Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF
This paper addresses the issue of limited timing accuracy in complex environments for both the BeiDou system (BD) and the long-range navigation system (LORAN). We delve into the correction algorithm for LORAN timing signals and an adaptive fusion timing algorithm based on the extended Kalman filter...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/246 |
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| _version_ | 1832587596143788032 |
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| author | Xiaolong Guan Jianfeng Wu Yuji Li Zhibo Zhou Yan Xing Huabing Wu Aiping Zhao |
| author_facet | Xiaolong Guan Jianfeng Wu Yuji Li Zhibo Zhou Yan Xing Huabing Wu Aiping Zhao |
| author_sort | Xiaolong Guan |
| collection | DOAJ |
| description | This paper addresses the issue of limited timing accuracy in complex environments for both the BeiDou system (BD) and the long-range navigation system (LORAN). We delve into the correction algorithm for LORAN timing signals and an adaptive fusion timing algorithm based on the extended Kalman filter (EKF). First, we introduce the advantages and limitations of the BD and LORAN systems in timing applications, as well as the principles of the EKF algorithm and its application in multisource information fusion. Next, we propose a correction algorithm signal to address the significant fluctuations in LORAN timing signals. Building on this, we continue to study an adaptive BD and LORAN fusion timing algorithm based on the EKF. This involves optimising system noise covariance through adaptive adjustments and establishing a fusion timing algorithm model based on the EKF. Finally, we construct a test platform to verify the effectiveness of the proposed algorithms. The experimental results demonstrate that, compared to a single navigation system, the adaptive BD and LORAN fusion timing algorithm based on the EKF significantly improves the accuracy and stability of system timing. Additionally, correcting the LORAN timing results before fusion further enhances system fusion timing performance metrics. The algorithm still maintains high performance in complex environments, showing great application prospects. |
| format | Article |
| id | doaj-art-c2b53753cb334b118260f88c86beb4b5 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c2b53753cb334b118260f88c86beb4b52025-01-24T13:47:51ZengMDPI AGRemote Sensing2072-42922025-01-0117224610.3390/rs17020246Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKFXiaolong Guan0Jianfeng Wu1Yuji Li2Zhibo Zhou3Yan Xing4Huabing Wu5Aiping Zhao6National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaThis paper addresses the issue of limited timing accuracy in complex environments for both the BeiDou system (BD) and the long-range navigation system (LORAN). We delve into the correction algorithm for LORAN timing signals and an adaptive fusion timing algorithm based on the extended Kalman filter (EKF). First, we introduce the advantages and limitations of the BD and LORAN systems in timing applications, as well as the principles of the EKF algorithm and its application in multisource information fusion. Next, we propose a correction algorithm signal to address the significant fluctuations in LORAN timing signals. Building on this, we continue to study an adaptive BD and LORAN fusion timing algorithm based on the EKF. This involves optimising system noise covariance through adaptive adjustments and establishing a fusion timing algorithm model based on the EKF. Finally, we construct a test platform to verify the effectiveness of the proposed algorithms. The experimental results demonstrate that, compared to a single navigation system, the adaptive BD and LORAN fusion timing algorithm based on the EKF significantly improves the accuracy and stability of system timing. Additionally, correcting the LORAN timing results before fusion further enhances system fusion timing performance metrics. The algorithm still maintains high performance in complex environments, showing great application prospects.https://www.mdpi.com/2072-4292/17/2/246BeiDou systemlong-range navigation systemextended Kalman filteradaptive fusiontiming algorithm |
| spellingShingle | Xiaolong Guan Jianfeng Wu Yuji Li Zhibo Zhou Yan Xing Huabing Wu Aiping Zhao Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF Remote Sensing BeiDou system long-range navigation system extended Kalman filter adaptive fusion timing algorithm |
| title | Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF |
| title_full | Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF |
| title_fullStr | Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF |
| title_full_unstemmed | Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF |
| title_short | Research on the Adaptive Fusion Timing Algorithm for BeiDou and LORAN Based on the EKF |
| title_sort | research on the adaptive fusion timing algorithm for beidou and loran based on the ekf |
| topic | BeiDou system long-range navigation system extended Kalman filter adaptive fusion timing algorithm |
| url | https://www.mdpi.com/2072-4292/17/2/246 |
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