Multi-day integrated modeling method of multipath error considering daily correlation of multipath effect

Abstract

The present invention provides a multi-day overall modeling method for multi-path errors that takes into account the daily correlation of multi-path effects. For navigation systems such as Beidou and Galileo, whose satellite orbits need to be repeated after many days, the existing station space domain multi-path error modeling method is to use the observation data of each day within the satellite orbit repetition period to process and model the whole. Considering that the observation environment around the station is different every day, there are differences in the consistency of the observation environment between different modeling days and extrapolated days. While the patent performs overall modeling of the multi-path error effect space domain for multiple days, it takes into account the daily correlation of the multi-path effect, so that the modeling results are more consistent with the actual multi-path error effect of the subsequent extrapolated days, thereby further improving the accuracy and reliability of the existing GNSS precision data processing.

Description

Technical Field

[0001] The present invention relates to the field of global satellite navigation technology, and in particular to a multi-day overall modeling method for multi-path errors taking into account the daily correlation of multi-path effects. Background Art

[0002] In the global satellite navigation system, the multipath error at the observation station is the deviation of the observation value caused by the scattering, reflection and other reasons of the Global Navigation Satellite System (GNSS) satellite signal reaching the receiver antenna through multiple paths. It has a strong correlation with the satellite signal, signal receiving equipment and observation environment of the observation station, and cannot be eliminated or weakened by differential processing, existing correction models or parameterized estimation methods. The impact of multipath error in the observation environment can be approximately described using the following simplified model. The theoretical multipath error value is related to four parameters: the distance from the antenna phase center to the reflection plane ; Reflection coefficient ; Wavelength of the carrier signal and the incident angle of the direct signal For carrier phase observations, the maximum impact of multipath error can reach 1 / 4 of the wavelength, such as Figure 1 shown.

[0003] To model the multipath error of GNSS observation signals, the most widely used and effective method is the sidereal filtering (SF) method. This method corrects the multipath error in the observation value domain or coordinate domain based on the periodic repeatability of the geometric structure between the satellite, the station antenna and the interference source. However, since the differences in the repetition periods of different satellite orbits need to be taken into account, it is not suitable for the design of the Beidou Satellite Navigation System (BDS) composed of multiple types of high-medium-low orbit satellites. Describing the carrier phase multipath error effect at the station in the spatial domain is also an effective solution to weaken its influence. This type of modeling method reduces the influence of the differences in the repetition period of satellite orbits and is a current research hotspot. Moore et al. (2014) established an empirical site model (ESM) by obtaining the residual of the non-differenced phase observation value of each station to weaken the unmodeled station-related error. Dong et al. (2016) proposed a multi-antenna shared clock method to establish a multipath error hemispherical model MHM (Multipath Hemispherical Map). Zhang et al. (2023) systematically analyzed the application service scheme of the MHM model in the BeiDou system. Since the zero mean assumption of the ESM method when obtaining non-difference residuals is unreasonable in some cases (Shi et al. 2016), Tang et al. (2021) directly used the double-difference observation residuals between stations to model the multipath error in the semi-celestial space domain (Multi-point Hemispherical Grid Model, MHGM), and used 18 years of GPS observation data from the IGS station to verify the effectiveness of this method. However, Figure 2 As shown in Figure 2, the model correction effect of multipath error decreases with the increase of extrapolation time. The increase of extrapolation time will have a certain negative impact on the effectiveness of the model, and its impact depends on the changes in the observation environment around the station, such as changes in meteorological conditions and changes in surrounding vegetation.

[0004] Therefore, by Figure 2It can be seen that although the measuring station is stationary, there are certain differences in the multipath error effects on different days, and the multipath error effects on adjacent days are relatively more consistent. In the application process of the multipath error correction model, its purpose is to eliminate the multipath error influence of the extrapolated day as accurately as possible. Therefore, for the modeling data of the last day, since the multipath error effect of this day is more consistent with the extrapolated day, in the overall modeling process of multipath error for multiple days, a relatively large reference weight can be set for the data of this day. For the observation data of the remaining days, if the weight can be appropriately reduced according to the correlation between the multipath effect of the corresponding day and the last day, in theory, a modeling result that is more suitable for the correction of the multipath error of the extrapolated day can be obtained. Summary of the invention

[0005] The present invention provides a multi-day overall modeling method for multi-path errors taking into account the daily correlation of multi-path effects, so as to solve the defects existing in the prior art.

