GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars
Traditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural appli...
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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/2072-4292/17/2/224 |
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| author | Song Zhang Yuntao Yang Yilin Xie Haoran Tang Haiyang Li Lianbi Yao Yin Yang |
| author_facet | Song Zhang Yuntao Yang Yilin Xie Haoran Tang Haiyang Li Lianbi Yao Yin Yang |
| author_sort | Song Zhang |
| collection | DOAJ |
| description | Traditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural applications. Accurate signal extraction is essential for reliable deformation monitoring, as it directly influences the quality of the detected structural changes. However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. The proposed approach effectively mitigates high-frequency noise interference and modal mixing in GNSS signals, thereby enhancing the accuracy and reliability of deformation measurements. Simulation experiments and real-world scenario applications with operational field data processing demonstrate the effectiveness of the proposed method. This research contributes to advancing GNSS-based deformation monitoring techniques, offering a robust solution for detecting and analyzing subtle structural changes in various engineering contexts. |
| format | Article |
| id | doaj-art-ecdbdc77f0a845f4806a4dd60ca4ff52 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ecdbdc77f0a845f4806a4dd60ca4ff522025-01-24T13:47:47ZengMDPI AGRemote Sensing2072-42922025-01-0117222410.3390/rs17020224GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway PillarsSong Zhang0Yuntao Yang1Yilin Xie2Haoran Tang3Haiyang Li4Lianbi Yao5Yin Yang6No. 1 Institute of Geology and Mineral Resources of Shandong Province, Jinan 250000, ChinaNo. 1 Institute of Geology and Mineral Resources of Shandong Province, Jinan 250000, ChinaJiangsu Hydraulic Research Institute, Nanjing 210017, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaJiangsu Hydraulic Research Institute, Nanjing 210017, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaJiangsu Hydraulic Research Institute, Nanjing 210017, ChinaTraditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural applications. Accurate signal extraction is essential for reliable deformation monitoring, as it directly influences the quality of the detected structural changes. However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. The proposed approach effectively mitigates high-frequency noise interference and modal mixing in GNSS signals, thereby enhancing the accuracy and reliability of deformation measurements. Simulation experiments and real-world scenario applications with operational field data processing demonstrate the effectiveness of the proposed method. This research contributes to advancing GNSS-based deformation monitoring techniques, offering a robust solution for detecting and analyzing subtle structural changes in various engineering contexts.https://www.mdpi.com/2072-4292/17/2/224GNSS signalropeway pillardeformation monitoringsignal extractionCEEMDANwavelet packet decomposition |
| spellingShingle | Song Zhang Yuntao Yang Yilin Xie Haoran Tang Haiyang Li Lianbi Yao Yin Yang GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars Remote Sensing GNSS signal ropeway pillar deformation monitoring signal extraction CEEMDAN wavelet packet decomposition |
| title | GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars |
| title_full | GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars |
| title_fullStr | GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars |
| title_full_unstemmed | GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars |
| title_short | GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars |
| title_sort | gnss signal extraction using ceemdan wpd for deformation monitoring of ropeway pillars |
| topic | GNSS signal ropeway pillar deformation monitoring signal extraction CEEMDAN wavelet packet decomposition |
| url | https://www.mdpi.com/2072-4292/17/2/224 |
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