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Modelling and mitigation of GNSS multipath effects by least-squares collocation considering spatial autocorrelation
https://orcid.org/0000-0002-8454-7294
Abstract Multipath effects can lead to delays of centimeters in GNSS phase observations depending on the station environment. Those delays can seriously degrade the accuracy of GNSS positioning and need to be carefully calibrated. Although existing multipath mitigation methods use the spatial autocorrelation feature of the multipath in one way or another, no methods can well express and utilize the spatial autocorrelation, resulting in limited calibration performance. In this contribution, a straightforward approach via least-squares collocation (LSC) based on the covariance function, which can accurately model and utilize the spatial autocorrelation feature, is proposed to model and mitigate GNSS multipath effects. This approach does not need to separate the hemispherical surface by grid but can obtain the multipath value of any point based on the covariance function, such as Markov’s function which is homogeneous and isotropic, i.e., the function depends only on the distance between points and is rotationally symmetric. In the experiment with GPS and Galileo baseline and precise point positioning (PPP) models, the LSC approach can effectively reduce the residuals caused by multipath and has lower standard deviations and higher variance reductions than the widely used modified sidereal filter approach and grid approach. For examples, except for the advantages with 10-day residuals in Galileo data processing of the baseline MAT1_MATE, the mean double difference (DD) residual covariance reduction index of other 9 days by the LSC approach reaches 50.8% with only 5-day residuals in multipath modelling. In comparison with the value of 36.6% obtained by the grid approach with the same residuals for modelling, an improvement of 38.8% is achieved. The mean residual covariance reduction indexes of Galileo PPP of the two approaches with 2-day residuals in multipath modelling for the station MADR are 37.9% and 22.2%, respectively, and for the station REYK are 34.9% and 15.8%, respectively, where the LSC approach shows nearly doubled improvement.
Modelling and mitigation of GNSS multipath effects by least-squares collocation considering spatial autocorrelation
https://orcid.org/0000-0002-8454-7294
Abstract Multipath effects can lead to delays of centimeters in GNSS phase observations depending on the station environment. Those delays can seriously degrade the accuracy of GNSS positioning and need to be carefully calibrated. Although existing multipath mitigation methods use the spatial autocorrelation feature of the multipath in one way or another, no methods can well express and utilize the spatial autocorrelation, resulting in limited calibration performance. In this contribution, a straightforward approach via least-squares collocation (LSC) based on the covariance function, which can accurately model and utilize the spatial autocorrelation feature, is proposed to model and mitigate GNSS multipath effects. This approach does not need to separate the hemispherical surface by grid but can obtain the multipath value of any point based on the covariance function, such as Markov’s function which is homogeneous and isotropic, i.e., the function depends only on the distance between points and is rotationally symmetric. In the experiment with GPS and Galileo baseline and precise point positioning (PPP) models, the LSC approach can effectively reduce the residuals caused by multipath and has lower standard deviations and higher variance reductions than the widely used modified sidereal filter approach and grid approach. For examples, except for the advantages with 10-day residuals in Galileo data processing of the baseline MAT1_MATE, the mean double difference (DD) residual covariance reduction index of other 9 days by the LSC approach reaches 50.8% with only 5-day residuals in multipath modelling. In comparison with the value of 36.6% obtained by the grid approach with the same residuals for modelling, an improvement of 38.8% is achieved. The mean residual covariance reduction indexes of Galileo PPP of the two approaches with 2-day residuals in multipath modelling for the station MADR are 37.9% and 22.2%, respectively, and for the station REYK are 34.9% and 15.8%, respectively, where the LSC approach shows nearly doubled improvement.
Modelling and mitigation of GNSS multipath effects by least-squares collocation considering spatial autocorrelation
https://orcid.org/0000-0002-8454-7294
Abstract Multipath effects can lead to delays of centimeters in GNSS phase observations depending on the station environment. Those delays can seriously degrade the accuracy of GNSS positioning and need to be carefully calibrated. Although existing multipath mitigation methods use the spatial autocorrelation feature of the multipath in one way or another, no methods can well express and utilize the spatial autocorrelation, resulting in limited calibration performance. In this contribution, a straightforward approach via least-squares collocation (LSC) based on the covariance function, which can accurately model and utilize the spatial autocorrelation feature, is proposed to model and mitigate GNSS multipath effects. This approach does not need to separate the hemispherical surface by grid but can obtain the multipath value of any point based on the covariance function, such as Markov’s function which is homogeneous and isotropic, i.e., the function depends only on the distance between points and is rotationally symmetric. In the experiment with GPS and Galileo baseline and precise point positioning (PPP) models, the LSC approach can effectively reduce the residuals caused by multipath and has lower standard deviations and higher variance reductions than the widely used modified sidereal filter approach and grid approach. For examples, except for the advantages with 10-day residuals in Galileo data processing of the baseline MAT1_MATE, the mean double difference (DD) residual covariance reduction index of other 9 days by the LSC approach reaches 50.8% with only 5-day residuals in multipath modelling. In comparison with the value of 36.6% obtained by the grid approach with the same residuals for modelling, an improvement of 38.8% is achieved. The mean residual covariance reduction indexes of Galileo PPP of the two approaches with 2-day residuals in multipath modelling for the station MADR are 37.9% and 22.2%, respectively, and for the station REYK are 34.9% and 15.8%, respectively, where the LSC approach shows nearly doubled improvement.
Modelling and mitigation of GNSS multipath effects by least-squares collocation considering spatial autocorrelation
Tian, Yumiao (author) / Liu, Zhifang (author) / Lin, Miao (author) / Li, Kaige (author)
Journal of Geodesy ; 97
2023
Article (Journal)
Electronic Resource
English
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