[0006] In a first aspect, the present invention provides a multi-day overall modeling method for multi-path errors taking into account the daily correlation of multi-path effects, comprising:

[0007] Collect multi-day multipath error modeling observation data, and calculate and obtain the residual root mean square of the double difference observation value of the GPS carrier phase every day based on the multi-day multipath error modeling observation data;

[0008] The MHGM method is used to model multipath errors using the double-difference observation residuals of the GPS carrier phase on the last day.

[0009] Based on the result of the multipath error modeling, multipath error correction is performed on the multi-day multipath error modeling observation data to obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day after model correction;

[0010] Based on the residual root mean square of the daily GPS carrier phase double difference observation value and the residual root mean square of the daily GPS carrier phase double difference observation value after model correction, a daily GPS multipath error model correction is obtained;

[0011] Taking the GPS multipath error model correction amount of the last day as the reference benchmark, calculate the improvement ratio of the GPS multipath error model correction amount of the remaining days relative to the GPS multipath error model correction amount of the last day;

[0012] The weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio is determined, and the multi-day multipath error modeling observation data is re-modeled as a whole based on the MHGM method to obtain an optimized MHGM multipath error correction model.

[0013] According to a multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effect provided by the present invention, multi-day multi-path error modeling observation data is collected, and the residual root mean square of the double difference observation value of the GPS carrier phase for each day is calculated and statistically obtained based on the multi-day multi-path error modeling observation data, including:

[0014] Use GNSS precision post-processing software to obtain the spatial rectangular coordinate information of each observation station;

[0015] Based on the spatial rectangular coordinate information of each observation station, according to the network solution relative positioning processing mode, any two different observation stations ( , ) relative to any satellite pair ( , ) between the GPS carrier phase double difference ambiguity, and obtain the GPS carrier phase double difference observation value residual sequence between the corresponding observation station satellite pair within the ambiguity fixed period ,in is the observation time;

[0016] Calculate the The root mean square of the residuals of all Q groups of GPS carrier phase double-difference observations on the day :

[0017] .

[0018] According to a multi-day overall modeling method for multipath error taking into account the daily correlation of multipath effect provided by the present invention, the multipath error modeling is performed using the MHGM method using the GPS carrier phase double-difference observation value residual of the last day, including:

[0019] Based on the preset grid division interval angle, the semi-celestial space domain of each observation station is grid-divided according to the altitude angle and azimuth angle, and the semi-celestial grid division scheme at the observation station is obtained;

[0020] The residual of the double difference observation of GPS carrier phase on the last day n is As input information, according to the semi-celestial grid division scheme at the observation station, multipath error modeling is performed according to the MHGM method to obtain the GPS multipath error correction model of each observation station.

[0021] According to a multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effect provided by the present invention, multi-path error correction is performed on the multi-day multi-path error modeling observation data according to the result of the multi-path error modeling to obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day after the model correction, including:

[0022] Using the GPS multipath error correction model, for any day from 1 to n days Perform multipath error correction on the GPS observation data to obtain GPS observation data after multipath error correction;

[0023] Based on the spatial rectangular coordinate information of each observation station, according to the network solution relative positioning processing mode, any two different observation stations ( , ) relative to any satellite pair ( , ), and processing the GPS observation data after the multipath error correction to obtain the GPS carrier phase double difference observation value residual update sequence between the corresponding observation station satellite pair within the ambiguity fixed period. ,in is the observation time;

[0024] Calculate the The root mean square of the residuals of all Q groups of GPS carrier phase double-difference observations on the day :

[0025] .

