Method and device for establishing overlapping frequency multi-path error correction model

Abstract

The invention relates to a method and device for establishing an overlapping frequency multipath error correction model, and belongs to the technical field of data processing, and the method comprises the steps: obtaining the carrier phase observation data of a plurality of satellites at an overlapping frequency; the plurality of satellites belong to a plurality of different satellite systems, and the plurality of satellites comprise a reference satellite and at least three other common-view satellites; according to carrier phase observation data, phase double-difference residual errors between other common-view satellites and the reference satellite at different observation times are determined; the phase double-difference residual errors between the other common-view satellites and the reference satellite are converted into phase single-difference residual errors of the satellites; and establishing a multi-system fusion multi-path hemispherical graph model according to the phase single-difference residual error and a DBSCAN clustering algorithm. According to the method, the multi-system fusion MHM model is established based on the multi-path error of the overlapping frequency of the multiple satellite systems, modeling data can be expanded, the modeling data acquisition period can be shortened, the modeling efficiency can be improved, and the modeling number can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for establishing an overlapping frequency multipath error correction model. Background Art

[0002] Multipath error is one of the main sources of error affecting Global Navigation Satellite System (GNSS) positioning accuracy. Multipath error occurs when reflected and direct signals interfere with each other, causing satellite signal observations to deviate from their true values.

[0003] While selecting a better observation environment and improving hardware can also mitigate multipath errors, their application scenarios are limited and their cost is high. Therefore, methods based on data processing algorithms have attracted considerable attention. Multipath mitigation methods based on random models are prone to observation loss, while methods that decompose and filter observation signals are often used for data post-processing. Sidereal filtering (SF) exploits the temporal repetition of multipath errors, effectively correcting for them, but is susceptible to orbital maneuvers. The multipath hemispherical map (MHM) model, on the other hand, exploits the spatial repetition of multipath errors and can be used for real-time correction. Its core concept is to divide the sky above the observation station into grid cells, calculate the multipath errors of the observed satellites, assign the errors to corresponding grid cells based on the satellite's spatial position, and calculate the multipath error corrections for each grid cell to correct for the target satellite's multipath error.

[0004] However, building a robust MHM model typically requires historical data for at least one orbital repetition period. For the Galileo satellite system (which has an orbital repetition period of approximately 10 days) and the BDS satellite system (whose MEO satellites have an orbital repetition period of approximately 7 days), this can take weeks to build, severely impacting modeling efficiency. For multi-frequency, multi-satellite systems, separate MHM models must be built for each satellite system and frequency, significantly increasing the computational workload. Furthermore, the requirement for the ambient environment to remain constant during data collection poses significant challenges. Summary of the Invention

[0005] In view of this, it is necessary to provide a method for establishing an overlapping frequency multipath error correction model, an observation value correction method and a device to solve the problems of low modeling efficiency and limited data volume in the existing technology.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a method for establishing an overlapping frequency multipath error correction model, comprising: Acquiring carrier phase observation data of a plurality of satellites at overlapping frequencies; wherein the plurality of satellites belong to a plurality of different satellite systems, and the plurality of satellites include a reference satellite and at least three other common-view satellites; Determining, based on the carrier phase observation data, a phase double difference residual between each of the other common-view satellites and the reference satellite at different observation times; Converting the phase double difference residuals between each of the other common-view satellites and the reference satellite into the phase single difference residuals of each of the satellites; A multi-system fusion multi-path hemispherical graph model is established based on the phase single-difference residual and the DBSCAN clustering algorithm.

[0007] In a possible implementation, determining, based on the carrier phase observation data, the phase double-difference residuals between each of the other common-view satellites and the reference satellite at different observation times includes: Based on the carrier phase observation data, a phase double-difference observation equation is constructed between each of the other common-view satellites and the reference satellite at different observation times using a short baseline difference technique; wherein the phase double-difference observation equation includes a first phase double-difference observation equation constructed based on the other common-view satellites belonging to the same satellite system and the reference satellite, and a second phase double-difference observation equation constructed based on the other common-view satellites belonging to different satellite systems and the reference satellite; the first phase double-difference observation equation and the second phase double-difference observation equation include a multipath error term and an observation noise term; the second phase double-difference observation equation also includes an inter-system bias term; the inter-system bias term is determined by a random walk model; A phase double difference residual is determined according to the first phase double difference observation equation and the second phase double difference observation equation; the phase double difference residual includes a multipath error term and an observation noise term.

