Baseline Ambiguity Verification Method, Device, Equipment and Storage Medium

Abstract

The present application discloses a method, device, equipment and storage medium for baseline ambiguity verification, including: obtaining GNSS observation data, satellite broadcast ephemeris and observation value domain space correction parameters collected by ground observation stations in a target area; performing baseline solution according to the GNSS observation data, satellite broadcast ephemeris and observation value domain space correction parameters to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch; forming N closed loops with M target baselines in the baseline of the ground observation station that meet preset conditions; based on the integer linear programming method and target constraint conditions, performing overall verification on the double-difference ambiguities of the N closed loops, and solving the minimum value of the objective function based on the simplex method and the branch and bound method to obtain the verification information of each double-difference ambiguity, thereby solving the problems in the related art that the accuracy of the solved double-difference ambiguity is low, the solution efficiency is low, and the requirements of high-precision and fast positioning cannot be met.

Description

Technical Field

[0001] This application belongs to the field of positioning technology, and particularly relates to a method, device, equipment and storage medium for baseline ambiguity verification. Background Art

[0002] With the rapid development of satellite positioning technology, people's demand for fast and high-precision position information is also increasing. In order to achieve high-precision fast positioning, it is necessary to evaluate the double-difference ambiguity to obtain a reliable double-difference ambiguity.

[0003] Currently, in the process of evaluating the double-difference ambiguity in related technologies, it is necessary to determine whether the pass rate of the closed loop involved in each baseline meets the threshold, and then perform multiple iterations on the baselines that meet the threshold to solve the double-difference ambiguity, so as to ensure that the pass rate of all baseline closed loops corresponding to each satellite meets the empirical value. However, the thresholds in related technologies are all determined by empirical values, resulting in low accuracy of the solved double-difference ambiguity, and the solution efficiency of the double-difference ambiguity is low by using the iterative method, which cannot meet the requirements of fast high-precision positioning. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide a method, device, equipment and storage medium for baseline ambiguity verification, which can solve the problems that the thresholds in related technologies are all determined by empirical values, resulting in low accuracy of the solved double-difference ambiguity, and the solution efficiency of the double-difference ambiguity is low by using the iterative method, which cannot meet the requirements of high-precision positioning and fast positioning.

[0005] In a first aspect, the embodiments of this application provide a method for baseline ambiguity verification, and the method includes:

[0006] Obtain GNSS observation data, satellite broadcast ephemeris and observation value domain space correction parameters collected by ground observation stations in the target area;

[0007] Perform baseline solution according to the GNSS observation data, satellite broadcast ephemeris and observation value domain space correction parameters to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch;

[0008] Form N closed loops with M target baselines that meet the preset conditions in the baselines of the ground observation station, where the preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers;

[0009] Based on the integer linear programming method and the target constraint conditions, the double-difference ambiguities of N closed loops are globally verified, and the minimum value of the objective function is solved based on the simplex method and the branch and bound method to obtain the verification information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the correction numbers corresponding to the target baseline in any closed loop is 0, and the objective function is the sum of the absolute values of all correction numbers.

[0010] In one implementable manner of the first aspect, the method further includes:

[0011] Process the double-difference ambiguities according to the correction numbers corresponding to the double-difference ambiguities in the verification information to obtain the target double-difference ambiguities.

[0012] In one implementable manner of the first aspect, processing the double-difference ambiguities according to the correction numbers corresponding to the double-difference ambiguities in the verification information to obtain the target double-difference ambiguities specifically includes:

[0013] Eliminate the double-difference ambiguities with non-zero correction numbers to obtain the double-difference ambiguities with zero correction numbers;

[0014] Or,

[0015] Correct the first double-difference ambiguity according to the correction number of the first double-difference ambiguity to obtain the target double-difference ambiguity. The target double-difference ambiguity includes the corrected first double-difference ambiguity and the double-difference ambiguity with a zero correction number. The first double-difference ambiguity is the double-difference ambiguity with a non-zero correction number.

[0016] In one implementable manner of the first aspect, after processing the double-difference ambiguities according to the correction numbers corresponding to the double-difference ambiguities in the verification information, the method further includes:

[0017] Calculate the non-differential atmospheric delay value of each ground-based augmentation station in the target area according to the non-differential ambiguity result of each ground-based augmentation station in the target area and the target double-difference ambiguity;

[0018] Perform atmospheric modeling on the target area according to the non-differential atmospheric delay values to obtain the atmospheric model parameters in the target area;

[0019] Broadcast the atmospheric model parameters for the terminal to perform positioning according to the atmospheric model parameters.