[0026] According to a multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effect provided by the present invention, based on the residual root mean square of the daily GPS carrier phase double-difference observation value and the residual root mean square of the daily GPS carrier phase double-difference observation value after model correction, the daily GPS multi-path error model correction is obtained:

[0027] Calculate the daily GPS multipath error model correction for days 1 to n :

[0028] .

[0029] According to a multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effect provided by the present invention, the GPS multi-path error model correction amount of the last day is used as a reference benchmark, and the improvement ratio of the GPS multi-path error model correction amount of the remaining days relative to the GPS multi-path error model correction amount of the last day is calculated, including:

[0030] Taking the GPS multipath error model correction on the nth day as the reference, calculate the improvement ratio of the multipath error model correction from the 1st to the nth day relative to the nth day. :

[0031] .

[0032] According to a multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effect provided by the present invention, the weight of the observation data of the global navigation satellite system GNSS system to be modeled in the specified satellite system corresponding to the improvement ratio is determined, and the multi-day overall modeling of multi-path error is re-performed on the multi-day multi-path error modeling observation data based on the MHGM method to obtain an optimized MHGM multi-path error correction model, including:

[0033] Constructing any day according to the MHGM method The normal equation is:

[0034]

[0035] in, is the estimated parameter of the MHGM model, , is the coefficient matrix and constant matrix of the normal equation;

[0036] The normal equations of the 1st to nth days are superimposed, and the normal equation coefficient matrix of each day is multiplied by the corresponding weight during the superposition process. ;

[0037]

[0038] The optimized MHGM multipath error correction model for multi-day overall modeling of the GNSS system to be modeled is obtained:

[0039] .

[0040] In a second aspect, the present invention further provides a multi-path error multi-day overall modeling system taking into account the daily correlation of multi-path effects, comprising:

[0041] An acquisition module is used to acquire multi-day multipath error modeling observation data, and calculate and obtain the residual root mean square of the double difference observation value of the GPS carrier phase every day based on the multi-day multipath error modeling observation data;

[0042] A modeling module is used to use the residual of the double-difference observation value of the GPS carrier phase on the last day to model the multipath error using the MHGM method;

[0043] A correction module is used to perform multipath error correction on the multipath error modeling observation data of the multi-days according to the result of the multipath error modeling, so as to obtain the residual root mean square of the double difference observation value of the GPS carrier phase every day after the model correction;

[0044] A correction module, for obtaining a daily GPS multipath error model correction based on the residual root mean square of the daily GPS carrier phase double difference observation value and the residual root mean square of the daily GPS carrier phase double difference observation value after model correction;

[0045] A ratio module is used to calculate the improvement ratio of the GPS multipath error model correction amount of the remaining days relative to the GPS multipath error model correction amount of the last day, taking the GPS multipath error model correction amount of the last day as a reference benchmark;

[0046] The reconstruction module is used to determine the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, and re-model the multi-day multi-path error multi-day overall modeling of the multi-day multi-path error modeling observation data based on the MHGM method to obtain an optimized MHGM multi-path error correction model.

[0047] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements a multi-day overall modeling method for multi-path errors that takes into account the daily correlation of multi-path effects as described in any one of the above methods.

[0048] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-day overall modeling method for multi-path errors that takes into account the daily correlation of multi-path effects as described in any of the above methods.

[0049] The multi-day overall modeling method for multi-path errors taking into account the daily correlation of multi-path effects provided by the present invention uses GPS observation data to evaluate the correlation between multi-path effects on different days and the modeling data of the last day based on the satellite orbit operation characteristics of the GPS system with a repetition period of 1 day, and determines the weighted relationship between the observation data of each group of days participating in the modeling according to the correlation. Thus, when the multi-day overall modeling is performed, a modeling result that is more suitable for the correction of the multi-path errors of the extrapolated days is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a schematic diagram of a simplified multipath model provided by the prior art where the receiver antenna is located on a vertical reflection plane;

[0052] Figure 2 This is the improvement effect of the multipath error modeling results of different test cases from 2000 to 2019 when extrapolated for 1 to 9 days, provided by the existing technology;

