuracy is improved in comparison with that of the classical CKF method which uses the nominal values of the random parameters. An application to the mobile source localization with time difference of arrival(TDOA) measurements and random sensor positions is provided where the simulation results illustrate that the augmented CKF method leads to a superior performance in comparison with the classical CKF method.
In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter(CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more computational complexity. However, the estimation accuracy is improved in comparison with that of the classical CKF method which uses the nominal values of the random parameters. An application to the mobile source localization with time difference of arrival(TDOA) measurements and random sensor positions is provided where the simulation results illustrate that the augmented CKF method leads to a superior performance in comparison with the classical CKF method.
In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter(CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more computational complexity. However, the estimation accuracy is improved in comparison with that of the classical CKF method which uses the nominal values of the random parameters. An application to the mobile source localization with time difference of arrival(TDOA) measurements and random sensor positions is provided where the simulation results illustrate that the augmented CKF method leads to a superior performance in comparison with the classical CKF method.
引文
[1]I.Arasaratnam,S.Haykin and R.J.Elliott,Discrete-Time Nonlinear Filtering Algorithms Using Gauss-Hermite Quadrature,Proceedings of the IEEE,95(5):953–977,2007.
[2]C.Cheng and R.Ansari,Kernel particle filter for visual tracking,IEEE Signal Processing Letters,12(3):242–245,2005.
[3]S.Bolognani,L.Tubiana and M.Zigliotto,Extended Kalman filter tuning in sensorless PMSM drives,IEEE Transactions on Industry Applications,39(6):1741–1747,2003.
[4]S.J.Julier and J.K.Uhlmann,Unscented filtering and nonlinear estimation,Proc.IEEE,92(3):401–422,2004.
[5]K.Ito and K.Xiong,Gaussian filters for nonlinear filtering problems,IEEE Transactions on Automatic Control,45(5):910–927,2000.
[6]J.Lu and D.L.Darmofal,Higher-Dimensional Integration with Gaussian Weight for Applications in Probabilistic Design,Siam Journal on Scientific Computing,26(2):613–624,2006.
[7]I.Arasaratnam and S.Haykin,Cubature Kalman Filters,IEEE Transactions on Automatic Control,54(6):1254–1269,2009.
[8]C.Fernandez-Prades and J.Vila-Valls,Bayesian Nonlinear Filtering Using Quadrature and Cubature Rules Applied to Sensor Data Fusion for Positioning,in IEEE international conference on communications,Cape Town,South Africa,2010:1–5.
[9]W.L.De Koning,Optimal estimation of linear discrete-time systems with stochastic parameters,Automatica,20:113–115,1984.
[10]B.Sinopoli,L.Schenato,M.Franceschetti,K.Poolla,M.I.Jordan and S.S.Sastry,Kalman filtering with intermittent observations,IEEE Transactions on Automatic Control,49:1453–1464,2004.
[11]X.Qu and L.Xie,An efficient convex constrained weighted least squares source localization algorithm based on TDOA measurements,Signal Processing,119:142–152,2016.
[12]W.Wang,J.Zhou and X.Qu,A novel multiple-model treatment for maneuvering target tracking,in IEEE International Conference on Information Fusion,Heidelberg,Germany,2016:31–38.
[2]C.Cheng and R.Ansari,Kernel particle filter for visual tracking,IEEE Signal Processing Letters,12(3):242–245,2005.
[3]S.Bolognani,L.Tubiana and M.Zigliotto,Extended Kalman filter tuning in sensorless PMSM drives,IEEE Transactions on Industry Applications,39(6):1741–1747,2003.
[4]S.J.Julier and J.K.Uhlmann,Unscented filtering and nonlinear estimation,Proc.IEEE,92(3):401–422,2004.
[5]K.Ito and K.Xiong,Gaussian filters for nonlinear filtering problems,IEEE Transactions on Automatic Control,45(5):910–927,2000.
[6]J.Lu and D.L.Darmofal,Higher-Dimensional Integration with Gaussian Weight for Applications in Probabilistic Design,Siam Journal on Scientific Computing,26(2):613–624,2006.
[7]I.Arasaratnam and S.Haykin,Cubature Kalman Filters,IEEE Transactions on Automatic Control,54(6):1254–1269,2009.
[8]C.Fernandez-Prades and J.Vila-Valls,Bayesian Nonlinear Filtering Using Quadrature and Cubature Rules Applied to Sensor Data Fusion for Positioning,in IEEE international conference on communications,Cape Town,South Africa,2010:1–5.
[9]W.L.De Koning,Optimal estimation of linear discrete-time systems with stochastic parameters,Automatica,20:113–115,1984.
[10]B.Sinopoli,L.Schenato,M.Franceschetti,K.Poolla,M.I.Jordan and S.S.Sastry,Kalman filtering with intermittent observations,IEEE Transactions on Automatic Control,49:1453–1464,2004.
[11]X.Qu and L.Xie,An efficient convex constrained weighted least squares source localization algorithm based on TDOA measurements,Signal Processing,119:142–152,2016.
[12]W.Wang,J.Zhou and X.Qu,A novel multiple-model treatment for maneuvering target tracking,in IEEE International Conference on Information Fusion,Heidelberg,Germany,2016:31–38.