Method for correcting time series of vertical coordinates of all mobile GNSS (Global Navigation Satellite System) in region

Abstract

A method for correcting time sequences of vertical coordinates of all flowing GNSS in a region comprises the following steps: acquiring coordinate information of all flowing GNSS and continuous GNSS sites in an observation region, calculating the distance between each flowing GNSS site and each continuous GNSS site, and summarizing to obtain a distance set; according to the distance set, determining each mobile GNSS station and a continuous GNSS station closest to the mobile GNSS station to obtain a station set, and then summarizing the station set; performing time sequence fitting according to the station set, and then obtaining time sequence periodic parameters of vertical coordinates of all continuous GNSS stations corresponding to the station set; and correcting the vertical coordinate time sequence of the mobile GNSS station through the periodic parameters of the time sequence of the continuous GNSS station closest to the mobile GNSS to obtain corrected mobile GNSS station data. The GNSS vertical time sequence is accurate in periodic correction and small in error.

Description

Technical Field

[0001] The invention relates to an improvement of a mobile GNSS time series periodicity technology, belongs to the field of geodesy, and in particular to a correction method for all mobile GNSS vertical coordinate time series in a region. Background Art

[0002] When estimating the linear rate of the time series of mobile GNSS stations, since the observation time of mobile GNSS observation stations is usually concentrated from May to August each year, and each observation lasts for 4 days, although this observation mode can reduce the impact of some periodic deformations, there will still be certain periodic effects due to the difference in observation time each year; therefore, these periodic effects need to be corrected. The existing technical methods are mainly based on GRACE / GFO observation data to calculate the periodic elastic deformation at the GNSS mobile station, and then perform periodic corrections on the mobile GNSS coordinate time series. This method can reduce the impact of periodicity to a certain extent, but since GRACE / GFO observation data mainly observes changes caused by hydrological factors, the impact of other unknown factors cannot be observed, which leads to the elastic deformation calculated using GRACE / GFO data may be smaller than the actual deformation, making the periodic correction of the mobile GNSS vertical time series inaccurate.

[0003] The Chinese patent application with application number CN201710035054.5 and application date January 17, 2017 discloses a method for mining the periodic characteristics of GNSS position time series. The invention takes into account the limitations of traditional harmonic functions, introduces wavelet analysis, FAMOUS time-frequency analysis method, maximum likelihood estimation and other methods, and provides an accurate method for periodic signal analysis and characteristic analysis of GNSS position time series. The method decomposes the original time series into new time series of different frequency bands based on the different natural frequencies of different signals in the original sequence, decomposes and reconstructs the signal, processes each layer, and obtains the required part, which overcomes the limitations of the traditional GNSS time series model, helps to truly reflect the periodic changes of the coordinate sequence, and further improves the accuracy and reliability of the GNSS station coordinates. However, the above scheme does not solve the problem of inaccurate periodic correction of the mobile GNSS vertical time series.

[0004] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of this patent application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem in the prior art that the periodic correction of the mobile GNSS vertical time series is not accurate enough, and to provide a correction method for all mobile GNSS vertical coordinate time series in an area where the periodic correction of the GNSS vertical time series is accurate.

[0006] To achieve the above objectives, the technical solution of the present invention is: a method for correcting the time series of vertical coordinates of all mobile GNSS in a region, the method for correcting the time series of vertical coordinates of all mobile GNSS in a region comprising the following steps:

[0007] The first step is to obtain the coordinate information of all mobile GNSS stations and continuous GNSS stations in the observation area, and then calculate the distance between each mobile GNSS station and all continuous GNSS stations to obtain a distance set;

[0008] Step 2: First, according to the above distance set, determine the continuous GNSS stations closest to each mobile GNSS station to obtain the corresponding station group, and then summarize all the station groups to obtain the station set;

[0009] The third step is to first perform time series fitting based on the above site set, and then obtain the time series periodic parameters of the vertical coordinates of all continuous GNSS sites corresponding to the site set;

[0010] The fourth step is to correct the vertical coordinate time series of the mobile GNSS station using the periodic parameters of the time series of the continuous GNSS station closest to the mobile GNSS to obtain the corrected mobile GNSS vertical coordinate time series.

