The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error
Although the Unscented Kalman Filter (UKF) generally outperforms the Extended Kalman Filter (EKF) due to its superior approximation of nonlinear state transition functions, the core Unscented Transform (UT) still cannot precisely capture complex nonlinear dynamics. This inherent limitation introduce...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11096602/ |
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| author | Ziran Luo Chenglin Wen |
| author_facet | Ziran Luo Chenglin Wen |
| author_sort | Ziran Luo |
| collection | DOAJ |
| description | Although the Unscented Kalman Filter (UKF) generally outperforms the Extended Kalman Filter (EKF) due to its superior approximation of nonlinear state transition functions, the core Unscented Transform (UT) still cannot precisely capture complex nonlinear dynamics. This inherent limitation introduces non negligible truncation errors into state estimation, leading to significantly degraded accuracy in practical applications such as high-maneuvering target tracking, robotic localization in complex environments, and high-precision inertial navigation. Crucially, UKF performance deteriorates further as system non-linearity intensifies, exemplified by scenarios like aggressive UAV maneuvers in strong winds or rapid spacecraft attitude adjustments. To address this challenge, this paper proposes a novel estimation algorithm that explicitly accounts for the UT truncation error. We developed an improved UKF method that uses this error estimate. Rigorous performance analysis and numerical simulations closely replicating real-world scenarios demonstrate the proposed method’s significant effectiveness in boosting estimation accuracy for highly complex nonlinear systems. |
| format | Article |
| id | doaj-art-6b9b6bf6a2b8489783249c4b57e9b2f7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6b9b6bf6a2b8489783249c4b57e9b2f72025-08-20T03:40:59ZengIEEEIEEE Access2169-35362025-01-011313555813556610.1109/ACCESS.2025.359281311096602The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation ErrorZiran Luo0https://orcid.org/0009-0000-4549-5949Chenglin Wen1https://orcid.org/0009-0000-6391-1168School of Automation, Guangdong University of Petrochemical Technology, Maoming, ChinaSchool of Automation, Guangdong University of Petrochemical Technology, Maoming, ChinaAlthough the Unscented Kalman Filter (UKF) generally outperforms the Extended Kalman Filter (EKF) due to its superior approximation of nonlinear state transition functions, the core Unscented Transform (UT) still cannot precisely capture complex nonlinear dynamics. This inherent limitation introduces non negligible truncation errors into state estimation, leading to significantly degraded accuracy in practical applications such as high-maneuvering target tracking, robotic localization in complex environments, and high-precision inertial navigation. Crucially, UKF performance deteriorates further as system non-linearity intensifies, exemplified by scenarios like aggressive UAV maneuvers in strong winds or rapid spacecraft attitude adjustments. To address this challenge, this paper proposes a novel estimation algorithm that explicitly accounts for the UT truncation error. We developed an improved UKF method that uses this error estimate. Rigorous performance analysis and numerical simulations closely replicating real-world scenarios demonstrate the proposed method’s significant effectiveness in boosting estimation accuracy for highly complex nonlinear systems.https://ieeexplore.ieee.org/document/11096602/Nonlinear Gaussian systemtruncation error estimationleast squares methodUKF |
| spellingShingle | Ziran Luo Chenglin Wen The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error IEEE Access Nonlinear Gaussian system truncation error estimation least squares method UKF |
| title | The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error |
| title_full | The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error |
| title_fullStr | The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error |
| title_full_unstemmed | The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error |
| title_short | The Design Method of Enhanced Unscented Kalman Filter Considering UT Transform Truncation Error |
| title_sort | design method of enhanced unscented kalman filter considering ut transform truncation error |
| topic | Nonlinear Gaussian system truncation error estimation least squares method UKF |
| url | https://ieeexplore.ieee.org/document/11096602/ |
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