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Quadrature Kalman particle fitler 被引量：4

Quadrature Kalman particle fitler
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摘要 In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm. In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm.
作者 Chunlincl Wu Chongzhao Han
机构地区 School of Electronic and Control Engineering School of Electronic and Information Engineering
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期175-179,共5页 系统工程与电子技术（英文版）
基金 supported by the National Natural Science Foundation of China(60574033)
关键词 particle filter statistical linear regression quadrature Kalman filter importance density function. particle filter, statistical linear regression, quadrature Kalman filter, importance density function.
分类号 TP277 [自动化与计算机技术—检测技术与自动化装置] TN713 [电子电信—电路与系统]
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参考文献11

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同被引文献37

  • 1范文兵,刘春风,张素贞.一种强跟踪扩展卡尔曼滤波器的改进算法[J].控制与决策,2006,21(1):73-76. 被引量：28
  • 2侯志强,韩崇昭.视觉跟踪技术综述[J].自动化学报,2006,32(4):603-617. 被引量：257
  • 3Brasnett P,Mihaylova L,Bull D,et al.Sequential Monte Carlo tracking by fusing multiple cues in video sequences[J].Image and Vision Computing,2007,25(8):1217-1227.
  • 4Doucet A,Godsill S J,Andrieu C.On sequential Monte Carlo sampling methods for Bayesian filtering[J].Statistics and Computing,2000,10(3):197-208.
  • 5Julier S J,Uhlmann J K.Unscented filtering and nonlinear estimation[J].IEEE Trans on Signal Processing,2004,92(3):401-422.
  • 6Arulampalam M S,Maskell S,Gordon N,et al.A tutorial on particle filters for on line non-linear/non-Gaussian Bayesian tracking[J].IEEE Transactions on Signal Pro-cessing,2002,50(2):174-188.
  • 7Arasaratnam I,Haykin S,Elliott R J.Discrete-time non-linear filtering algorithms using Gauss-Hermite quadra-ture[J].Proc of the IEEE,2007,95(5):953-977.
  • 8van der Merwe R.A doucet the unscented particle filter advance in neural information processing systems[M].[S.l.]:MIT,2000.
  • 9MORRIS B T,TRIVEDI M M.Contextextual activity visualization from long-term video observations[J].IEEE Intelligent Systerms,2010,25(3):50-62.
  • 10LIU P R,MENG M Q H,LIU P X,et al.Optical flow and active contour for moving object segmentation and detection in monocular ro-bot[C]//Proc of IEEE International Conference on Robotics and Au-tomation.Washigton DC:IEEE Computer Society,2006:4075-4080.

引证文献4

二级引证文献1

Journal of Systems Engineering and Electronics
2010年 第2期

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