[0011] In the first step, the distance between each mobile GNSS station and the continuous GNSS station is calculated as follows:

[0012] d=111.12cos{1 / [sinΦ A sinΦ B Ten cosΦ A cosΦ B cos(λ B —λ A )]};

[0013] Among them, the longitude and latitude of point A are λ A and Φ A , the longitude and latitude of point B are λ B and Φ B , d is the distance.

[0014] The site coordinate information includes timestamp, frequency, phase and amplitude data of collected satellite signals.

[0015] After obtaining the site coordinate information, the method also includes filtering the data, removing noise and correcting errors.

[0016] In the second step, the continuous GNSS stations closest to each mobile GNSS station are determined based on the above distance set, and then the corresponding station set is obtained, and then all the station sets are summarized, specifically:

[0017] First, based on the above distance set, the surface distance between each pair of mobile GNSS stations and continuous GNSS stations is calculated using the geographic coordinate distance formula. Then, for each mobile GNSS station, the nearest continuous GNSS station is found. Then, a site set is created for each mobile GNSS station, which includes the mobile station and its nearest continuous GNSS station. Then, the site sets of all mobile GNSS stations are merged into a total site set list.

[0018] In the third step, time series fitting is first performed according to all the above-mentioned site sets, and then the vertical coordinate time series periodic parameters of all continuous GNSS sites corresponding to the site set are obtained, specifically: the vertical coordinate time series data of the continuous GNSS sites are analyzed, and the periodic parameters, including amplitude, phase and frequency, are extracted from the time series analysis.

[0019] The time series fitting, i.e. the linear fitting of the long-term coordinate time series, is specifically:

[0020]

[0021] Among them, a and b are the intercept and linear rate, respectively, cf is the annual and semi-annual amplitude of the time series, g i and h i Yes i The step value at time t, H represents the Heaviside step function, n is the number of steps, h is the post-earthquake deformation amplitude, t eq is the time when the earthquake occurs, and τ is the post-earthquake relaxation time constant.

[0022] The time series method is ARIMA model or Fourier transform time series analysis.

[0023] The time series is fitted using a parameter model with an annual cycle term and a semi-annual cycle term.

[0024] The mobile station uses the average values ​​of the annual cycle items and the semi-annual cycle items of adjacent continuous stations to reduce the influence of the cycle error, and uses the spatial distance interpolation to obtain the residual value to reduce the influence of the common mode error.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. In a correction method for all mobile GNSS vertical coordinate time series in a region of the present invention, the influence of periodic deformation is eliminated or reduced by periodic correction, the accuracy and reliability of GNSS observation data are improved, and the data can better reflect the actual vertical movement of the earth's crust. The distance set is first calculated, and then the nearest continuous GNSS station is determined, so that the continuous station with the greatest impact on the mobile GNSS station can be quickly located, which improves the efficiency of data processing. Then, the continuous GNSS station closest to the mobile GNSS station is selected, and the periodic deformation parameters of the mobile GNSS station can be more accurately estimated, because the closer the distance, the more similar the geological conditions and environmental factors, and the closer the periodic deformation. By periodically correcting the time series of the mobile GNSS station, the error caused by periodic deformation can be significantly reduced, and the accuracy of the data can be improved. Therefore, the periodic correction of the GNSS vertical time series of the present invention is accurate and the error is small.

[0027] 2. In the correction method of the vertical coordinate time series of all mobile GNSS in a region, by summarizing all the station sets and the corresponding periodic parameters, the data can be systematically analyzed and processed, the reliability of the data is enhanced, and it can adapt to different observation conditions and environments, because it is based on the actual distance between stations and periodic deformation parameters, rather than relying on a single model or hypothesis. Therefore, the data reliability of the present invention is high and can adapt to different environments.