[0053] Figure 3 It is one of the flow diagrams of the multi-day overall modeling method of multi-path error taking into account the daily correlation of multi-path effect provided by the present invention;

[0054] Figure 4 This is the second flow chart of the multi-day overall modeling method of multi-path error taking into account the daily correlation of multi-path effects provided by the present invention;

[0055] Figure 5 It is a site distribution diagram provided by the present invention;

[0056] Figure 6 is the daily multipath error improvement ratio provided by the present invention The change sequence diagram of

[0057] Figure 7 It is a structural schematic diagram of a multi-path error multi-day overall modeling system taking into account the daily correlation of multi-path effects provided by the present invention;

[0058] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Figure 3 FIG. 1 is one of the flow charts of the multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effect provided by an embodiment of the present invention, such as Figure 3 As shown, including:

[0061] Step 100: Collect multi-day multipath error modeling observation data, and calculate and obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day based on the multi-day multipath error modeling observation data;

[0062] Step 200: Using the GPS carrier phase double-difference observation residual of the last day, the MHGM method is used to perform multipath error modeling;

[0063] Step 300: Based on the result of the multipath error modeling, multipath error correction is performed on the multi-day multipath error modeling observation data to obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day after model correction;

[0064] Step 400: Obtaining a daily GPS multipath error model correction based on the daily GPS carrier phase double-difference observation residual root mean square and the daily GPS carrier phase double-difference observation residual root mean square after model correction;

[0065] Step 500: Taking the GPS multipath error model correction amount of the last day as a reference, calculate the improvement ratio of the GPS multipath error model correction amount of the remaining days relative to the GPS multipath error model correction amount of the last day;

[0066] Step 600: Determine the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, and re-model the multi-day multi-path error modeling observation data based on the MHGM method to obtain an optimized MHGM multi-path error correction model.

[0067] Specifically, Figure 4 As shown, the invention specifically includes the following steps:

[0068] Step 1: Prepare n days of observation data for multipath error modeling. The observation data must include GPS satellite observation data. Calculate and count the observation data for each day from 1 to n days ( ) is the root mean square of the residual error of the GPS carrier phase double difference observation .

[0069] Step 1.1, use GNSS precision post-processing software (such as PANDA, GAMIT, etc.) to obtain the precise spatial rectangular coordinate information of each measuring station;

[0070] Step 1.2: Fix the coordinates of each station to the known true value obtained in step 1.1, and fix different stations ( , )satellite( , ) pairs, thereby obtaining the GPS carrier phase double difference observation residual sequence between the corresponding station satellite pair within the fixed ambiguity period ,in is the observation time;

[0071] Step 1.3: Calculate the The root mean square of the residuals of all Q groups of GPS carrier phase double-difference observations on the day :

[0072] (1)

[0073] Step 2: Use the GPS carrier phase double-difference observation residuals of the nth day to model the multipath error according to the MHGM method.

[0074] Step 2.1: Divide the semi-celestial space domain of each station into grids according to the altitude angle and azimuth angle. The grid division interval can be 2°.

[0075] Step 2.2: Take the GPS carrier phase double difference observation residual of the nth day As input information, according to the semi-celestial grid division scheme at the measuring station in step 2.1, multipath error modeling is performed according to the MHGM method to obtain the GPS multipath error correction model for each measuring station.

[0076] Step 3: Using the GPS multipath error modeling results of step 2, multipath error correction is performed on the GPS observation data of days 1 to n, and the root mean square of the residual error of the double-difference observation value of the carrier phase on each day after model correction is obtained. .

[0077] Step 3.1: For any day from day 1 to day n The GPS observation data is corrected using the GPS multipath error correction model obtained in step 2;

[0078] Step 3.2, fix the coordinates of each station to the known true value obtained in step 1.1, and process the GPS observation data after multipath error correction in step 3.1 according to the network solution relative positioning processing mode. , )satellite( , ) pairs, thereby obtaining the GPS carrier phase double difference observation residual sequence between the corresponding station satellite pair within the fixed ambiguity period ,in is the observation time;

[0079] Step 3.3: Calculate the The root mean square of the residuals of all Q groups of GPS carrier phase double-difference observations on the day :

[0080] (2)

[0081] Step 4: Calculate the GPS multipath error model correction for each day from day 1 to day n. .

