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Redundant Adaptive Robust Tracking for Active Satellite

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Non-Cooperative Target Tracking, Fusion and Control

Part of the book series: Information Fusion and Data Science ((IFDS))

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Abstract

In this chapter, a switched H ∞ robust filtering method is discussed and applied into a type of noncooperative target tracking problem, named active satellite tracking. To describe the orbital relative motion, a state model based on differential orbital elements (DOE) and a measurement model using unbiased converted measurements (UCM) are established first. Then, the switched H ∞ robust filter to be presented is followed, which we called the redundant adaptive robust extended Kalman filter (RAREKF). The filtering method has a redundancy to system uncertainties such as modeling errors and disturbances, so that the unnecessary loss of filtering optimality, that is, conservativeness of the traditional H ∞ filtering, can be improved remarkably. Through theoretical analysis and numerical simulation, it is verified that RAREKF can achieve better tracking performance than other compared typical filters. Additionally, an error index function considering both tracking model and filtering algorithm is also presented for evaluating the overall tracking method.

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Acknowledgements

The work was supported in part by China Natural Science Foundation (No. 60775022 and No. 60674107).

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Zhongliang Jing & Han Pan

  2. University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yuankai Li

  3. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Peng Dong

Authors
  1. Zhongliang Jing
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  2. Han Pan
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  3. Yuankai Li
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  4. Peng Dong
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Jing, Z., Pan, H., Li, Y., Dong, P. (2018). Redundant Adaptive Robust Tracking for Active Satellite. In: Non-Cooperative Target Tracking, Fusion and Control. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-90716-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-90716-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90715-4

  • Online ISBN: 978-3-319-90716-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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