An Advanced Multipath Mitigation Method Based on Trend Surface Analysis

Wang, Zhiren; Wen, Chenglu; Dong, Danan; Zhang, Chenglong; Yu, Peng; Zheng, Zhengqi

doi:10.3390/rs12213601

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…The MHM mainly depends on the temporal and spatial correlation of satellites, and some scholars have proposed that the multipath is only related to the position of the satellites (independent of different systems and satellites), which avoids the calculation of the repeat periods of the satellites. In contrast to SF, the MHM can mitigate multipaths in quasi-static environments, whereas the main multipath environment remains unchanged [30]. However, the grid resolution plays an important role in the performance of the MHM.…”

Section: Discussionmentioning

confidence: 99%

“…A limitation of the SF method and its extensions is that they are only suitable for multipath mitigation in static environments [30]. Therefore, multipath mitigation in dynamic environments is a worthwhile research topic.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the sparse distribution of satellite orbits, a high resolution increases the insufficiency of mitigating multipaths, whereas a low resolution makes it difficult to mitigate high-frequency multipaths in the grid. To solve this problem, trend surface analysis-based MHM and advanced trend surface analysis-based MHM have been successively proposed to fit the spatial variation of multipath within the grid using classical trend-surface or advanced trend-surface analysis [30,34]. It remains insufficient to fit multipath using linear (quadratic and cubic nonlinear) regression functions or constrained linear regression functions.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tong,

Liu,

Tao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The MHM was not limited by the time shift of each satellite orbit repeat time (ORT) and only needed to calculate the current satellite position to directly mitigate the multipath, which improved the efficiency and the positioning accuracy. Wang et al [26,27] considered that the satellite tracks were not strictly repeated and proposed a trend surface analysis-based multipath hemispherical map that effectively mitigated the GPS multipath. Moreover, the correlation of satellite spatial-domain multipath has been applied in precision point positioning [28] and dynamic relative positioning [29] to mitigate the multipath.…”

Section: Introductionmentioning

confidence: 99%

A Single-Difference Multipath Hemispherical Map for Multipath Mitigation in BDS-2/BDS-3 Short Baseline Positioning

Liu

Yuan

Xin³

et al. 2021

Remote Sensing

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The BeiDou Navigation Satellite System (BDS) features a heterogeneous constellation so that it is difficult to mitigate the multipath in the coordinate-domain. Therefore, mitigating the multipath in the observation-domain becomes more important. Sidereal filtering is commonly used for multipath mitigation, which needs to calculate the orbit repeat time of each satellite. However, that poses a computational challenge and damages the integrity at the end of the multipath model. Therefore, this paper proposes a single-difference model based on the multipath hemispherical map (SD-MHM) to mitigate the BDS-2/BDS-3 multipath in a short baseline. The proposed method is converted from double-difference residuals to single-difference residuals, which is not restricted by the pivot satellite transformation. Moreover, it takes the elevation and the azimuth angles of the satellite as the independent variables of the multipath model. The SD-MHM overcomes the unequal observation time of some satellites and does not require specific hardware. The experimental results show that the SD-MHM reduces the root mean square of the positioning errors by 56.4%, 63.9%, and 67.4% in the east, north, and vertical directions; moreover, it contributes to an increase in the baseline accuracy from 1.97 to 0.84 mm. The proposed SD-MHM has significant advantages in multipath mitigation compared with the advanced sidereal filtering method. Besides, the SD-MHM also features an excellent multipath correction capability for observation data with a period of more than seven days. Therefore, the SD-MHM provides a universal strategy for BDS multipath mitigation.

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“…In the baseline E, N, and U directions, T-MHM has a higher positioning accuracy than MHM. To solve the problem of the multicollinearity and overfitting of the fitting parameters in the T-MHM, Wang et al [83] proposed the AT-MHM method. Theoretically, this method can be applied to carrier-phase multipath error correction.…”

mentioning

confidence: 99%

GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

Zhang,

Sun

et al. 2024

Remote Sensing

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A multipath error is one of the main sources of GNSS positioning errors. It cannot be eliminated by forming double-difference and other methods, and it has become an issue in GNSS positioning error processing, because it is mainly related to the surrounding environment of the station. To address multipath errors, three main mitigation strategies are employed: site selection, hardware enhancements, and data processing. Among these, data processing methods have been a focal point of research due to their cost-effectiveness, impressive performance, and widespread applicability. This paper focuses on the review of data processing mitigation methods for GNSS carrier-phase multipath errors. The paper begins by elucidating the origins and mitigation strategies of multipath errors. Subsequently, it reviews the current research status pertaining to data processing methods using stochastic and functional models to counter multipath errors. The paper also provides an overview of filtering techniques for extracting multipath error models from coordinate sequences or observations. Additionally, it introduces the evolution and algorithmic workflow of sidereal filtering (SF) and multipath hemispherical mapping (MHM), from both coordinate and observation domain perspectives. Furthermore, the paper emphasizes the practical significance and research relevance of multipath error processing. It concludes by delineating future research directions in the realm of multipath error mitigation.

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An Advanced Multipath Mitigation Method Based on Trend Surface Analysis

Cited by 13 publications

References 27 publications

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

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

A Single-Difference Multipath Hemispherical Map for Multipath Mitigation in BDS-2/BDS-3 Short Baseline Positioning

GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

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