[0082] (3)

[0083] Step 5: Using the GPS multipath error model correction on day n as a reference, calculate the improvement ratio of the multipath error model correction on days 1 to n relative to that on day n. .

[0084] (4)

[0085] Step 6: As the weight of the corresponding Beidou and Galileo waiting modeling GNSS system observation data, the multi-day overall multi-path error modeling is performed on the observation data from the 1st to the nth day according to the MHGM method.

[0086] Step 6.1: Construct any day according to the MHGM method The normal equation is is the estimated parameter of the MHGM model, , is the coefficient matrix and constant matrix of the normal equation;

[0087] (5)

[0088] Step 6.2: Superimpose the normal equations of the 1st to nth day, and multiply the normal equation coefficient matrix of each day by the corresponding weight during the superposition process. ;

[0089] (6)

[0090] Step 6.3: According to formula (7), the optimized MHGM multipath error correction model for multi-day overall modeling of the GNSS system to be modeled can be obtained.

[0091] (7)

[0092] It can be understood that the present invention takes into account the daily correlation of the multipath effect while modeling the multi-day multipath error effect spatial domain as a whole. The satellite orbit operation characteristics of the GPS system with a repetition period of 1 day are used to evaluate the correlation between the multipath effects of different days and the modeling data of the last day using GPS observation data, and the weighted relationship between the observation data of each group participating in the modeling day is determined based on the correlation. Thus, when modeling the multi-day as a whole, a modeling result that is more suitable for the correction of the extrapolated day multipath error is obtained.

[0093] Compared with the prior art, the present invention has the following beneficial effects:

[0094] When the existing MHGM method performs multipath error modeling, the daily observation data involved in the modeling are treated with equal weights. However, when the present invention performs overall modeling of multi-day observation data, it is based on the correlation between the multipath error effects of the last day, and adaptively adjusts the weight ratio relationship of the observation data of each day in the MHGM multi-day overall modeling, thereby obtaining a multipath error correction model that is more suitable for extrapolated day observation data.

[0095] The multi-day multi-path error modeling method proposed in the present invention can be applied to various types of GNSS satellite navigation systems, and the observation data involved in the modeling does not need to be limited to only the observation data within the previous satellite orbit repetition period (the original MHGM method uses the observation data within the previous satellite orbit repetition period of the GNSS system to be modeled), which further expands the applicability and effectiveness of the MHGM method in the overall modeling of multi-day multi-path errors.

[0096] Furthermore, in one embodiment, in order to verify the effectiveness of the present invention, five stations with a distance of about 15 km between them in a province were collected for verification analysis. Figure 5 As shown in the figure, the data was collected from 023 to 041 days in 2024, with a sampling interval of 1 second. The GNSS precision data processing software PANDA developed by the Satellite Navigation and Positioning Technology Research Center of a university was used to fix the coordinates of the measuring stations to their known true values, and the carrier phase double difference observation residuals of the GPS and Beidou systems between the stations were obtained. Considering that the micro-orbit repetition period of the GPS system is 1 day, and the satellite orbit repetition period of the Beidou system is 7 days. The multipath error modeling was carried out with the observation data from 023 to 035 days in 2024, and the demonstration and analysis of the effectiveness of the multipath error modeling was carried out with the observation data from 036 to 041 days.

[0097] According to the modeling method proposed in the present invention, the multipath error modeling is firstly carried out using the residual of the double-difference observation value of the GPS carrier phase on day 035 according to the MHGM method, and the multipath error correction effect of each day from day 023 to day 035 is evaluated. Figure 6 is the daily multipath error improvement ratio evaluated according to formula (4) As can be seen from the figure, there are significant differences in the correlation of the GPS multipath error effect on each day. The multipath error effect on days 031 and 033 is relatively different from that on day 035.