[0008] In a possible implementation, converting the phase double-difference residuals between each of the other common-view satellites and the reference satellite into the phase single-difference residuals of each of the satellites includes: The phase double difference residuals between each of the other common-view satellites and the reference satellite are converted into the phase single difference residuals of each of the satellites through a zero mean assumption method.

[0009] In a possible implementation, establishing a multi-system fusion multi-path hemispherical graph model based on the phase single-difference residual and the DBSCAN clustering algorithm includes: Dividing the sky map into a plurality of grid cells, and allocating the phase single difference residual of each satellite at different observation times to a corresponding grid cell according to the position information of each satellite in the sky at different observation times; A multi-system fusion multi-path hemispherical graph model is established based on the phase single-difference residuals of each grid unit and the DBSCAN clustering algorithm.

[0010] In a possible implementation, dividing the sky map into a plurality of grid cells includes: Divide the sky map into grid cells according to a preset resolution.

[0011] In a possible implementation, the method further includes: Before establishing the multi-system fusion multi-path hemispherical graph model, calculating the standard deviation of the phase single difference residual of each grid unit; Determining the phase single-difference residual abnormal value of each of the grid cells according to the standard deviation of each of the grid cells; Delete the phase single-difference residual anomaly.

[0012] In a possible implementation, establishing a multi-system fusion multi-path hemispherical graph model based on the phase single-difference residual of each grid unit and the DBSCAN clustering algorithm includes: Clustering the phase single difference residuals of each grid cell according to satellite information corresponding to the phase single difference residuals to obtain cluster clusters; wherein the satellite information includes the satellite's altitude angle, azimuth angle, altitude change rate, and signal-to-noise ratio; An average value of the phase single difference residuals of each cluster is used as the multipath error correction value of the cluster.

[0013] In a possible implementation, the different satellite systems include a GPS satellite system, a BDS satellite system, and a Galileo satellite system.

[0014] In a second aspect, the present invention further provides a phase observation value correction method, comprising: Obtain the satellite carrier phase observation value to be corrected; Determining a multipath error correction value according to a multipath error correction model; the multipath error correction model is a multipath error correction model established based on any of the above methods; The satellite carrier phase observation value to be corrected is corrected according to the multipath error correction value.

[0015] In a third aspect, the present invention further provides an overlapping frequency multipath error correction model establishment device, comprising: an observation data acquisition module, configured to acquire carrier phase observation data of a plurality of satellites at overlapping frequencies; wherein the plurality of satellites belong to a plurality of different satellite systems, and the plurality of satellites include a reference satellite and at least three other common-view satellites; a double-difference residual determination module, configured to determine, based on the carrier phase observation data, a phase double-difference residual between each of the other common-view satellites and the reference satellite at different observation times; a single-difference residual determination module, configured to convert the phase double-difference residuals between each of the other common-view satellites and the reference satellite into the phase single-difference residuals of each of the satellites; The model building module is used to establish a multi-system fusion multi-path hemispherical graph model based on the phase single difference residual and the DBSCAN clustering algorithm.

