An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles
To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decr...
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2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/551 |
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| author | Jinchao Zhao Ya Zhang Shizhong Li Jiaxuan Wang Lingling Fang Luoyin Ning Jinghao Feng Jianwu Zhang |
| author_facet | Jinchao Zhao Ya Zhang Shizhong Li Jiaxuan Wang Lingling Fang Luoyin Ning Jinghao Feng Jianwu Zhang |
| author_sort | Jinchao Zhao |
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| description | To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution. An estimation model for system noise, the adaptive Unscented Kalman Filter (UKF) algorithm was derived in light of the maximum likelihood criterion and optimized by applying the rolling-horizon estimation method, using the Newton–Raphson algorithm for the maximum likelihood estimation of noise statistics, and it was verified by simulation experiments using the Lie group inertial navigation error model. The results indicate that, compared with the UKF algorithm and the ARUKF, the improved algorithm reduces attitude angle errors by 45%, speed errors by 44%, and three-dimensional position errors by 47%. It can better cope with complex underwater environments, effectively address the problems of low filtering accuracy and even divergence, and improve the stability of submersibles. |
| format | Article |
| id | doaj-art-28fc044132d34fc589a2d36c7f09b6ad |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-28fc044132d34fc589a2d36c7f09b6ad2025-01-24T13:49:19ZengMDPI AGSensors1424-82202025-01-0125255110.3390/s25020551An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater VehiclesJinchao Zhao0Ya Zhang1Shizhong Li2Jiaxuan Wang3Lingling Fang4Luoyin Ning5Jinghao Feng6Jianwu Zhang7College of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechatronics Engineering, North University of China, Taiyuan 030051, ChinaTo enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution. An estimation model for system noise, the adaptive Unscented Kalman Filter (UKF) algorithm was derived in light of the maximum likelihood criterion and optimized by applying the rolling-horizon estimation method, using the Newton–Raphson algorithm for the maximum likelihood estimation of noise statistics, and it was verified by simulation experiments using the Lie group inertial navigation error model. The results indicate that, compared with the UKF algorithm and the ARUKF, the improved algorithm reduces attitude angle errors by 45%, speed errors by 44%, and three-dimensional position errors by 47%. It can better cope with complex underwater environments, effectively address the problems of low filtering accuracy and even divergence, and improve the stability of submersibles.https://www.mdpi.com/1424-8220/25/2/551unscented Kalman filterinertial navigation systemunmanned underwater vehicleintegrated navigation |
| spellingShingle | Jinchao Zhao Ya Zhang Shizhong Li Jiaxuan Wang Lingling Fang Luoyin Ning Jinghao Feng Jianwu Zhang An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles Sensors unscented Kalman filter inertial navigation system unmanned underwater vehicle integrated navigation |
| title | An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles |
| title_full | An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles |
| title_fullStr | An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles |
| title_full_unstemmed | An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles |
| title_short | An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles |
| title_sort | improved unscented kalman filter applied to positioning and navigation of autonomous underwater vehicles |
| topic | unscented Kalman filter inertial navigation system unmanned underwater vehicle integrated navigation |
| url | https://www.mdpi.com/1424-8220/25/2/551 |
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