[0028] 3. In the correction method of the vertical coordinate time series of all mobile GNSS in a region of the present invention, by selecting the nearest continuous station for periodic correction, the influence of these unknown factors can be reduced to a certain extent. This method can perform personalized periodic correction for different mobile GNSS stations, because each station has its unique nearest continuous station and periodic parameters. Therefore, the present invention is highly targeted and reduces the influence of unknown factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the present invention.

[0030] Figure 2 It is a schematic diagram of periodic fitting of GNSS continuous stations of the present invention.

[0031] Figure 3 It is a schematic diagram of the periodic correction of the mobile station of the present invention. DETAILED DESCRIPTION

[0032] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] See also Figures 1 to 3A method for correcting the time series of vertical coordinates of all mobile GNSS in a region, the method for correcting the time series of vertical coordinates of all mobile GNSS in a region comprising the following steps:

[0034] The first step is to obtain the coordinate information of all mobile GNSS stations and continuous GNSS stations in the observation area, and then calculate the distance between each mobile GNSS station and all continuous GNSS stations to obtain a distance set;

[0035] Step 2: First, according to the above distance set, determine the continuous GNSS stations closest to each mobile GNSS station to obtain the corresponding station group, and then summarize all the station groups to obtain the station set;

[0036] The third step is to first perform time series fitting based on the above site set, and then obtain the time series periodic parameters of the vertical coordinates of all continuous GNSS sites corresponding to the site set;

[0037] The fourth step is to correct the vertical coordinate time series of the mobile GNSS station using the periodic parameters of the time series of the continuous GNSS station closest to the mobile GNSS to obtain the corrected mobile GNSS vertical coordinate time series.

[0038] In the first step, the distance between each mobile GNSS station and the continuous GNSS station is calculated as follows:

[0039] d=111.12cos{1 / [sinΦ A sinΦ B Ten cosΦ A cosΦ B cos(λ B —λ A )]};

[0040] Among them, the longitude and latitude of point A are λ A and Φ A , the longitude and latitude of point B are λ B and Φ B , d is the distance.

[0041] The site coordinate information includes timestamp, frequency, phase and amplitude data of collected satellite signals.

[0042] After obtaining the site coordinate information, the method also includes filtering the data, removing noise and correcting errors.

[0043] In the second step, the continuous GNSS stations closest to each mobile GNSS station are determined based on the above distance set, and then the corresponding station set is obtained, and then all the station sets are summarized, specifically:

[0044] First, based on the above distance set, the surface distance between each pair of mobile GNSS stations and continuous GNSS stations is calculated using the geographic coordinate distance formula. Then, for each mobile GNSS station, the nearest continuous GNSS station is found. Then, a site set is created for each mobile GNSS station, which includes the mobile station and its nearest continuous GNSS station. Then, the site sets of all mobile GNSS stations are merged into a total site set list.

[0045] In the third step, time series fitting is first performed according to all the above-mentioned site sets, and then the vertical coordinate time series periodic parameters of all continuous GNSS sites corresponding to the site set are obtained, specifically: the vertical coordinate time series data of the continuous GNSS sites are analyzed, and the periodic parameters, including amplitude, phase and frequency, are extracted from the time series analysis.

[0046] The time series fitting, i.e. the linear fitting of the long-term coordinate time series, is specifically:

[0047]

[0048] Among them, a and b are the intercept and linear rate, respectively, cf is the annual and semi-annual amplitude of the time series, g i and h i Yes i The step value at time t, H represents the Heaviside step function, n is the number of steps, h is the post-earthquake deformation amplitude, t eq is the time when the earthquake occurs, and τ is the post-earthquake relaxation time constant.

[0049] The time series method is ARIMA model or Fourier transform time series analysis.

[0050] The time series is fitted using a parameter model with an annual cycle term and a semi-annual cycle term.