[0020] In one implementable manner of the first aspect, calculating the non-differential atmospheric delay value of each ground-based augmentation station in the target area according to the non-differential ambiguity result of each ground-based augmentation station in the target area and the target double-difference ambiguity includes:

[0021] Determine a reference benchmark according to the target double-difference ambiguity in the target area and the non-differential ambiguity results of each ground-based augmentation station;

[0022] Restore the undifferenced ambiguity true value of each ground-based augmentation station according to the reference benchmark;

[0023] Under the fixed solution condition of the satellite, calculate the undifferenced atmospheric delay value of each ground-based augmentation station in the target area according to the undifferenced ambiguity true value.

[0024] In an implementable manner of the first aspect, perform baseline solution according to GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch, specifically including:

[0025] Determine the pseudorange observation and phase observation according to GNSS observation data and observation value domain space correction parameters;

[0026] Perform inter-station difference and inter-satellite difference on the pseudorange observation and phase observation respectively to obtain the double-difference observation equation;

[0027] Eliminate the difference of the ionospheric effect and the difference of the tropospheric effect of two satellites relative to two stations in the double-difference observation equation according to the observation value domain space correction parameters to obtain the double-difference ambiguity.

[0028] In the second aspect, an embodiment of the present application provides a baseline ambiguity verification device, including:

[0029] An acquisition module, configured to acquire GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by a ground observation station in the target area;

[0030] A solution module, configured to perform baseline solution according to GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch;

[0031] A composition module, configured to form N closed loops from M target baselines in the baseline of the ground observation station that meet the preset conditions. The preset condition is that the angle between adjacent target baselines is greater than the preset angle threshold, and both M and N are positive integers;

[0032] A verification module, configured to perform overall verification on the double-difference ambiguity of the N closed loops based on the integer linear programming method and the target constraint condition, and solve the minimum value of the objective function based on the simplex method and the branch and bound method to obtain the verification information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop is 0, and the objective function is the sum of the absolute values of all corrections.

[0033] In an implementable manner of the second aspect, the device further includes:

[0034] A processing module, configured to process the double-difference ambiguity according to the correction number corresponding to the double-difference ambiguity in the verification information, so as to obtain a target double-difference ambiguity.

[0035] In an implementable manner of the second aspect, the processing module is specifically configured to eliminate the double-difference ambiguities with non-zero correction numbers to obtain double-difference ambiguities with zero correction numbers; or correct the first double-difference ambiguity according to the correction number of the first double-difference ambiguity to obtain a target double-difference ambiguity, where the target double-difference ambiguity includes the corrected first double-difference ambiguity and the double-difference ambiguity with a zero correction number, and the first double-difference ambiguity is the double-difference ambiguity with a non-zero correction number.

[0036] In an implementable manner of the second aspect, the device further includes:

[0037] A calculation module, configured to calculate the non-differential atmospheric delay value of each ground-based augmentation station in the target area according to the non-differential ambiguity result of each ground-based augmentation station in the target area and the target double-difference ambiguity;

[0038] A modeling module, configured to perform atmospheric modeling on the target area according to the non-differential atmospheric delay value to obtain atmospheric model parameters in the target area;

[0039] A broadcast module, configured to broadcast the atmospheric model parameters for the terminal to perform positioning according to the atmospheric model parameters.

[0040] In an implementable manner of the second aspect, the calculation module is specifically configured to determine a reference benchmark according to the target double-difference ambiguity in the target area and the non-differential ambiguity result of each ground-based augmentation station; restore the true value of the non-differential ambiguity of each ground-based augmentation station according to the reference benchmark; and calculate the non-differential atmospheric delay value of each ground-based augmentation station in the target area under the fixed solution condition of the satellite according to the true value of the non-differential ambiguity.

[0041] In an implementable manner of the second aspect, the solution module is specifically configured to determine the pseudorange observation and the phase observation according to the GNSS observation data and the observation value domain space correction parameters; perform inter-station difference and inter-satellite difference on the pseudorange observation and the phase observation respectively to obtain double-difference observation equations; and eliminate the difference of the ionospheric effect and the difference of the tropospheric effect of two satellites relative to two stations in the double-difference observation equations according to the observation value domain space correction parameters to obtain double-difference ambiguities.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method in the first aspect are implemented.

