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Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems.

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Title: Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems.
Authors: Li, Shoupeng1 (AUTHOR) lishoupeng403@outlook.com, Mu, Rongjun1 (AUTHOR) murjun@hit.edu.cn, Cui, Naigang1 (AUTHOR) cui_naigang@163.com
Source: Aerospace Science & Technology. Apr2022, Vol. 123, pN.PAG-N.PAG. 1p.
Subjects: Kalman filtering, Information filtering, Multisensor data fusion, Monte Carlo method, Filters & filtration, Noise
Abstract: For a highly nonlinear system with additive process and observation noises, the non-augmented sigma-point nonlinear filter induces a loss of odd-order moment information (skewness), thereby resulting in a degradation of state estimation accuracy. To address this problem, we present a novel filtering algorithm, namely augmented cubature information filter (ACIF). The adopted augmentation strategy can facilitate the estimator to capture and propagate higher odd-order moment information of random variables. In addition, the covariance resulting from linearization errors is compensated based on the proposed filtering framework. Further, the ACIF is extended to the decentralized multi-sensor system. The employed modified state augmentation strategy can eliminate the inconsistency of covariance estimation induced by the state-mean samples. And the propagation of cubature points is simplified to reduce the computational cost associated with state augmentation. To validate the proposed algorithm, a comparative study is performed via Monte Carlo simulation in the scenario of tracking a maneuvering target. The results show that the simplified ACIF can improve the filtering efficiency without dramatically increasing the computational burden. [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: 156078036
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PubTypeId: academicJournal
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  Data: Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems.
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  Data: <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="%22Mu%2C+Rongjun%22">Mu, Rongjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> murjun@hit.edu.cn</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>. Apr2022, Vol. 123, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Information+filtering%22">Information filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Filters+%26+filtration%22">Filters & filtration</searchLink><br /><searchLink fieldCode="DE" term="%22Noise%22">Noise</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: For a highly nonlinear system with additive process and observation noises, the non-augmented sigma-point nonlinear filter induces a loss of odd-order moment information (skewness), thereby resulting in a degradation of state estimation accuracy. To address this problem, we present a novel filtering algorithm, namely augmented cubature information filter (ACIF). The adopted augmentation strategy can facilitate the estimator to capture and propagate higher odd-order moment information of random variables. In addition, the covariance resulting from linearization errors is compensated based on the proposed filtering framework. Further, the ACIF is extended to the decentralized multi-sensor system. The employed modified state augmentation strategy can eliminate the inconsistency of covariance estimation induced by the state-mean samples. And the propagation of cubature points is simplified to reduce the computational cost associated with state augmentation. To validate the proposed algorithm, a comparative study is performed via Monte Carlo simulation in the scenario of tracking a maneuvering target. The results show that the simplified ACIF can improve the filtering efficiency without dramatically increasing the computational burden. [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:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.ast.2022.107445
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Information filtering
        Type: general
      – SubjectFull: Multisensor data fusion
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Filters & filtration
        Type: general
      – SubjectFull: Noise
        Type: general
    Titles:
      – TitleFull: Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems.
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            NameFull: Li, Shoupeng
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            NameFull: Mu, Rongjun
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            NameFull: Cui, Naigang
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          Dates:
            – D: 01
              M: 04
              Text: Apr2022
              Type: published
              Y: 2022
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              Value: 123
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            – TitleFull: Aerospace Science & Technology
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