A combined algorithm for denoising GNSS-RTK positioning solutions with application to displacement monitoring of a super-high-rise building

Yu, Lie; Xiong, Chunbao; Gao, Yang; Ye, Zuoan; Shi, Qingfa

doi:10.1088/1361-6501/ac0b6d

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Additionally, the proposed method is useful and potentially inspiring for related research fields. Typically, strong multipath GNSS measurement sequences using the DD technology are not detrimental to deformation monitoring in realtime [44]. If a multipath can be predicted in real time, the accuracy of deformation monitoring can be improved.…”

Section: Discussionmentioning

confidence: 99%

ConvGRU-MHM: a CNN GRU-enhanced MHM for mitigating GNSS multipath

Tong,

Liu,

Tao

et al. 2024

Meas. Sci. Technol.

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In high-precision global navigation satellite system (GNSS) short-baseline positioning, multipath is the main source of errors. If the station environment is quasi-static, repeat periods of satellites can be utilized to generate time- or space-dependent multipath models to mitigate multipaths. However, two general problems are associated with the multipath models constructed based on satellite mechanics: (1) an accuracy decrease occurs when the above models are applied to multipath mitigation over a long time-span; (2) when constructing the spatial and temporal grids of the satellite-based spatially dependent multipath model, it is challenging to balance computational efficiency and spatial resolution. We propose a convolutional neural network-gated recurrent unit enhanced multipath hemispherical map (ConvGRU-MHM) in the observational domain to address these problems. The proposed method directly mines the deep features of elevation, azimuth angle, and multipath and the mapping relationship between these to establish a real-time prediction model. The predicted multipath is obtained and returned to the observation equation for multipath mitigation when the real-time position of the satellite is placed in the pre-trained model. We compared the multipath mitigation performance of sidereal filtering (SF) and a multipath hemispherical map (MHM) with that of the ConvGRU-MHM to demonstrate the advantages of the proposed method. The experimental results are as follows: (1) in the short time-span (first 20 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction performed better than those of the SF and MHM; and (2) in the long-term time (after 50 days), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction are higher than that of the SF and MHM by 10–20%. As a lightweight model, the ConvGRU-MHM can effectively improve the measurement accuracy of GNSS real-time monitoring in fields, such as deformation monitoring and seismic research.

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Section: Discussionmentioning

confidence: 99%

ConvGRU-MHM: a CNN GRU-enhanced MHM for mitigating GNSS multipath

Tong,

Liu,

Tao

et al. 2024

Meas. Sci. Technol.

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“…How to find an effective algorithm to better evaluate the modal parameters of the bridge from the noisy signal responses has consistently attracted the interest of researchers. Some time-frequency domain analysis methods are employed to decompose signal responses into several intrinsic mode functions (IMFs) and thus separate noise components, like empirical wavelet transform (EWT) [14,15], empirical mode decomposition (EMD) [16][17][18], ensemble empirical mode decomposition (EEMD) [19][20][21], complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [22][23][24], variational mode decomposition (VMD) [25][26][27][28]. Tao et al [29] proposed the EWT method to reduce the noise in the GNSS coordinate system and demonstrated that the signal after EWT denoising was close to the original signal.…”

Section: Introductionmentioning

confidence: 99%

Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

Xiong

Shang

Liu

et al. 2023

Meas. Sci. Technol.

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This paper aims to employ high-rate global navigation satellite system real-time kinematic (GNSS-RTK) and triaxial accelerometer sensors to evaluate the modal frequencies of a damaged long-span bridge based on the proposed modified variational mode decomposition (VMD) algorithm. Firstly, in the modified VMD, the relative root mean square error (RRMSE) was presented to optimize the decomposition parameter K, the correlation criterion was introduced to select the suitable intrinsic mode function (IMF) components. The above procedure is called the RCVMD algorithm. Subsequently, the performance comparison evaluation experiments proved that the RCVMD algorithm was more advantageous in the decomposition and reconstruction of signals, had a better noise reduction effect in the signal with different noise levels, and was suitable to process the actual measurement response. Finally, in the field experiment, the modal frequencies of the damaged bridge were correctly detected from the GNSS-RTK displacement and acceleration responses according to the RCVMD algorithm, and the accelerometer picked up the high-order structural frequencies, demonstrating the reliability of the proposed method. The frequency characteristics of the damaged bridge are different from the normal state. The first and third-order frequencies increase, which is especially obvious in the first-order frequency, yet the second-order frequency decreases. The measured results have practical applications and can provide data support for the maintenance of the damaged bridge.

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