[0051] The mobile station uses the average values ​​of the annual cycle items and the semi-annual cycle items of adjacent continuous stations to reduce the influence of the cycle error, and uses the spatial distance interpolation to obtain the residual value to reduce the influence of the common mode error.

[0052] The supplementary description of the present invention is as follows:

[0053] When estimating the linear rate of the time series of the mobile GNSS station, since the observation time of the mobile GNSS observation station is from May to August each year, and each observation lasts for 4 days, although this can eliminate some of the influence of periodic deformation, due to the difference in observation time each year, there will still be certain periodic influences, so it is necessary to make periodic corrections.

[0054] Embodiment 1:

[0055] A method for correcting the vertical coordinate time series of all mobile GNSS in a region, the method for correcting the vertical coordinate time series of all mobile GNSS in a region comprising the following steps:

[0056] The first step is to obtain the coordinate information of all mobile GNSS stations and continuous GNSS stations in the observation area, and then calculate the distance between each mobile GNSS station and all continuous GNSS stations to obtain a distance set;

[0057] Step 2: First, according to the above distance set, determine the continuous GNSS stations closest to each mobile GNSS station to obtain the corresponding station group, and then summarize all the station groups to obtain the station set;

[0058] The third step is to first perform time series fitting based on the above site set, and then obtain the time series periodic parameters of the vertical coordinates of all continuous GNSS sites corresponding to the site set;

[0059] The fourth step is to correct the vertical coordinate time series of the mobile GNSS station using the periodic parameters of the time series of the continuous GNSS station closest to the mobile GNSS to obtain the corrected mobile GNSS vertical coordinate time series.

[0060] Embodiment 2:

[0061] Embodiment 2 is substantially the same as Embodiment 1, except that:

[0062] In the first step, the distance between each mobile GNSS station and the continuous GNSS station is calculated as follows:

[0063] d=111.12cos{1 / [sinΦ A sinΦ B Ten cosΦ A cosΦ B cos(λ B —λ A )]};

[0064] Among them, the longitude and latitude of point A are λ A and Φ A , the longitude and latitude of point B are λ B and Φ B , d is the distance; the site coordinate information includes the timestamp, frequency, phase and amplitude data of the collected satellite signal; after obtaining the site coordinate information, it also includes filtering the data, removing noise and correcting errors.

[0065] Embodiment 3:

[0066] Embodiment 3 is substantially the same as Embodiment 1, except that:

[0067] Analyze the vertical coordinate time series data of continuous GNSS stations and extract periodic parameters from the time series analysis, including amplitude, phase and frequency;

[0068] The time series fitting, i.e. the linear fitting of the long-term coordinate time series, is specifically:

[0069]

[0070] Among them, a and b are the intercept and linear rate, respectively, cf is the annual and semi-annual amplitude of the time series, g i and h i Yes i The step value at time t, H represents the Heaviside step function, n is the number of steps, h is the post-earthquake deformation amplitude, t eq is the time when the earthquake occurs, τ is the post-earthquake relaxation time constant, and the periodic parameters of the continuous GNSS station time series can be estimated, which are the parameters cf, which are the anniversary and semi-annual amplitudes of the time series. These parameters are then used to plot the periodicity of the mobile GNSS stations, and then the mobile GNSS stations are corrected. Then the slope of the mobile GNSS station time series is estimated to improve its accuracy. Compared with the traditional least squares method, this method introduces a parameter optimal solution method based on the Bayesian framework, takes into account random characteristics, and can extract tectonic and non-tectonic movement information more accurately and efficiently.

