An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems
Aimed at the problem of navigation performance degradation in inertial navigation system/global navigation satellite system (INS/GNSS)-integrated navigation systems due to measurement anomalies and non-Gaussian measurement noise in complex navigation environments, an adaptive unscented Kalman filter...
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MDPI AG
2025-05-01
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| author | Jiahao Zhang Kaiqiang Feng Jie Li Chunxing Zhang Xiaokai Wei |
| author_facet | Jiahao Zhang Kaiqiang Feng Jie Li Chunxing Zhang Xiaokai Wei |
| author_sort | Jiahao Zhang |
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| description | Aimed at the problem of navigation performance degradation in inertial navigation system/global navigation satellite system (INS/GNSS)-integrated navigation systems due to measurement anomalies and non-Gaussian measurement noise in complex navigation environments, an adaptive unscented Kalman filter (AUKF) algorithm based on the maximum versoria criterion (MVC) is developed. The proposed method is designed to enhance INS/GNSS-integrated navigation system robustness and accuracy by addressing the limitations of conventional filtering approaches. An adaptive unscented Kalman filter is constructed to enable dynamic adjustment of filter parameters, allowing for real-time adaptation to measurement anomalies. This ensures accurate tracking of navigation parameter states, thereby improving the robustness of the INS/GNSS-integrated navigation system in the presence of abnormal measurements. On this basis, fully considering the high-order moments of estimation errors, the maximum versoria criterion is introduced as the optimization criterion to construct a novel cost function, further effectively suppressing deviations caused by non-Gaussian disturbances and improving system navigation accuracy. The effectiveness of the proposed method was verified through vehicle navigation experiments. The experimental results demonstrate that the proposed method outperforms traditional approaches, effectively handling measurement anomalies and non-Gaussian measurement noise while maintaining robust navigation performance. Specifically, compared to the EKF, UKF, and MCCUKF, the proposed method reduces the root mean square error of velocity and position by over 60%, 50%, and 30%, respectively, under complex navigation conditions. The algorithm exhibits good accuracy and stability in complex environments, showcasing its practical applicability in real-world navigation systems. |
| format | Article |
| id | doaj-art-32aab38cbf4f4cf2984f6039708286e4 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-32aab38cbf4f4cf2984f6039708286e42025-08-20T03:46:38ZengMDPI AGSensors1424-82202025-05-012511348310.3390/s25113483An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS SystemsJiahao Zhang0Kaiqiang Feng1Jie Li2Chunxing Zhang3Xiaokai Wei4National Key Laboratory of Photoelectric Dynamic Testing Technology and Instrument in Extreme Environment, North University of China, Taiyuan 030051, ChinaNational Key Laboratory of Photoelectric Dynamic Testing Technology and Instrument in Extreme Environment, North University of China, Taiyuan 030051, ChinaNational Key Laboratory of Photoelectric Dynamic Testing Technology and Instrument in Extreme Environment, North University of China, Taiyuan 030051, ChinaNational Key Laboratory of Photoelectric Dynamic Testing Technology and Instrument in Extreme Environment, North University of China, Taiyuan 030051, ChinaSchool of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, ChinaAimed at the problem of navigation performance degradation in inertial navigation system/global navigation satellite system (INS/GNSS)-integrated navigation systems due to measurement anomalies and non-Gaussian measurement noise in complex navigation environments, an adaptive unscented Kalman filter (AUKF) algorithm based on the maximum versoria criterion (MVC) is developed. The proposed method is designed to enhance INS/GNSS-integrated navigation system robustness and accuracy by addressing the limitations of conventional filtering approaches. An adaptive unscented Kalman filter is constructed to enable dynamic adjustment of filter parameters, allowing for real-time adaptation to measurement anomalies. This ensures accurate tracking of navigation parameter states, thereby improving the robustness of the INS/GNSS-integrated navigation system in the presence of abnormal measurements. On this basis, fully considering the high-order moments of estimation errors, the maximum versoria criterion is introduced as the optimization criterion to construct a novel cost function, further effectively suppressing deviations caused by non-Gaussian disturbances and improving system navigation accuracy. The effectiveness of the proposed method was verified through vehicle navigation experiments. The experimental results demonstrate that the proposed method outperforms traditional approaches, effectively handling measurement anomalies and non-Gaussian measurement noise while maintaining robust navigation performance. Specifically, compared to the EKF, UKF, and MCCUKF, the proposed method reduces the root mean square error of velocity and position by over 60%, 50%, and 30%, respectively, under complex navigation conditions. The algorithm exhibits good accuracy and stability in complex environments, showcasing its practical applicability in real-world navigation systems.https://www.mdpi.com/1424-8220/25/11/3483INS/GNSS-integrated navigationnon-Gaussian noisemaximum versoria criterionunscented Kalman filter |
| spellingShingle | Jiahao Zhang Kaiqiang Feng Jie Li Chunxing Zhang Xiaokai Wei An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems Sensors INS/GNSS-integrated navigation non-Gaussian noise maximum versoria criterion unscented Kalman filter |
| title | An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems |
| title_full | An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems |
| title_fullStr | An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems |
| title_full_unstemmed | An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems |
| title_short | An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems |
| title_sort | adaptive unscented kalman ilter integrated navigation method based on the maximum versoria criterion for ins gnss systems |
| topic | INS/GNSS-integrated navigation non-Gaussian noise maximum versoria criterion unscented Kalman filter |
| url | https://www.mdpi.com/1424-8220/25/11/3483 |
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