[0098] by As the multipath error modeling weight corresponding to the Beidou system observation data, the MHGM method is used to perform multi-day overall modeling based on the normal equation superposition according to formula (6), and the MHGM multi-day overall modeling results of equal-weighted modeling are generated for comparison. The multipath error correction effect of the Beidou system carrier phase double difference observation residuals from 036 to 041 is used to verify the effectiveness of the method proposed in the present invention.

[0099] As shown in Table 1, if multipath error correction is not performed, the RMS average of the residual double-difference observation value of the carrier phase of the Beidou system on each test day is 1.52 cm. If the traditional MHGM method is used for equal-weighted modeling, the RMS average after model correction is 1.36 cm. However, by adopting the multi-day overall modeling method of multipath error that takes into account the daily correlation of multipath effects proposed in the present invention, through the unequal-weighted MHGM multi-day superposition overall modeling, the RMS of the residual double-difference observation value of the carrier phase of the Beidou system after model correction can be further reduced to 1.28 cm, which verifies the application effectiveness of the present invention.

[0100] Table 1 Verifies the RMS of the residual error of the double-difference observation value of the carrier phase of the Beidou system before and after the multipath model correction

[0101]

[0102] The multi-path error multi-day overall modeling system taking into account the daily correlation of multi-path effects provided by the present invention is described below. The multi-path error multi-day overall modeling system taking into account the daily correlation of multi-path effects described below and the multi-path error multi-day overall modeling method taking into account the daily correlation of multi-path effects described above can be referenced to each other.

[0103] Figure 7 is a schematic diagram of the structure of a multi-day overall modeling system for multi-path errors taking into account the daily correlation of multi-path effects provided by an embodiment of the present invention, such as Figure 7 As shown, it includes: an acquisition module 71, a modeling module 72, a correction module 73, a correction module 74, a ratio module 75 and a reconstruction module 76, wherein:

[0104] The acquisition module 71 is used to acquire multi-day multipath error modeling observation data, and calculate and statistically obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day based on the multi-day multipath error modeling observation data; the modeling module 72 is used to use the residual of the double-difference observation value of the GPS carrier phase of the last day to perform multipath error modeling using the MHGM method; the correction module 73 is used to perform multipath error correction on the multi-day multipath error modeling observation data according to the result of the multipath error modeling, and obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day after the model correction; the correction module 74 is used to calculate the residual root mean square of the double-difference observation value of the GPS carrier phase every day based on the residual root mean square of the double-difference observation value of the GPS carrier phase and the The residual root mean square of the double-difference observation value of the GPS carrier phase of each day after model correction is used to obtain the daily GPS multipath error model correction; the ratio module 75 is used to calculate the improvement ratio of the GPS multipath error model correction of the remaining days relative to the GPS multipath error model correction of the last day with the GPS multipath error model correction of the last day as the reference benchmark; the reconstruction module 76 is used to determine the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, and re-model the multipath error multi-day overall modeling of the multi-day multipath error modeling observation data based on the MHGM method to obtain the optimized MHGM multipath error correction model.

[0105] Figure 8 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 8As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute a multi-day overall modeling method for multi-path errors that takes into account the daily correlation of multi-path effects. The method includes: collecting multi-day multi-path error modeling observation data, and calculating and statistically obtaining the residual root mean square of the double-difference observation value of the GPS carrier phase for each day based on the multi-day multi-path error modeling observation data; using the residual of the double-difference observation value of the GPS carrier phase of the last day, adopting the MHGM method to perform multi-path error modeling; performing multi-path error correction on the multi-day multi-path error modeling observation data according to the result of the multi-path error modeling, and obtaining the residual root mean square of the double-difference observation value of the GPS carrier phase for each day after the model correction; and calculating the residual root mean square of the double-difference observation value of the GPS carrier phase for each day based on the multi-path error modeling observation data. The residual root mean square of the double-difference observation values ​​of the GPS carrier phase on each day and the residual root mean square of the double-difference observation values ​​of the GPS carrier phase on each day after model correction are used to obtain the daily GPS multipath error model correction; taking the GPS multipath error model correction value of the last day as a reference benchmark, calculating the improvement ratio of the GPS multipath error model correction value of the remaining days relative to the GPS multipath error model correction value of the last day; determining the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, and re-modeling the multi-day multipath error overall modeling for the multi-day multipath error modeling observation data based on the MHGM method to obtain the optimized MHGM multipath error correction model.

