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An improved adaptive robust information filter for spacecraft relative navigation.

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Title: An improved adaptive robust information filter for spacecraft relative navigation.
Authors: Chu, Yanfeng1 (AUTHOR) chuyanfeng_hit@163.com, Mu, Rongjun1 (AUTHOR) murjun@hit.edu.cn, Li, Shoupeng1 (AUTHOR) lishoupeng403@outlook.com, Cui, Naigang1 (AUTHOR) cui_naigang@163.com
Source: Aerospace Science & Technology. Nov2022, Vol. 130, pN.PAG-N.PAG. 1p.
Subjects: Information filtering, Kalman filtering, Monte Carlo method, Space vehicles, Adaptive filters, Air filters
Abstract: This work concentrates on addressing the problem of time-varying measurement noise covariance and outliers (contaminated Gaussian distribution), which can significantly deteriorate the estimation accuracy of conventional Kalman filter. Adaptive filters or robust filters are only suitable for solving one of the above-mentioned problems. Meanwhile, the existing adaptive robust filters, performing unsatisfactorily, also can not meet the requirements of spacecraft relative navigation, which needs an accurate and reliable filter. This paper proposes an improved adaptive robust information filter, referred to as variational Bayesian (VB) adaptive dynamic-covariance-scaling (DCS)-based cubature information filter (VB-DCSCIF). The proposed filter, based on the cubature information filter framework, uses VB approximation to track measurement noise covariance with time-varying statistical characteristics, and relies on the improved DCS kernel function to suppress outliers in measurements. Therefore, VB-DCSCIF incorporates the advantages of VB approximation and the improved DCS kernel, exhibiting adaptivity and robustness. Spacecraft relative navigation simulations and Monte Carlo simulations demonstrate that VB-DCSCIF outperforms other filters for providing high-precision state estimation under time-varying measurement noise covariance and outliers. [ABSTRACT FROM AUTHOR]
Copyright of Aerospace Science & Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 160315131
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: An improved adaptive robust information filter for spacecraft relative navigation.
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  Data: <searchLink fieldCode="AR" term="%22Chu%2C+Yanfeng%22">Chu, Yanfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chuyanfeng_hit@163.com</i><br /><searchLink fieldCode="AR" term="%22Mu%2C+Rongjun%22">Mu, Rongjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> murjun@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Shoupeng%22">Li, Shoupeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lishoupeng403@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Cui%2C+Naigang%22">Cui, Naigang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cui_naigang@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Aerospace+Science+%26+Technology%22">Aerospace Science & Technology</searchLink>. Nov2022, Vol. 130, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Information+filtering%22">Information filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Space+vehicles%22">Space vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink><br /><searchLink fieldCode="DE" term="%22Air+filters%22">Air filters</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This work concentrates on addressing the problem of time-varying measurement noise covariance and outliers (contaminated Gaussian distribution), which can significantly deteriorate the estimation accuracy of conventional Kalman filter. Adaptive filters or robust filters are only suitable for solving one of the above-mentioned problems. Meanwhile, the existing adaptive robust filters, performing unsatisfactorily, also can not meet the requirements of spacecraft relative navigation, which needs an accurate and reliable filter. This paper proposes an improved adaptive robust information filter, referred to as variational Bayesian (VB) adaptive dynamic-covariance-scaling (DCS)-based cubature information filter (VB-DCSCIF). The proposed filter, based on the cubature information filter framework, uses VB approximation to track measurement noise covariance with time-varying statistical characteristics, and relies on the improved DCS kernel function to suppress outliers in measurements. Therefore, VB-DCSCIF incorporates the advantages of VB approximation and the improved DCS kernel, exhibiting adaptivity and robustness. Spacecraft relative navigation simulations and Monte Carlo simulations demonstrate that VB-DCSCIF outperforms other filters for providing high-precision state estimation under time-varying measurement noise covariance and outliers. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Aerospace Science & Technology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ast.2022.107873
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Information filtering
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Space vehicles
        Type: general
      – SubjectFull: Adaptive filters
        Type: general
      – SubjectFull: Air filters
        Type: general
    Titles:
      – TitleFull: An improved adaptive robust information filter for spacecraft relative navigation.
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            NameFull: Chu, Yanfeng
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            NameFull: Mu, Rongjun
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            NameFull: Li, Shoupeng
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            NameFull: Cui, Naigang
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          Dates:
            – D: 01
              M: 11
              Text: Nov2022
              Type: published
              Y: 2022
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              Value: 12709638
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              Value: 130
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            – TitleFull: Aerospace Science & Technology
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