A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory
The accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditional gross error detection methods assume that real-state in...
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/4758 |
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| author | Ao Sun Qiuzhao Zhang Zhangjun Yu Xiaolin Meng Xin Liu Yunlong Zhang Yilin Xie |
| author_facet | Ao Sun Qiuzhao Zhang Zhangjun Yu Xiaolin Meng Xin Liu Yunlong Zhang Yilin Xie |
| author_sort | Ao Sun |
| collection | DOAJ |
| description | The accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditional gross error detection methods assume that real-state information is known, and they need to establish state iterators, which leads to low computational efficiency. Meanwhile, in multi-sensor fusion, if the sampling rates are different, the change in the dimension of the observation matrix must be considered, and the calculation is complex. Based on state-domain consistency theory, this paper proposes the State-domain Robust Autonomous Integrity Monitoring by Extrapolation (SRAIME) method for identifying slow-growing gross errors for GNSS/accelerometer integrated deformation monitoring. Compared with the traditional gross error detection method, the proposed method constructs state test statistics based on the state estimated value and the state predicted value, and it directly performs gross error identification in the state domain. This paper deduces the feasibility of the proposed method theoretically and verifies that the proposed method does not need to consider the dimension change of the observation matrix in gross error detection. Meanwhile, in the excitation deformation experiments of the Suntuan River Bridge in Anhui and the Wilford Bridge in the United Kingdom, the slow gradient of the slope was added to the measurement domain, and the traditional AIME method and the method proposed in this paper were adopted for the gross error identification of the GNSS/accelerometer fusion process. The results demonstrate that both methods can effectively detect gross errors, but the proposed method does not need to consider the dimensional change in the observation matrix during the fusion process, which has better applicability to GNSS/accelerometer integrated deformation monitoring. |
| first_indexed | 2024-03-09T21:13:35Z |
| format | Article |
| id | doaj.art-0005702068d24359913ae48d77b65c15 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:13:35Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0005702068d24359913ae48d77b65c152023-11-23T21:38:12ZengMDPI AGRemote Sensing2072-42922022-09-011419475810.3390/rs14194758A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency TheoryAo Sun0Qiuzhao Zhang1Zhangjun Yu2Xiaolin Meng3Xin Liu4Yunlong Zhang5Yilin Xie6School of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, ChinaSchool of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, ChinaNational Engineering Research Center for Digital Construction and Evaluation of Urban Rail Transit, China Railway Design Group Co., Ltd., Tianjin 300308, ChinaJiangsu Hydraulic Research Institute, Nanjing 210017, ChinaThe accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditional gross error detection methods assume that real-state information is known, and they need to establish state iterators, which leads to low computational efficiency. Meanwhile, in multi-sensor fusion, if the sampling rates are different, the change in the dimension of the observation matrix must be considered, and the calculation is complex. Based on state-domain consistency theory, this paper proposes the State-domain Robust Autonomous Integrity Monitoring by Extrapolation (SRAIME) method for identifying slow-growing gross errors for GNSS/accelerometer integrated deformation monitoring. Compared with the traditional gross error detection method, the proposed method constructs state test statistics based on the state estimated value and the state predicted value, and it directly performs gross error identification in the state domain. This paper deduces the feasibility of the proposed method theoretically and verifies that the proposed method does not need to consider the dimension change of the observation matrix in gross error detection. Meanwhile, in the excitation deformation experiments of the Suntuan River Bridge in Anhui and the Wilford Bridge in the United Kingdom, the slow gradient of the slope was added to the measurement domain, and the traditional AIME method and the method proposed in this paper were adopted for the gross error identification of the GNSS/accelerometer fusion process. The results demonstrate that both methods can effectively detect gross errors, but the proposed method does not need to consider the dimensional change in the observation matrix during the fusion process, which has better applicability to GNSS/accelerometer integrated deformation monitoring.https://www.mdpi.com/2072-4292/14/19/4758gross error detectiondeformation monitoringGNSSstate domain |
| spellingShingle | Ao Sun Qiuzhao Zhang Zhangjun Yu Xiaolin Meng Xin Liu Yunlong Zhang Yilin Xie A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory Remote Sensing gross error detection deformation monitoring GNSS state domain |
| title | A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory |
| title_full | A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory |
| title_fullStr | A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory |
| title_full_unstemmed | A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory |
| title_short | A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory |
| title_sort | novel slow growing gross error detection method for gnss accelerometer integrated deformation monitoring based on state domain consistency theory |
| topic | gross error detection deformation monitoring GNSS state domain |
| url | https://www.mdpi.com/2072-4292/14/19/4758 |
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