[0043] Fourthly, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method according to the first aspect are implemented.

[0044] In the embodiment of the present application, according to the ground-based augmentation site, GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by ground observation stations in the target area can be obtained. According to the GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters, baseline solution is performed to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch; M target baselines that meet the preset conditions in the baseline of the ground observation station are formed into N closed loops, and the preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers; based on the integer linear programming method and target constraint conditions, the double-difference ambiguities of the N closed loops are overall checked, and based on the simplex method and the branch and bound method, the minimum value of the objective function is solved to obtain the check information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop is 0, and the objective function is the sum of the absolute values of all corrections. In this way, the accuracy of solving the ambiguity is high, and the wrong double-difference ambiguity can be quickly located. And by using the integer linear programming method to check the closed loop, during the process of solving the double-difference ambiguity, the solution efficiency can increase linearly with the increase of the baseline and the closed loop, avoiding solving the double-difference ambiguity by an iterative method, improving the solution efficiency, and meeting the requirements of high precision and fast positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic flowchart of a method for checking baseline ambiguity provided by an embodiment of the present application;

[0046] Figure 2 is a schematic structural diagram of a device for checking baseline ambiguity provided by an embodiment of the present application;

[0047] Figure 3 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The features and exemplary embodiments of each aspect of the present application will be described in detail below. For the purpose of making the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present application by showing examples of the present application.

[0049] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0050] With the rapid development of satellite positioning technology, people's demand for fast and high-precision position information is also increasing. To achieve high-precision fast positioning, it is necessary to evaluate the double-difference ambiguity to obtain a reliable double-difference ambiguity.

[0051] Currently, in the process of evaluating the double-difference ambiguity in related technologies, it is necessary to judge whether the pass rate of the closed-loop involved in each baseline meets the threshold, and then perform multiple iterations on the baselines that meet the threshold to solve the double-difference ambiguity, so as to ensure that the pass rate of all baseline closed-loops corresponding to each satellite meets the empirical value. However, the thresholds in related technologies are all determined by empirical values, resulting in low accuracy of the solved double-difference ambiguity, and the solution efficiency of the double-difference ambiguity is low by using the iterative method, which cannot meet the requirements of fast high-precision positioning.

[0052] To solve the technical problems in related technologies, the embodiments of the present application provide a method, device, equipment and computer storage medium for baseline ambiguity verification. First, the method for baseline ambiguity verification provided by the embodiments of the present application will be introduced below.

[0053] Figure 1 It is a schematic flowchart of a baseline ambiguity verification method 100 provided by the embodiments of the present application.

[0054] As Figure 1 shown, the baseline ambiguity verification method 100 provided by the embodiments of the present application may include S101 to S104.

[0055] S101: Obtain GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by ground observation stations in the target area.

[0056] The ground observation station can collect GNSS observation data in real time. Among them, the GNSS observation data may include pseudorange, carrier phase, clock error, ionosphere, troposphere effect, carrier wavelength, integer ambiguity, etc. Based on the satellite broadcast ephemeris, the distance between the satellite centroid and the observation station at the signal emission time can be calculated. The target area can be any area on the ground. There are at least three ground observation stations in the target area, and the observation data collected by the ground observation stations in real time can be obtained, and the satellite broadcast ephemeris and the observation value domain space correction parameters can be obtained.

[0057] In one embodiment, the observation value domain space correction parameters can be received through the satellite-based link, and the observation value domain space correction parameters can also be received through the network link.

[0058] S102: According to the GNSS observation data, the satellite broadcast ephemeris, and the observation value domain space correction parameters, perform baseline solution to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch.

[0059] There are ground-based augmentation sites evenly distributed in the target area. Baseline solution can be performed according to the GNSS observation data, the satellite broadcast ephemeris, and the observation value domain space correction parameters, so as to obtain the double-difference ambiguity of the baseline at each epoch.

[0060] Specifically, under the condition that the tidal effect and the relativistic effect have been corrected before taking the difference, S102 can specifically obtain the double-difference ambiguity through the following steps: First, determine the pseudorange observation and the phase observation according to the GNSS observation data and the observation value domain space correction parameters; then, perform inter-station difference and inter-satellite difference on the pseudorange observation and the phase observation respectively to obtain the double-difference observation equation; finally, eliminate the difference of the ionosphere effect and the difference of the troposphere effect of two satellites relative to two stations in the double-difference observation equation according to the observation value domain space correction parameters to obtain the double-difference ambiguity.