[0106] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0107] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for multi-day overall modeling of multi-path errors taking into account the daily correlation of multi-path effects provided by the above-mentioned methods is implemented. The method comprises: collecting multi-day multi-path error modeling observation data, and calculating and statistically obtaining the residual root mean square of the double-difference observation value of the GPS carrier phase for each day based on the multi-day multi-path error modeling observation data; using the residual of the double-difference observation value of the GPS carrier phase of the last day to perform multi-path error modeling using the MHGM method; and performing multi-path error correction on the multi-day multi-path error modeling observation data according to the result of the multi-path error modeling to obtain the daily GPS carrier phase residual root mean square of the ... phase double difference observation residual root mean square; based on the daily GPS carrier phase double difference observation residual root mean square and the daily GPS carrier phase double difference observation residual root mean square after model correction, obtain the daily GPS multipath error model correction; taking the last day's GPS multipath error model correction as a reference, calculate the improvement ratio of the remaining days' GPS multipath error model correction relative to the last day's GPS multipath error model correction; determine the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, re-model the multi-day multipath error overall modeling for the multi-day multipath error modeling observation data based on the MHGM method, and obtain the optimized MHGM multipath error correction model.

[0108] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-day overall modeling method for multipath errors taking into account the daily correlation of multipath effects, characterized in that: include: Collecting multi-day multipath error modeling observation data, and calculating and statistically obtaining the residual root mean square of the double difference observation value of the global positioning system GPS carrier phase every day based on the multi-day multipath error modeling observation data; The multipath error modeling is carried out by using the double-difference observation residual of GPS carrier phase on the last day and adopting the MHGM method of multipath error semi-celestial space domain modeling. Based on the result of the multipath error modeling, multipath error correction is performed on the multi-day multipath error modeling observation data to obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day after model correction; Based on the residual root mean square of the daily GPS carrier phase double difference observation value and the residual root mean square of the daily GPS carrier phase double difference observation value after model correction, a daily GPS multipath error model correction is obtained; Taking the GPS multipath error model correction amount of the last day as the reference benchmark, calculate the improvement ratio of the GPS multipath error model correction amount of the remaining days relative to the GPS multipath error model correction amount of the last day; The weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio is determined, and the multi-day multi-path error modeling observation data is re-modeled as a whole based on the MHGM method to obtain an optimized MHGM multi-path error correction model.

2. The multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effects according to claim 1 is characterized in that: Collect multi-day multipath error modeling observation data, and calculate and obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day based on the multi-day multipath error modeling observation data, including: Use GNSS precision post-processing software to obtain the spatial rectangular coordinate information of each observation station; Based on the spatial rectangular coordinate information of each observation station, according to the network solution relative positioning processing mode, any two different observation stations are fixed , Relative to any satellite pair , The GPS carrier phase double difference ambiguity between the two satellites is obtained to obtain the residual sequence of GPS carrier phase double difference observation values ​​between the corresponding observation station satellite pair within the fixed ambiguity period. ,in is the observation time; Calculate the The root mean square of the residuals of all Q groups of GPS carrier phase double-difference observations on the day : 。 3. The multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effects according to claim 1 is characterized in that: The MHGM method is used to model multipath errors using the double-difference residuals of the GPS carrier phase on the last day, including: Based on the preset grid division interval angle, the semi-celestial space domain of each observation station is grid-divided according to the altitude angle and azimuth angle, and the semi-celestial grid division scheme at the observation station is obtained; The residual of the double difference observation of GPS carrier phase on the last day n is As input information, according to the semi-celestial grid division scheme at the observation station, multipath error modeling is performed according to the MHGM method to obtain the GPS multipath error correction model of each observation station.

