Recursive adaptive filter using current innovation for celestial navigation during the Mars approach phase

火星探测器接近段递推自适应卡尔曼滤波方法

Science China Information Sciences volume 60, Article number: 032205 (2017) Cite this article

Abstract

Celestial navigation is a commonly used autonomous navigation technique for deep space navigation. A nonlinear filter such as the unscented Kalman filter (UKF) is typically applied in a celestial navigation system (CNS). However, on account of being subject to a number of factors, such as ephemeris errors and centroid determination, the measurement model error of a CNS cannot be accurately determined. The analysis conducted in this study also shows that the measurement model error is time-variant during the Mars approach phase. This implies that covariance matrix of the measurement error R is usually inaccurate, which may induce large estimation errors that even result in filter divergence. Some adaptive methods are able to address this issue. However, traditional adaptive filters, for scaling R, usually require a sequence of innovation and are affected by the statistic window size. A new recursive adaptive UKF (RAUKF) is proposed in this paper, which only uses current innovation to scale R. The navigation performance of the proposed RAUKF method is compared with some traditional adaptive filters through simulations. The results show that this method is better than traditional adaptive filters in a CNS during the Mars approach phase.

创新点

  1. 1.

    本文对火星卫星由于质心提取引起的量测误差和火星卫星星历误差引起的量测误差进行了分析, 分析结果表明, 在火星探测器接近段, 量测模型误差是未知的时变噪声。

  2. 2.

    针对量测模型噪声方差阵(R)的时变特性, 本文提出了一种递推的自适应卡尔曼滤波方法(RAUKF), 该方法仅利用当前时刻的新息信息即可对R阵进行调节。

  3. 3.

    本文将RAUKF方法与传统无迹卡尔曼滤波(UKF), Sage-Husa滤波, 及基于新息的自适应滤波方法(AUKF)的导航精度进行了对比, 仿真结果表明, 与其他三种方法对比, 该方法可以获得最高的导航精度。

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61233005, 61503013), National Basic Research Program of China (973) (Grant No. 2014CB744206). The authors express their gratitude to all the members of the Science & Technology on Inertial Laboratory, Fundamental Science on Novel Inertial Instrument & Navigation System Technology Laboratory for their valuable comments.

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Affiliations

  1. School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing, 100191, China

    Xiaolin Ning, Zhuo Li, Yuqing Yang, Jiancheng Fang & Gang Liu

  2. Lunar Exploration and Space Program Center, China National Space Administration, Beijing, 100037, China

    Weiren Wu

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  1. Xiaolin Ning
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  2. Zhuo Li
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  3. Weiren Wu
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Correspondence to Yuqing Yang.

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Ning, X., Li, Z., Wu, W. et al. Recursive adaptive filter using current innovation for celestial navigation during the Mars approach phase. Sci. China Inf. Sci. 60, 032205 (2017). https://doi.org/10.1007/s11432-016-0405-2

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Keywords

  • deep space
  • adaptive UKF
  • navigation
  • Mars approach
  • variant noise

Keywords

  • 深空探测
  • 自适应UKF
  • 导航
  • 接近段
  • 时变噪声