[0061] Among them, under the condition that the tidal effect and the relativistic effect have been corrected before taking the difference, the pseudorange observation satisfies the following formula (1), and the phase observation satisfies the following formula (2):

[0062]

[0063]

[0064] In formula (1) and formula (2), f represents the frequency, j = 1, 2, 5 are the subscripts of the frequency f; i is the number of the ground observation station; k is the satellite identification number; R represents the pseudorange; Φ represents the carrier phase; ρ represents the distance between the satellite centroid and the ground observation station at the signal emission time; c represents the speed of light; δt iDenote the receiver clock offset of the ground observation station; δt k and the receiver clock offset of the satellite; δ ion Denote the ionospheric effect; δ trop Denote the tropospheric effect; λ denotes the carrier wavelength; Denote the integer ambiguity with respect to the ground observation station i and the satellite k; ε c and ε p Both denote residuals.

[0065] The pseudorange observation at frequency j The double-difference observation equation after performing the difference between satellite k1 and satellite k2 and performing the difference between ground observation station i1 and ground observation station i2 Satisfies the following formula (3):

[0066]

[0067] For the phase observation at frequency j The double-difference observation equation after performing the difference between satellite k1 and satellite k2 and performing the difference between ground observation station i1 and ground observation station i2 Satisfies the following formula (4):

[0068]

[0069] In formula (3) and formula (4), Denotes the distance between the centroid of satellite k and ground observation station i, λ j Denotes the carrier wavelength at frequency j, Denotes the integer ambiguity of ground observation station i and satellite k at frequency j, ddδ ion (j) Denotes the difference in the ionospheric effect of two satellites with respect to two ground observation stations at frequency j, ddδ trop Denotes the difference in the tropospheric effect of two satellites with respect to two ground observation stations, ddε p Denotes the pseudorange observation of two satellites with respect to two ground observation stations of the residual, ddε c Denotes the phase observation of two satellites with respect to two ground observation stations of the residual.

[0070] It is determined that the difference ddδ of the ionospheric effect in the double-difference observation equation can be eliminated by using the carrier phase difference (Real-Time Kinematic, RTK) algorithm and the observation value domain space correction ion (j) and the difference ddδ of the tropospheric effect trop .

[0071] In some embodiments, the baseline can be determined by the distances between ground observation stations within the target area. Specifically, ground observation stations with distances between them less than a preset distance threshold can be grouped to form a baseline.

[0072] S103: Form N closed loops with M target baselines that meet the preset conditions in the baseline of the ground observation stations. The preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers.

[0073] The target baseline can be determined based on the angle between baselines, where the angle between adjacent target baselines is greater than the preset angle threshold, that is, the angle between adjacent target baselines is an acute angle. Here, when there is no error in the double-difference ambiguity of the baseline, the sum of the baseline vectors of the formed closed loop is 0.

[0074] S104: Based on the integer linear programming method and the target constraint conditions, conduct an overall check of the double-difference ambiguities of the N closed loops, and solve the minimum value of the objective function based on the simplex method and the branch and bound method to obtain the check information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop is 0, and the objective function is the sum of the absolute values of all corrections.

[0075] The correction of the target baseline can be preset. Here, the preset correction can be a variable, and the optimal solution is obtained through certain calculations. Set the sum of the absolute values of the corrections as the objective function, and then use the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop being 0 as the target constraint condition. Use the integer linear programming algorithm to conduct an overall check of the double-difference ambiguities of the N closed loops, and solve the minimum value of the objective function based on the simplex method and the branch and bound method, thereby obtaining the check information of the double-difference ambiguity. Among them, the check information of the double-difference ambiguity includes the magnitude of the correction of the target baseline. If there is an error in the double-difference ambiguity of the target baseline, the correction of the target baseline is not 0. If there is no error in the double-difference ambiguity of the target baseline, the correction of the target baseline is 0.

[0076] The following details the optimal solution of the objective function when the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop is 0.

[0077] Suppose there are m target baselines, then the set M of the target baselines = {1, 2, …, m}, and there are n triangular closed loops formed, then the closed loop set L = {l1, l2, …, l n}.