4. The multi-day overall modeling method for multipath error taking into account the daily correlation of multipath effects according to claim 3 is characterized in that: Based on the result of the multipath error modeling, multipath error correction is performed on the multi-day multipath error modeling observation data to obtain the residual root mean square of the double-difference observation value of the GPS carrier phase every day after model correction, including: Using the GPS multipath error correction model, for any day from 1 to n days Perform multipath error correction on the GPS observation data to obtain GPS observation data after multipath error correction; Based on the spatial rectangular coordinate information of each observation station, according to the network solution relative positioning processing mode, any two different observation stations are fixed , Relative to any satellite pair , The GPS carrier phase double difference ambiguity between the two satellites is processed to obtain the GPS carrier phase double difference observation residual update sequence between the corresponding observation station satellite pair within the ambiguity fixed period. ,in is the observation time; Calculate the The root mean square of the residuals of all Q groups of GPS carrier phase double-difference observations on the day : 。 5. The multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effects according to claim 1 is characterized in that: Based on the daily GPS carrier phase double difference observation residual root mean square and the daily GPS carrier phase double difference observation residual root mean square after model correction, the daily GPS multipath error model correction is obtained: Calculate the daily GPS multipath error model correction for days 1 to n : 。 6. The multi-day overall modeling method for multi-path error taking into account the daily correlation of multi-path effects according to claim 1 is characterized in that: Taking the GPS multipath error model correction of the last day as the reference, calculate the improvement ratio of the GPS multipath error model correction of the remaining days relative to the GPS multipath error model correction of the last day, including: Taking the GPS multipath error model correction on the nth day as the reference, calculate the improvement ratio of the multipath error model correction from the 1st to the nth day relative to the nth day. : 。 7. The multi-day overall modeling method for multipath errors taking into account the daily correlation of multipath effects according to claim 1 is characterized in that: Determine the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, re-model the multi-day multi-path error multi-day overall modeling of the multi-day multi-path error modeling observation data based on the MHGM method, and obtain the optimized MHGM multi-path error correction model, including: Constructing any day according to the MHGM method The normal equation is: in, is the estimated parameter of the MHGM model, , is the coefficient matrix and constant matrix of the normal equation; The normal equations of the 1st to nth days are superimposed, and the normal equation coefficient matrix of each day is multiplied by the corresponding weight during the superposition process. ; The optimized MHGM multipath error correction model for multi-day overall modeling of the GNSS system to be modeled is obtained: 。 8. A multi-day overall modeling system for multi-path errors taking into account the daily correlation of multi-path effects, characterized in that: include: An acquisition module is used to acquire multi-day multipath error modeling observation data, and calculate and obtain the residual root mean square of the double difference observation value of the GPS carrier phase every day based on the multi-day multipath error modeling observation data; A modeling module is used to use the residual of the double-difference observation value of the GPS carrier phase on the last day to model the multipath error using the MHGM method; A correction module is used to perform multipath error correction on the multipath error modeling observation data of the multi-days according to the result of the multipath error modeling, so as to obtain the residual root mean square of the double difference observation value of the GPS carrier phase every day after the model correction; A correction module, for obtaining a daily GPS multipath error model correction based on the residual root mean square of the daily GPS carrier phase double difference observation value and the residual root mean square of the daily GPS carrier phase double difference observation value after model correction; A ratio module is used to calculate the improvement ratio of the GPS multipath error model correction amount of the remaining days relative to the GPS multipath error model correction amount of the last day, taking the GPS multipath error model correction amount of the last day as a reference benchmark; The reconstruction module is used to determine the weight of the GNSS system observation data to be modeled in the specified satellite system corresponding to the improvement ratio, and re-model the multi-day multi-path error multi-day overall modeling of the multi-day multi-path error modeling observation data based on the MHGM method to obtain an optimized MHGM multi-path error correction model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the multi-day overall modeling method of multipath error taking into account the daily correlation of multipath effects as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-day integrated modeling method for multipath errors taking into account the daily correlation of multipath effects as claimed in any one of claims 1 to 7 is implemented.

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