[0016] The beneficial effects of the present invention are: When constructing a multi-system fusion MHM model, the present invention first collects carrier phase observation data from multiple satellites at overlapping frequencies; wherein the multiple satellites belong to multiple different satellite systems, and the multiple satellites include a reference satellite and at least three other common-view satellites. Then, based on the carrier phase observation data, the phase double-difference residuals between each other common-view satellite and the reference satellite at different observation times are determined. Through inter-station differences and inter-satellite differences, other errors besides multipath errors, such as satellite clock errors, receiver clock errors, atmospheric errors, etc., can be eliminated or weakened. Then, since the phase double-difference residuals include the observation errors of the reference satellite and cannot accurately reflect the multipath errors of a single satellite, the present invention further converts the phase double-difference residuals between each other common-view satellite and the reference satellite into phase single-difference residuals of each satellite. Finally, a fusion MHM model is established based on the phase single-difference residuals of each satellite and the DBSCAN clustering algorithm. In summary, compared with the existing technology of establishing an MHM model for each satellite system separately, the present invention establishes a multi-system fusion MHM model based on the multipath errors of multiple satellite systems, which can expand the modeling data, shorten the modeling data collection cycle, improve the modeling efficiency, and reduce the number of modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of an embodiment of a method for establishing an overlapping frequency multipath error correction model provided by the present invention; Figure 2 For the present invention Figure 1 A schematic flow chart of an embodiment of S102; Figure 3 A schematic flow chart of an embodiment of a phase observation value correction method provided by the present invention; Figure 4 A schematic diagram of the overall correction process provided by the present invention; Figure 5 This is a structural diagram of an embodiment of the overlapping frequency multipath error correction model establishment device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, the meaning of "plurality" is two or more. The "first", "second", etc. involved in the embodiments of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence, nor are they used to indicate or imply their relative importance or implicitly indicate the number of technical features indicated. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more.

[0021] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0022] Reference Figure 1 , which shows a flow chart of an embodiment of a method for establishing an overlapping frequency multipath error correction model provided by the present invention, the method comprising: S101, acquiring carrier phase observation data of a plurality of satellites at overlapping frequencies; wherein the plurality of satellites belong to a plurality of different satellite systems, and the plurality of satellites include a reference satellite and at least three other common-view satellites.

[0023] The carrier phase observation data may include: carrier phase observation value, observation time, carrier frequency, receiver information of the phase observation data, satellite information of the observed satellite, etc.

[0024] Satellite systems may include: GPS satellite system, BDS satellite system and Galileo satellite system, etc.

[0025] The reference satellite can be one of multiple satellites, and the other co-viewing satellites are satellites other than the reference satellite, and these satellites are co-viewing each other. Satellite co-viewing means that a satellite is observed by multiple receivers (such as a base station and a rover) at the same time.

[0026] S102 : determining the phase double difference residuals between other common-view satellites and the reference satellite at different observation times based on the carrier phase observation data.

[0027] The phase double difference residual is expressed as the difference between the actual phase double difference value and the theoretical phase double difference value. The theoretical phase double difference value can be calculated using a theoretical model.

[0028] S103: Convert the double-difference phase residuals between each of the other common-view satellites and the reference satellite into the single-difference phase residuals of each satellite.

[0029] Since the double-difference residual includes the influence of the reference satellite observation noise, it is difficult to accurately reflect the multipath error of a single satellite. Therefore, it is necessary to convert the double-difference residual into a single-difference residual.

[0030] S104: Establish a multi-system fusion multi-path hemispherical graph model based on the phase single-difference residual and the DBSCAN clustering algorithm.

[0031] The DBSCAN clustering algorithm refers to a density-based spatial clustering algorithm with noise.

[0032] This embodiment models the overlapping frequencies of different satellite systems together. For example, the carrier frequency of GPS L1, BDS B1C, and Galileo E1 is 1575.42 MHz, which is an overlapping carrier frequency. Therefore, the phase observation data of these three systems at this carrier frequency can be used to model the system together. If there are multiple overlapping carrier frequencies, each overlapping carrier frequency is modeled separately.

[0033] The multipath error correction model establishment method provided in this embodiment can be applied to a multipath error correction model establishment system. The multipath error correction model establishment system can be based on a software system running on a terminal device. The terminal device can be a tablet computer, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), mobile phone, or other terminal device. This embodiment does not impose any restrictions on the specific type of terminal device.

