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Variational Bayesian adaptive high‐degree cubature Huber‐based filter for vision‐aided inertial navigation on asteroid missions.

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Title: Variational Bayesian adaptive high‐degree cubature Huber‐based filter for vision‐aided inertial navigation on asteroid missions.
Authors: Su, Bingzhi1 (AUTHOR), Mu, Rongjun1 (AUTHOR) murjun@hit.edu.cn, Long, Teng1 (AUTHOR), Li, Yuntian1 (AUTHOR), Cui, Naigang1 (AUTHOR)
Source: IET Radar, Sonar & Navigation (Wiley-Blackwell). Sep2020, Vol. 14 Issue 9, p1391-1401. 11p.
Subjects: Statistical measurement, Kalman filtering, Random noise theory, Robot vision, Robust control, Asteroids
Abstract: Vision‐aided inertial navigation (VAIN) is a prospective technique for determining the pose of the spacecraft during asteroid missions. The VAIN system can fuse the inertial and visual data by employing the high‐degree cubature Kalman filter (HCKF) because it can accurately handle non‐linear problems. However, the visual measurements can be corrupted by non‐Gaussian noise with unknown time‐varying covariance, resulting in severe degradation of the HCKF. To improve the navigational accuracy of the spacecraft in these situations, the authors propose a novel adaptive robust HCKF known as variational Bayesian (VB) adaptive high‐degree cubature Huber‐based filter (VB‐AHCHF). In the novel algorithm, the fifth‐degree cubature rule and VB theory are combined to estimate the state and track the non‐stationary statistical characteristics of the measurement noise. In addition, utilising the M‐estimation, which is defined as the Huber technique, it modifies the update step of the formal Bayesian filtering. Therefore, the VB‐AHCHF can exhibit adaptability and robustness to the covariance uncertainty and non‐Gaussianity of the measurement noise. Their simulation results show that the estimation accuracy of VB‐AHCHF, as well as its adaptability and robustness, is superior to all state‐of‐the‐art algorithms, e.g. HCKF, high‐degree cubature Huber‐based filter, and the VB adaptive HCKF. [ABSTRACT FROM AUTHOR]
Copyright of IET Radar, Sonar & Navigation (Wiley-Blackwell) is the property of Wiley-Blackwell 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: 183865481
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PubTypeId: academicJournal
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  Data: <searchLink fieldCode="DE" term="%22Statistical+measurement%22">Statistical measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+vision%22">Robot vision</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Asteroids%22">Asteroids</searchLink>
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  Data: Vision‐aided inertial navigation (VAIN) is a prospective technique for determining the pose of the spacecraft during asteroid missions. The VAIN system can fuse the inertial and visual data by employing the high‐degree cubature Kalman filter (HCKF) because it can accurately handle non‐linear problems. However, the visual measurements can be corrupted by non‐Gaussian noise with unknown time‐varying covariance, resulting in severe degradation of the HCKF. To improve the navigational accuracy of the spacecraft in these situations, the authors propose a novel adaptive robust HCKF known as variational Bayesian (VB) adaptive high‐degree cubature Huber‐based filter (VB‐AHCHF). In the novel algorithm, the fifth‐degree cubature rule and VB theory are combined to estimate the state and track the non‐stationary statistical characteristics of the measurement noise. In addition, utilising the M‐estimation, which is defined as the Huber technique, it modifies the update step of the formal Bayesian filtering. Therefore, the VB‐AHCHF can exhibit adaptability and robustness to the covariance uncertainty and non‐Gaussianity of the measurement noise. Their simulation results show that the estimation accuracy of VB‐AHCHF, as well as its adaptability and robustness, is superior to all state‐of‐the‐art algorithms, e.g. HCKF, high‐degree cubature Huber‐based filter, and the VB adaptive HCKF. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IET Radar, Sonar & Navigation (Wiley-Blackwell) is the property of Wiley-Blackwell 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:
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      – Type: doi
        Value: 10.1049/iet-rsn.2020.0024
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1391
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      – SubjectFull: Statistical measurement
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Random noise theory
        Type: general
      – SubjectFull: Robot vision
        Type: general
      – SubjectFull: Robust control
        Type: general
      – SubjectFull: Asteroids
        Type: general
    Titles:
      – TitleFull: Variational Bayesian adaptive high‐degree cubature Huber‐based filter for vision‐aided inertial navigation on asteroid missions.
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            NameFull: Su, Bingzhi
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            NameFull: Mu, Rongjun
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            NameFull: Long, Teng
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            NameFull: Li, Yuntian
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            – D: 01
              M: 09
              Text: Sep2020
              Type: published
              Y: 2020
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              Value: 14
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