Deformation Monitoring of Truss Structure Bridge with Time-Series InSAR Analysis
Time-Series InSAR is an advanced technology for real-time monitoring and precise analysis of object deformation. However, large-span steel truss structure bridges have complex bridge structures and deformation forms, which make it difficult for the traditional PSI method to realize the precise detec...
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Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing 2024-12, p.1-14 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Time-Series InSAR is an advanced technology for real-time monitoring and precise analysis of object deformation. However, large-span steel truss structure bridges have complex bridge structures and deformation forms, which make it difficult for the traditional PSI method to realize the precise detection of their deformation, resulting in a series of limitations. In order to solve these problems, an improved PSI method is proposed in this paper, which is firstly to carry out preliminary elevation measurement by small temporal baseline, use the preliminary elevation results to PS-arcs construction based on 3D spatial network, and utilize the optimal model coherence coefficient adaptive selection based on CFAR to carry out the parameter estimation, so as to realize the accurate monitoring of deformation of large-span steel truss structure bridges. In this paper, 24 ascending CSK Stripmap are used to monitor the deformation of Dongting Lake Bridge of the Menghua Railway, and the monitoring results show that the method not only realizes the accurate detection of deformation of large-span steel truss structure bridges, but also improves the accuracy and robustness of parameter estimation. The results show that the proposed method improves the accuracy of deformation parameter estimation by comparing with the deformation parameters solved by the traditional method, and the method reduces the average residual phase standard deviation by 10 |
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ISSN: | 1939-1404 |
DOI: | 10.1109/JSTARS.2024.3512787 |