[0034] In summary, when constructing a multi-system fusion MHM model, this embodiment first collects carrier phase observation data from multiple satellites at overlapping frequencies; wherein the multiple satellites belong to multiple different satellite systems, and the multiple satellites include a reference satellite and at least three other common-view satellites. Then, based on the carrier phase observation data, the phase double-difference residuals between each other common-view satellite and the reference satellite at different observation times are determined. Through inter-station differences and inter-satellite differences, other errors other than multipath errors, such as satellite clock errors, receiver clock errors, atmospheric errors, etc., can be eliminated or weakened. Then, since the phase double-difference residuals include the observation errors of the reference satellite and are difficult to accurately reflect the multipath errors of a single satellite, the present invention further converts the phase double-difference residuals between each other common-view satellite and the reference satellite into the phase single-difference residuals of each satellite. Finally, a fusion MHM model is established based on the phase single-difference residuals of each satellite and the DBSCAN clustering algorithm. Compared with the existing technology of establishing an MHM model for each satellite system separately, this embodiment establishes a multi-system fusion MHM model based on the multipath errors of multiple satellite systems, which can expand the modeling data, shorten the modeling data collection cycle, improve the modeling efficiency, and reduce the number of modeling.

[0035] In some embodiments of the present invention, Figure 2 As shown, step S102 includes: S201, constructing a phase double-difference observation equation between each other common-view satellite and the reference satellite at different observation times using a short baseline difference technique based on the carrier phase observation data.

[0036] Among them, the phase double-difference observation equation includes a first phase double-difference observation equation constructed based on other common-view satellites and a reference satellite belonging to the same satellite system, and a second phase double-difference observation equation constructed based on other common-view satellites and a reference satellite belonging to different satellite systems; the first phase double-difference observation equation and the second phase double-difference observation equation include multipath error terms and observation noise terms; the second phase double-difference observation equation also includes an inter-system bias term; the inter-system bias term is determined by a random walk model.

[0037] In one example, when the satellite system includes GPS, BDS and Galileo systems, it is assumed that the GPS system is selected. G m The satellite is a reference satellite, and the phase double difference observation equation can be specifically expressed by the following formula:

[0038] Where: Denote GPS, BDS and Galileo systems respectively, All represent satellites. represents the double difference operator, and represent the base station and mobile station respectively, represents the overlapping frequency, represents the carrier phase observation value, is the distance between the satellite and the receiver, Overlap frequency The corresponding wavelength, is the fuzziness, is the multipath error, is the observation noise, and are the inter-system biases between GPS, BDS and Galileo systems respectively.

[0039] The inter-system deviation is estimated using the random walk model. Specifically,

[0040] Where: represents the epoch, that is, the observation time, express or ; yes In the epoch The initial value when It is the epoch hour estimated value of; For noise; It is the epoch Department The prior variance of It is the epoch Department variance; represents the noise variance.

[0041] Because this embodiment uses short-baseline differencing technology to construct a double-difference phase observation equation, it eliminates satellite and receiver clock errors and significantly reduces atmospheric errors. The main remaining errors are multipath error and observation noise. Furthermore, because this embodiment uses tightly coupled relative positioning, inter-system bias also exists. Based on this, this embodiment further estimates the inter-system bias to subsequently obtain a more "pure" multipath error.

[0042] S202 , determining a phase double difference residual according to the first phase double difference observation equation and the second phase double difference observation equation; the phase double difference residual includes a multipath error term and an observation noise term.

[0043] Specifically, the least squares ambiguity reduction correlation adjustment method (LAMBDA) can be used to fix the ambiguity, and then the phase double difference residual is calculated according to the above phase double difference observation equation. The phase double difference residual is expressed as follows:

[0044] Where: is the phase double difference residual.

[0045] In some embodiments of the present invention, S103 includes: converting the phase double difference residuals between each other common view satellite and the reference satellite into the phase single difference residual of each satellite by using a zero mean assumption method.

[0046] The calculation process is as follows:

[0047] Where: is the phase single-difference residual, Indicates all GPS / BDS / Galileo satellites involved in the solution of this epoch, with the constraints being , represents the weight factor of the satellite in the baseline solution, The corresponding The altitude angle of the satellite.

