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Modified unscented particle filter for nonlinear Bayesian tracking 被引量：14

Modified unscented particle filter for nonlinear Bayesian tracking
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摘要 A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one. A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.
作者 Zhan Ronghui Xin Qin Wan Jianwei
机构地区 School of Electronic Science and Engineering
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第1期7-14,共8页 系统工程与电子技术（英文版）
关键词 Bayesian estimation modified unscented particle filter nonlinear filtering unscented Kalman filter Bayesian estimation, modified unscented particle filter, nonlinear filtering, unscented Kalman filter
分类号 TN713 [电子电信—电路与系统]
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参考文献11

  • 1Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with application to tracking and navigation: theory, algorithm, and software. New York: Wiley, 2001.
  • 2Julier J, Uhlmann K. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control, 2000, 45(3): 477- 482.
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  • 8Rui Y, Chen Y. Better proposal distributions: object tracking using unscented particle filter. Proc. IEEE Conf. on. Computer Vision and Pattern Recognition, Kauai, Hawaii, 2001: 786-793.
  • 9Merwe van der R. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. Electrical and Computer Engineering Ph.D. Dissertation, Oregon Health Sciences Univ., Portland, OR, 2004.
  • 10Merwe van der R, Wan E A. The square-root unscented Kalman filter for state and parameter-estimation, in Proc. Int. Conf. Acoust., Speech, Signal Process., Salt Lake City, UT. 2001: 3461-3464.

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Journal of Systems Engineering and Electronics
2008年 第1期

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