[0078] For all double-difference ambiguities in the area, when all baselines with errors in the double-difference ambiguities are correctly marked, the sum of the absolute values of the corrections is the smallest, and the closed loop check problem is transformed into an integer linear programming problem, which satisfies the following formula (5):

[0079]

[0080] The constraint equation of formula (5) satisfies the following formula (6):

[0081]

[0082] In formulas (5) and (6), y i represents the absolute value of the correction of the target baseline i, f(i) represents the double-difference ambiguity of the target baseline i in any closed loop, x i represents the correction of the target baseline i, and x i = x i + - x i - .

[0083] Converting the equation in formula (6) gives the following formula (7):

[0084]

[0085] As can be seen from the above formula (7), when ∑ i∈l f(i) is not 0, that is, there is an error in the observed value of a certain target baseline in the closed loop, there is no case where the right side of the inequality is less than 0, and the standard form of linear programming is not applicable. If it is required that the constants on the right side of the inequality are all positive, an auxiliary linear programming L aux needs to be constructed, where the auxiliary linear programming L aux satisfies the following formula (8), and the constraint equation of the auxiliary linear programming L aux satisfies the following formula (9):

[0086] min = x0 (8)

[0087]

[0088] According to the above formulas (8) and (9), if the optimal solution of L aux is 0, then the linear equations corresponding to formulas (5) and (6) have solutions.

[0089] Here, L aux can be written in the slack form as follows:

[0090] min = x0 (10)

[0091]

[0092] where k i , o i , p i , qi represents the basic variable, represents the non - basic variable. Select the row with the smallest right - hand side of the equation in formula (11) (for example, the e - th row, and the corresponding basic variable is k e ), and let At this time, \(x_0\) becomes a basic variable (entering the basis), and k e becomes a non - basic variable (leaving the basis). Eliminate \(x_0\) from other constraint equations, then all constant variables become positive (this process is called "pivoting"). Eliminate \(x_0\) from the objective function, then formula (10) becomes the following formula (12):

[0093]

[0094] Arrange the above - mentioned constraint equations (formula (11)) into the standard form of linear programming:

[0095]

[0096] where \(r = 2m + 2n\), \(t = 5m+2n + 1\)

[0097] Writing the above formula (12) and formula (13) in matrix form can be expressed as:

[0098] min = CX (15)

[0099]

[0100] where \(X\) represents the solution of the function, \(A\) represents the coefficient matrix of the constraint conditions, and \(A\) is row - full rank, that is, \(R(A)=r\); \(b\) represents the matrix of the constraint conditions; \(C\) represents the coefficient matrix of the objective function.

[0101] Using the simplex method to solve the above matrix (formula (15) and formula (16)), if \(x_0 = 0\) and the objective function takes the minimum value, then L aux has an optimal solution, indicating that the objective function has a solution. Using the L aux constraint equation to replace the constraint equation in the objective function, and using the basic variables in the L aux constraint equation to eliminate the variables in the original objective function, the new standard form of linear programming formed is:

[0102] min = C'X' (17)

[0103]

[0104] where \(C'\) represents the coefficient matrix of the objective function; \(A'\) represents the coefficient matrix of the constraint conditions after deleting the column where \(x_0\) is located in the optimal solution of the auxiliary linear programming, and \(b'\) represents the matrix of the constraint conditions in the optimal solution of the auxiliary linear programming.

[0105] Based on the above standard form of linear programming and using the simplex method in combination with the branch and bound method, the checking information for closed-loop checking can be obtained.

[0106] In some embodiments, in order to be able to determine accurate atmospheric parameters and thus improve the positioning accuracy, after S104, the following steps may further be included: processing the double-difference ambiguity according to the correction number corresponding to the double-difference ambiguity in the checking information to obtain the target double-difference ambiguity

[0107] Here, the target double-difference ambiguities are all double-difference ambiguities without errors. Specifically, the double-difference ambiguities with non-zero correction numbers can be excluded to obtain the double-difference ambiguities with zero correction numbers, that is, the double-difference ambiguities with zero correction numbers are the target double-difference ambiguities. It is also possible to correct the first double-difference ambiguity according to the correction number of the first double-difference ambiguity, where the first double-difference ambiguity is the double-difference ambiguity with a non-zero correction number. After correcting the first double-difference ambiguity, the target double-difference ambiguity can be obtained, that is, the double-difference ambiguity with zero correction number and the corrected first double-difference ambiguity.

[0108] Here, when the correction number of the double-difference ambiguity of the target baseline is non-zero, let the double-difference ambiguity of the target baseline be f(a) and the correction number of the double-difference ambiguity be x a , then the target baseline can be corrected by f(a) + x a To correct the target baseline.