[0048] In some embodiments of the present invention, S104 includes: dividing the sky map into multiple grid cells, and allocating the phase single difference residuals of each satellite at different observation times to corresponding grid cells according to the azimuth information of each satellite in the sky at different observation times; establishing a multi-system fusion MHM model based on the phase single difference residuals of each grid cell and the DBSCAN clustering algorithm.

[0049] Depending on the resolution r Divide the sky map above the mobile station into grid cells. i The azimuth and elevation angle ranges of each grid cell are:

[0050] Where: Indicates rounding down. , .

[0051] According to the azimuth and elevation angle ranges of the grid cells and the position information of each satellite in the sky at different observation times, the phase single difference residuals of each satellite at different observation times can be assigned to the corresponding grid cells.

[0052] In some embodiments of the present invention, the method further includes: before establishing the MHM model, calculating the standard deviation of the phase single difference residual of each grid cell; determining the phase single difference residual anomaly of each grid cell based on the standard deviation of each grid cell; and deleting the phase single difference residual anomaly.

[0053] In this embodiment, the mean and standard deviation of the phase single difference residuals may be calculated, and the phase single difference residuals outside the mean by plus or minus three standard deviations are treated as phase single difference residual anomalies and eliminated.

[0054] In some embodiments of the present invention, the step of establishing a multi-system fusion MHM model based on the phase single difference residuals of each grid cell and the DBSCAN clustering algorithm includes: clustering the phase single difference residuals of each grid cell according to the satellite information corresponding to the phase single difference residuals to obtain cluster clusters; wherein the satellite information includes the satellite's altitude angle, azimuth angle, altitude change rate and signal-to-noise ratio; and using the average value of the phase single difference residuals of each cluster cluster as the multipath error correction value of the cluster cluster.

[0055] In one example, the feature vector can be constructed first Q , the eigenvector Q The altitude angle of each satellite , azimuth , altitude angle change rate and signal-to-noise ratio Composition, feature vector It is expressed as follows:

[0056] Where: It is the vertical distance from the phase center of the antenna to the reflecting surface.

[0057] Then all the eigenvectors are normalized and clustered using the DBSCAN algorithm. Neighborhood and minimum number of points , cluster the residual data in each grid unit separately to form Clusters, frequency Next The multipath error correction value corresponding to the cluster for:

[0058] Where: It is a cluster The number of residuals in .

[0059] This embodiment uses the DBSCAN clustering algorithm to perform refined classification on the residual data, which can improve the accuracy of the MHM model.

[0060] Reference Figure 3 , which shows a flow chart of an embodiment of a phase observation value correction method provided by the present invention, the method comprising: S301, obtaining the satellite carrier phase observation value to be corrected.

[0061] S302 , determining a multipath error correction value according to a multipath error correction model; the multipath error correction model is a multipath error correction model established based on the above method.

[0062] S303: Correct the satellite carrier phase observation value to be corrected according to the multipath error correction value.

[0063] The target grid cell can be determined from the multipath error correction model based on the satellite information corresponding to the satellite carrier phase observation to be corrected. The multipath error correction value can then be determined based on the target grid cell. The corrected phase observation value can then be obtained by subtracting the satellite carrier phase observation to be corrected from the multipath error correction value. The satellite information includes the satellite's elevation angle and azimuth angle.

[0064] In some embodiments of the present invention, the correction method specifically includes: obtaining the satellite carrier phase observation value to be corrected corresponding to the satellite The eigenvector of , then normalize the feature vector and assign it to the corresponding target grid cell according to its altitude and azimuth. Secondly, calculate the Euclidean distance from its feature vector to all points in the grid , filter out The points are taken as neighborhood points; then, according to the number of points in the neighborhood and The cluster to which the point belongs is determined by the relationship between , that is, if the point is a core point or boundary point of a cluster, it is classified into that cluster. If the point belongs to multiple clusters at the same time, the cluster with the closest core point is selected. Finally, the phase observation value is corrected:

[0065] Where: Indicates the Satellite frequency The carrier phase observation value on Indicates satellite Cluster In frequency The corresponding multipath error correction value; Indicates the first Satellite frequency The carrier phase observation value on .

