Strong tracking Kalman filter for non-Gaussian observation

被引：0
|
作者：
Lü D.-H. [1 ]
Wang J.-Q. [1 ]
Xiong K. [2 ]
Hou B.-W. [1 ]
He Z.-M. [1 ]
机构：
[1] College of Liberal Arts and Science, National University of Defense Technology, Changsha, 410073, Hunan
[2] Beijing Institute of Control Engineering, Beijing
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Filter performance; Kalman filter; Non-Gaussian observation noise; Strong tracking filter;
D O I：
10.7641/CTA.2019.90535
中图分类号：
学科分类号：
摘要：
Under Gaussian noise, Kalman Filter (KF) can obtain the uniformly minimum variance linear unbiased estimation of system state. However, when the noise is non-Gaussian, the performance of KF will degrade seriously. Non-Gaussian phenomena of observation noise are often encountered in autonomous navigation of deep space exploration. However, the existing models may have drawbacks such as low accuracy, low stability or high computational complexity. In view of this situation, based on the orthogonal principle of innovation in traditional strong tracking Kalman filter (STKF), strong tracking Kalman filter for non-Gaussian observation (STKFNO) which is applicable to processing non-Gaussian observation noise is derived. By embedding STKFNO into the framework of unscented Kalman filter(UKF), strong tracking unscented Kalman filter for non-Gaussian observation (STUKFNO) suitable for dealing with non-Gaussian noise of nonlinear systems is also established. The proposed algorithm is applied to a deep space optical autonomous navigation system. The simulation results demonstrate that the proposed algorithm is effective in disposing of non-Gaussian observation noise. © 2019, Editorial Department of Control Theory & Applications. All right reserved.
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页码：1997 / 2004
页数：7
相关论文
共 24 条
  • [1] Kalman R.E., A new approach to linear filtering and prediction problems, Journal of Basic Engineering, 82, 1, pp. 35-45, (1960)
  • [2] Allotta B., Caiti A., Costanzi R., Et al., A new AUV navigation system exploiting unscented Kalman filter, Ocean Engineering, 113, pp. 121-132, (2016)
  • [3] Foo G.H.B., Zhang X., Vilthgamuwa D.M., A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter, IEEE Transactions on Industrial Electronics, 60, 8, pp. 3485-3495, (2013)
  • [4] Zhu W., Wang W., Yuan G., An improved interacting multiple model filtering algorithm based on cubature Kalman filter for maneuvering target tracking, Sensors, 16, 6, pp. 805-816, (2016)
  • [5] Anderson B., Moore J.B., Optimal filtering, Prentice-Hall Information and Sciences Series, (1979)
  • [6] Roth M., Gustafsson F., An efficient implementation of the second order extended Kalman filter, The 14th International Conference on Information Fusion, pp. 1-6, (2011)
  • [7] Julier S., Uhlmann J., Durrant-Whyte H.F., A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Transactions on Automatic Control, 45, 3, pp. 477-482, (2000)
  • [8] Arassaratnam I., Haykin S., An efficient implementation of the second order extended Kalman filter, IEEE Transactions on Automatic Control, 54, 6, pp. 1254-1269, (2011)
  • [9] Xiong K., Wei C., Liu L., Application of Robust Filtering Technology in Pulsar Navigation, Space Control Technology and Application, 34, 6, pp. 9-11, (2008)
  • [10] Gao A., Robust Estimation and Guidance Method for Planetary Autonomous Landing Based on Optical Information, (2012)
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