[0071] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A correction method for the time series of vertical coordinates of all mobile GNSS in an area, characterized by: The correction method for all mobile GNSS vertical coordinate time series in the area includes the following steps: The first step is to obtain the coordinate information of all mobile GNSS stations and continuous GNSS stations in the observation area, and then calculate the distance between each mobile GNSS station and all continuous GNSS stations to obtain a distance set; Step 2: First, according to the above distance set, determine the continuous GNSS stations closest to each mobile GNSS station to obtain the corresponding station group, and then summarize all the station groups to obtain the station set; The third step is to first perform time series fitting based on the above site set, and then obtain the time series periodic parameters of the vertical coordinates of all continuous GNSS sites corresponding to the site set; The fourth step is to correct the vertical coordinate time series of the mobile GNSS station using the periodic parameters of the time series of the continuous GNSS station closest to the mobile GNSS to obtain the corrected mobile GNSS vertical coordinate time series.

2. The correction method for all mobile GNSS vertical coordinate time series in a region according to claim 1, characterized in that: In the first step, the distance between each mobile GNSS station and the continuous GNSS station is calculated as follows: d = 111.12 cos{1 / [sin Φ A sin Φ B + cos Φ A cos Φ B cos(λ B — λ A )]}; It should be noted that there seems to be a "+" missing in the original formula in the part of "sinΦ + cosΦ", which has been corrected in the translation. Among them, the longitude and latitude of point A are λ A and Φ A , the longitude and latitude of point B are λ B and Φ B , d is the distance.

3. The correction method for all mobile GNSS vertical coordinate time series in a region according to claim 2, characterized in that: The site coordinate information includes timestamp, frequency, phase and amplitude data of collected satellite signals.

4. The correction method for all mobile GNSS vertical coordinate time series in a region according to claim 3, characterized in that: After obtaining the site coordinate information, the method also includes filtering the data, removing noise and correcting errors.

5. The correction method for all mobile GNSS vertical coordinate time series in a region according to claim 1, characterized in that: In the second step, the continuous GNSS stations closest to each mobile GNSS station are determined based on the above distance set, and then the corresponding station set is obtained, and then all the station sets are summarized, specifically: First, based on the above distance set, the surface distance between each pair of mobile GNSS stations and continuous GNSS stations is calculated using the geographic coordinate distance formula. Then, for each mobile GNSS station, the nearest continuous GNSS station is found. Then, a site set is created for each mobile GNSS station, which includes the mobile station and its nearest continuous GNSS station. Then, the site sets of all mobile GNSS stations are merged into a total site set list.

6. The method for correcting the time series of vertical coordinates of all mobile GNSS in a region according to claim 5, characterized in that: In the third step, time series fitting is first performed according to all the above-mentioned site sets, and then the vertical coordinate time series periodic parameters of all continuous GNSS sites corresponding to the site set are obtained, specifically: the vertical coordinate time series data of the continuous GNSS sites are analyzed, and the periodic parameters, including amplitude, phase and frequency, are extracted from the time series analysis.

7. The method for correcting the time series of vertical coordinates of all mobile GNSS in a region according to claim 6, characterized in that: The time series fitting, i.e. the linear fitting of the long-term coordinate time series, is specifically: Among them, a and b are the intercept and linear rate, respectively, cf is the annual and semi-annual amplitude of the time series, g i and h i Yes i The step value at time t, H represents the Heaviside step function, n is the number of steps, h is the post-earthquake deformation amplitude, t eq is the time when the earthquake occurs, and τ is the post-earthquake relaxation time constant.

8. The method for correcting the time series of vertical coordinates of all mobile GNSS in a region according to claim 7, characterized in that: The time series method is ARIMA model or Fourier transform time series analysis.

9. The method for correcting the time series of vertical coordinates of all mobile GNSS in a region according to claim 8, characterized in that: The time series is fitted using a parameter model with an annual cycle term and a semi-annual cycle term.

10. The method for correcting the time series of vertical coordinates of all mobile GNSS in a region according to claim 9, characterized in that: The mobile station uses the average values ​​of the annual cycle items and the semi-annual cycle items of adjacent continuous stations to reduce the influence of the cycle error, and uses the spatial distance interpolation to obtain the residual value to reduce the influence of the common mode error.

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