[0066] Reference Figure 4, which shows a schematic diagram of the overall process provided by the present invention. In the modeling stage, based on the observation files and navigation files of the GPS satellite system, the BDS satellite system and the Galileo satellite system, a tightly combined phase double-difference observation equation is constructed, and the phase double-difference residual is obtained according to the tightly combined phase double-difference observation equation, and then the phase single-difference residual is obtained. Finally, based on the phase single-difference residual, a fused MHM model is established in combination with the DBSCAN clustering algorithm. In the correction stage, the satellite carrier phase observation value to be corrected is obtained from the observation file, and then the multipath error correction value is determined by the fused MHM model to correct the satellite carrier phase observation value to be corrected. This embodiment establishes a fused MHM model based on the overlapping frequency of GPS / BDS / Galileo, which can improve the modeling efficiency while ensuring the amount of modeling data and construct a robust MHM model. At the same time, processing the data based on the DBSCAN clustering algorithm can improve the model accuracy.

[0067] Reference Figure 5 , which shows a schematic structural diagram of an embodiment of an apparatus for establishing an overlapping frequency multipath error correction model provided by the present invention, wherein the apparatus 500 includes: An observation data acquisition module 501 is configured to acquire carrier phase observation data of multiple satellites at overlapping frequencies; wherein the multiple satellites belong to multiple different satellite systems and include a reference satellite and at least three other common-view satellites; A double-difference residual determination module 502 is configured to determine the phase double-difference residuals between each other common-view satellite and the reference satellite at different observation times based on the carrier phase observation data; The single-difference residual determination module 503 is used to convert the phase double-difference residuals between each other common-view satellite and the reference satellite into the phase single-difference residual of each satellite; The model building module 504 is used to build a multi-system fusion multi-path hemispherical graph model based on the phase single-difference residual and the DBSCAN clustering algorithm.

[0068] When constructing a multi-system fusion MHM model, this embodiment first collects carrier phase observation data from multiple satellites at overlapping frequencies; wherein the multiple satellites belong to multiple different satellite systems, and the multiple satellites include a reference satellite and at least three other common-view satellites. Then, based on the carrier phase observation data, the phase double-difference residuals between each other common-view satellite and the reference satellite at different observation times are determined. Through inter-station differences and inter-satellite differences, other errors besides multipath error, such as satellite clock error, receiver clock error, atmospheric error, etc., can be eliminated or weakened. Then, since the phase double-difference residuals include the observation error of the reference satellite and cannot accurately reflect the multipath error of a single satellite, the present invention further converts the phase double-difference residuals between each other common-view satellite and the reference satellite into the phase single-difference residuals of each satellite. Finally, the multi-system fusion MHM model is established based on the phase single-difference residuals of each satellite and the DBSCAN clustering algorithm. In summary, compared with the existing technology of establishing an MHM model for each satellite system separately, the present invention integrates the multipath errors of multiple satellite systems to establish an MHM model, which can expand the modeling data, shorten the modeling data collection cycle, improve the modeling efficiency, and reduce the number of modeling.

[0069] It should be noted that the implementation principles or implementation processes of the above modules can refer to the embodiment of the above multipath error correction model establishment method, and will not be described in detail here.

[0070] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0071] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for establishing an overlapping frequency multipath error correction model, characterized in that: include: Acquiring carrier phase observation data of a plurality of satellites at overlapping frequencies; wherein the plurality of satellites belong to a plurality of different satellite systems, and the plurality of satellites include a reference satellite and at least three other common-view satellites; Determining, based on the carrier phase observation data, a phase double difference residual between each of the other common-view satellites and the reference satellite at different observation times; Converting the phase double difference residuals between each of the other common-view satellites and the reference satellite into the phase single difference residuals of each of the satellites; A multi-system fusion multi-path hemispherical graph model is established based on the phase single-difference residual and the DBSCAN clustering algorithm.

