Improving GNSS-RTK multipath error extraction with an integrated CEEMDAN and STD-based PCA algorithm

To address the background noise interference in GNSS-RTK during prolonged structural monitoring, the integration of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the standard deviation (STD)-based principal component analysis (PCA) method (CEEMDAN-PCA-S) is pro...

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Bibliographic Details
Published in:GPS solutions Vol. 28; no. 4; p. 192
Main Authors: Yu, Lina, Gao, Yang, Jijian, Lian, Li, Feilong, Gao, Xifeng, Wang, Ting
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024
Springer Nature B.V
Subjects:
Accuracy
Adaptive algorithms
Algorithms
Amplitudes
Artificial Intelligence Applications in GNSS
Atmospheric Sciences
Automotive Engineering
Background noise
Civil engineering
Construction
Correlation coefficient
Correlation coefficients
Earth and Environmental Science
Earth Sciences
Efficiency
Electrical Engineering
Geophysics/Geodesy
Noise monitoring
Original Article
Principal components analysis
Random noise
Root-mean-square errors
Satellites
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
White noise
Multipath error
Model correction
CEEMDAN-PCA-S
GNSS-RTK monitoring
ISSN:1080-5370, 1521-1886
Online Access:Get full text
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Summary:To address the background noise interference in GNSS-RTK during prolonged structural monitoring, the integration of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the standard deviation (STD)-based principal component analysis (PCA) method (CEEMDAN-PCA-S) is proposed for de-noising. The CEEMDAN-PCA-S aims to de-noise the internal random noise by leveraging its white noise characteristics and subsequently extract the multipath error by leveraging its periodic repetition characteristics. The efficiency of CEEMDAN-PCA-S is verified with a simulation and a field experiment. The simulation results demonstrate that the STD-based PCA (PCA-S) could achieve superior amplitude consistency with the original signal compared to traditional PCA. Additionally, the correlation coefficients of the multipath error obtained with different algorithms are PCA-S (0.9243) < CEEMDAN (0.9582) < CEEMDAN-PCA-S (0.9945). Moreover, the residual error of CEEMDAN-PCA-S is minimal in terms of amplitude and root mean square error (RMSE), confirming its superior de-noising performance compared to CEEMDAN and PCA-S. The outcomes of a field experiment utilizing the GNSS-RTK indicate that the correlation coefficients between the multipath error extracted with CEEMDAN-PCA-S and the CEEMDAN residual errors are all above 0.94. The CEEMDAN-PCA-S decreases the RMSE of residual error to 0.1599 cm, marking a 79% reduction from the original signal. Moreover, both the amplitude and mean value are reduced by 83% and 16%. In conclusion, the proposed CEEMDAN-PCA-S could effectively remove white noise and subsequently extract the multipath error, enhancing the accuracy of GNSS-RTK for long-term structural monitoring and safety warnings.
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ISSN:1080-5370
1521-1886
DOI:10.1007/s10291-024-01725-4