Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation.
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| Title: | Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation. |
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| Authors: | CAO, Longpan1, ZHOU, Xin2, SI, Yongbo2, YAN, Yuqian2, CHEN, Guangwu1,2 cgwyjh1976@126.com |
| Source: | Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p243-253. 11p. |
| Subjects: | Kalman filtering, Global Positioning System, Random noise theory, Wireless geolocation systems, Multisensor data fusion, Outlier detection, Monte Carlo method |
| Abstract: | The ensemble Kalman filter (EnKF) has emerged as a popular data fusion filtering method in vehicle-mounted global navigation satellite system/strapdown inertial navigation system (GNSS/SINS) integrated navigation systems. It employs Monte Carlo methods based on sample estimates to approximate the system's state distribution. However, the EnKF typically assumes a Gaussian distribution for the state distribution, and this assumption may fail in non-Gaussian scenarios. To address this issue, this paper proposes a Cauchy robust ensemble Kalman filter (CREnKF) that dynamically identifies and suppresses outliers through the Cauchy weighting function, and reduces the impact of non-Gaussian noise by combining residual direct weighting and observation covariance reconstruction dual-path robustness strategies. The algorithm was applied to a GNSS/SINS integrated navigation system and tested through simulation experiments and in-vehicle experiments. The experimental results show that the position RMSE of this scheme in a non-Gaussian noise environment is decreased by 82%, 81%, and 63% relative to EKF, EnKF, and EnKF robust with Huber Kernel function, respectively, effectively enhancing the positioning accuracy of the integrated navigation system. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation 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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| Items | – Name: Title Label: Title Group: Ti Data: Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22CAO%2C+Longpan%22">CAO, Longpan</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZHOU%2C+Xin%22">ZHOU, Xin</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22SI%2C+Yongbo%22">SI, Yongbo</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22YAN%2C+Yuqian%22">YAN, Yuqian</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22CHEN%2C+Guangwu%22">CHEN, Guangwu</searchLink><relatesTo>1,2</relatesTo><i> cgwyjh1976@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Measurement+Science+%26+Instrumentation%22">Journal of Measurement Science & Instrumentation</searchLink>. Jun2026, Vol. 17 Issue 2, p243-253. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+geolocation+systems%22">Wireless geolocation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The ensemble Kalman filter (EnKF) has emerged as a popular data fusion filtering method in vehicle-mounted global navigation satellite system/strapdown inertial navigation system (GNSS/SINS) integrated navigation systems. It employs Monte Carlo methods based on sample estimates to approximate the system's state distribution. However, the EnKF typically assumes a Gaussian distribution for the state distribution, and this assumption may fail in non-Gaussian scenarios. To address this issue, this paper proposes a Cauchy robust ensemble Kalman filter (CREnKF) that dynamically identifies and suppresses outliers through the Cauchy weighting function, and reduces the impact of non-Gaussian noise by combining residual direct weighting and observation covariance reconstruction dual-path robustness strategies. The algorithm was applied to a GNSS/SINS integrated navigation system and tested through simulation experiments and in-vehicle experiments. The experimental results show that the position RMSE of this scheme in a non-Gaussian noise environment is decreased by 82%, 81%, and 63% relative to EKF, EnKF, and EnKF robust with Huber Kernel function, respectively, effectively enhancing the positioning accuracy of the integrated navigation system. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation 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: 10.62756/jmsi.1674-8042.2026021 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 243 Subjects: – SubjectFull: Kalman filtering Type: general – SubjectFull: Global Positioning System Type: general – SubjectFull: Random noise theory Type: general – SubjectFull: Wireless geolocation systems Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Monte Carlo method Type: general Titles: – TitleFull: Improved ensemble Kalman filter algorithm based on GNSS/SINS integrated navigation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: CAO, Longpan – PersonEntity: Name: NameFull: ZHOU, Xin – PersonEntity: Name: NameFull: SI, Yongbo – PersonEntity: Name: NameFull: YAN, Yuqian – PersonEntity: Name: NameFull: CHEN, Guangwu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16748042 Numbering: – Type: volume Value: 17 – Type: issue Value: 2 Titles: – TitleFull: Journal of Measurement Science & Instrumentation Type: main |
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