2. The overlapping frequency multipath error correction model establishment method according to claim 1, characterized in that: The determining, based on the carrier phase observation data, the phase double difference residuals between each of the other common-view satellites and the reference satellite at different observation times includes: Based on the carrier phase observation data, a phase double-difference observation equation is constructed between each of the other common-view satellites and the reference satellite at different observation times using a short baseline difference technique; wherein the phase double-difference observation equation includes a first phase double-difference observation equation constructed based on the other common-view satellites belonging to the same satellite system and the reference satellite, and a second phase double-difference observation equation constructed based on the other common-view satellites belonging to different satellite systems and the reference satellite; the first phase double-difference observation equation and the second phase double-difference observation equation include a multipath error term and an observation noise term; the second phase double-difference observation equation also includes an inter-system bias term; the inter-system bias term is determined by a random walk model; A phase double difference residual is determined according to the first phase double difference observation equation and the second phase double difference observation equation; the phase double difference residual includes a multipath error term and an observation noise term.

3. The overlapping frequency multipath error correction model establishment method according to claim 1, characterized in that: The converting the phase double difference residuals between each of the other common-view satellites and the reference satellite into the phase single difference residuals of each of the satellites comprises: The phase double difference residuals between each of the other common-view satellites and the reference satellite are converted into the phase single difference residuals of each of the satellites through a zero mean assumption method.

4. The overlapping frequency multipath error correction model establishment method according to claim 1, characterized in that: The method of establishing a multi-system fusion multi-path hemispherical graph model based on the phase single-difference residual and the DBSCAN clustering algorithm includes: Dividing the sky map into a plurality of grid cells, and allocating the phase single difference residual of each satellite at different observation times to a corresponding grid cell according to the position information of each satellite in the sky at different observation times; A multi-system fusion multi-path hemispherical graph model is established based on the phase single-difference residuals of each grid unit and the DBSCAN clustering algorithm.

5. The overlapping frequency multipath error correction model establishment method according to claim 4, characterized in that: The sky map is divided into a plurality of grid cells, including: Divide the sky map into grid cells according to a preset resolution.

6. The overlapping frequency multipath error correction model establishment method according to claim 4, characterized in that: The method further comprises: Before establishing the multi-system fusion multi-path hemispherical graph model, calculating the standard deviation of the phase single difference residual of each grid unit; Determining the phase single-difference residual anomaly of each of the grid cells according to the standard deviation of each of the grid cells; Delete the phase single-difference residual anomaly.

7. The overlapping frequency multipath error correction model establishment method according to claim 4, characterized in that: The method of establishing a multi-system fusion multi-path hemispherical graph model based on the phase single-difference residual of each grid unit and the DBSCAN clustering algorithm includes: Clustering the phase single difference residuals of each grid cell according to satellite information corresponding to the phase single difference residuals to obtain cluster clusters; wherein the satellite information includes the satellite's altitude angle, azimuth angle, altitude change rate, and signal-to-noise ratio; An average value of the phase single difference residuals of each cluster is used as the multipath error correction value of the cluster.

8. The overlapping frequency multipath error correction model establishment method according to claim 1, characterized in that: The different satellite systems include the GPS satellite system, the BDS satellite system and the Galileo satellite system.

9. A phase observation value correction method, characterized in that: include: Obtain the satellite carrier phase observation value to be corrected; determining a multipath error correction value according to a multipath error correction model; The multipath error correction model is a multipath error correction model established based on the method according to any one of claims 1 to 8; The satellite carrier phase observation value to be corrected is corrected according to the multipath error correction value.

10. An overlapping frequency multipath error correction model establishment device, characterized in that: include: an observation data acquisition module, configured to acquire carrier phase observation data of a plurality of satellites at overlapping frequencies; wherein the plurality of satellites belong to a plurality of different satellite systems, and the plurality of satellites include a reference satellite and at least three other common-view satellites; a double-difference residual determination module, configured to determine, based on the carrier phase observation data, a phase double-difference residual between each of the other common-view satellites and the reference satellite at different observation times; a single-difference residual determination module, configured to convert the phase double-difference residuals between each of the other common-view satellites and the reference satellite into the phase single-difference residuals of each of the satellites; The model building module is used to establish a multi-system fusion multi-path hemispherical graph model based on the phase single difference residual and the DBSCAN clustering algorithm.

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