Academic Journal

GNSS signal processing based on improved lifting wavelet transform with prior constraint.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: GNSS signal processing based on improved lifting wavelet transform with prior constraint.
Συγγραφείς: Jiang, Chen, Yang, Wenbo, Wang, Yiya, Zhao, Dongbao
Πηγή: Scientific Reports; 6/18/2025, Vol. 15 Issue 1, p1-10, 10p
Θεματικοί όροι: Global Positioning System, Structural dynamics, Signal denoising, Frequencies of oscillating systems, Signal processing, Wavelet transforms
Περίληψη: During deformation monitoring via GNSS (Global Navigation Satellite System), the initial GNSS signal typically comprises abundant interference information, and controlling the influences of the GNSS noises and extracting pure structural vibration information become the challenging issue. Therefore, an improved three-segment soft threshold function was proposed to control the influence of the noises, and it is the prerequisite for extracting useful vibration information. Meanwhile, prior information such as the known frequency or the vibration characteristics of the construction and the significant noises can help further to improve the efficiency of the lifting wavelet transform. Thus, the wavelet decomposition was applied toward the denoised signal to extract useful vibration information and significant noises based on the prior information. The improved algorithm was implemented and compared with the conventional lifting wavelet transform in the coordinate calculation of the GNSS monitoring point. Experimental results indicate that the improved lifting wavelet transform performed better than the conventional lifting wavelet transform in signal denoising, and the valid structural vibration information and significant noises can be simply identified based on the prior information constraint. This research can provide valuable references for GNSS data processing, dynamic deformation information extraction, and external load analysis. [ABSTRACT FROM AUTHOR]
Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1038/s41598-024-83141-9
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  Data: GNSS signal processing based on improved lifting wavelet transform with prior constraint.
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  Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Chen%22">Jiang, Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Wenbo%22">Yang, Wenbo</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yiya%22">Wang, Yiya</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Dongbao%22">Zhao, Dongbao</searchLink>
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  Data: Scientific Reports; 6/18/2025, Vol. 15 Issue 1, p1-10, 10p
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  Data: <searchLink fieldCode="DE" term="%22Global+Positioning+System%22">Global Positioning System</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+dynamics%22">Structural dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Frequencies+of+oscillating+systems%22">Frequencies of oscillating systems</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: During deformation monitoring via GNSS (Global Navigation Satellite System), the initial GNSS signal typically comprises abundant interference information, and controlling the influences of the GNSS noises and extracting pure structural vibration information become the challenging issue. Therefore, an improved three-segment soft threshold function was proposed to control the influence of the noises, and it is the prerequisite for extracting useful vibration information. Meanwhile, prior information such as the known frequency or the vibration characteristics of the construction and the significant noises can help further to improve the efficiency of the lifting wavelet transform. Thus, the wavelet decomposition was applied toward the denoised signal to extract useful vibration information and significant noises based on the prior information. The improved algorithm was implemented and compared with the conventional lifting wavelet transform in the coordinate calculation of the GNSS monitoring point. Experimental results indicate that the improved lifting wavelet transform performed better than the conventional lifting wavelet transform in signal denoising, and the valid structural vibration information and significant noises can be simply identified based on the prior information constraint. This research can provide valuable references for GNSS data processing, dynamic deformation information extraction, and external load analysis. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1038/s41598-024-83141-9
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      – Code: eng
        Text: English
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      – SubjectFull: Global Positioning System
        Type: general
      – SubjectFull: Structural dynamics
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      – SubjectFull: Signal denoising
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      – SubjectFull: Frequencies of oscillating systems
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      – SubjectFull: Signal processing
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      – SubjectFull: Wavelet transforms
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              M: 06
              Text: 6/18/2025
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              Y: 2025
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