[0109] Thus, by excluding the double-difference ambiguities with non-zero correction numbers or correcting the double-difference ambiguities with non-zero correction numbers, it can be ensured that the double-difference ambiguities are all without errors, and by correcting the double-difference ambiguities with non-zero correction numbers, more double-difference ambiguities can be retained, so that accurate atmospheric parameters can be determined based on the double-difference ambiguities.

[0110] In some embodiments, in order to establish a high-precision regional atmospheric model, and thus determine accurate atmospheric parameters and improve the positioning accuracy, first, the non-differential atmospheric delay value of each ground-based augmentation station in the target area can be calculated according to the non-differential ambiguity result and the target double-difference ambiguity of each ground-based augmentation station in the target area. Then, based on the non-differential atmospheric delay value, atmospheric modeling of the target area is performed to obtain the atmospheric model parameters in the target area. Specifically, a reference benchmark can be determined according to the target double-difference ambiguity in the target area and the non-differential ambiguity results of each ground-based augmentation station. According to this reference benchmark, the true value of the non-differential ambiguity of each ground-based augmentation station can be restored. Then, under the fixed solution condition of the satellite, the non-differential atmospheric delay value of each ground-based augmentation station in the target area can be calculated according to the true value of the non-differential ambiguity. Since the target double-difference ambiguities are all ambiguities without errors, accurate non-differential atmospheric delay values can be determined, and thus a high-precision regional atmospheric model can be obtained.

[0111] Broadcast atmospheric model parameters for the terminal to perform positioning based on the atmospheric model parameters. Since the atmospheric model is determined based on ambiguity without error, the accuracy of the atmospheric model parameters is improved. Furthermore, when the terminal uses the atmospheric model parameters for positioning, its positioning accuracy is also improved.

[0112] In the embodiment of the present application, based on the ground-based augmentation station, GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by ground observation stations in the target area can be obtained. According to the GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters, baseline solution is performed to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch; M target baselines that meet the preset conditions in the baseline of the ground observation station are formed into N closed loops, and the preset condition is that the angle between adjacent target baselines is greater than the preset angle threshold, and both M and N are positive integers; based on the integer linear programming method and the target constraint condition, the double-difference ambiguities of the N closed loops are overall checked, and based on the simplex method and the branch and bound method, the minimum value of the objective function is solved to obtain the check information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop is 0, and the objective function is the sum of the absolute values of all corrections. In this way, the accuracy of solving the ambiguity is high, and the wrong double-difference ambiguity can be quickly located. And by using the integer linear programming method to check the closed loop, during the process of solving the double-difference ambiguity, the solution efficiency can increase linearly with the increase of the baseline and the closed loop, avoiding solving the double-difference ambiguity by iteration, improving the solution efficiency, and meeting the requirements of high precision and fast positioning.

[0113] Based on the baseline ambiguity checking method provided by the present application, correspondingly, the present application provides a baseline ambiguity checking device in an embodiment. Next, in the embodiment of the present application, taking the baseline ambiguity checking device executing the baseline ambiguity checking method as an example, the baseline ambiguity checking device provided by the embodiment of the present application is described.

[0114] Figure 2 It is a schematic structural diagram of a baseline ambiguity checking device 200 provided by the present application.

[0115] As Figure 2 shown, the baseline ambiguity checking device 200 provided by the present application may include: an acquisition module 201, a solution module 202, a composition module 203, and a checking module 204.

[0116] The acquisition module 201 is used to acquire GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by ground observation stations in the target area;

[0117] A solution module 202, configured to perform baseline solution according to GNSS observation data, satellite broadcast ephemeris, and observed value domain space correction parameters, so as to obtain double-difference ambiguities of the baseline corresponding to a ground observation station at each epoch.

[0118] A composition module 203, configured to form N closed loops with M target baselines that meet preset conditions in the baseline of the ground observation station, where the preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers.

[0119] An inspection module 204, configured to perform overall inspection on the double-difference ambiguities of the N closed loops based on the integer linear programming method and target constraint conditions, and solve the minimum value of the objective function based on the simplex method and the branch and bound method to obtain the inspection information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any closed loop is 0, and the objective function is the sum of the absolute values of all corrections.

[0120] In some embodiments of the present application, the device 200 further includes: a processing module, configured to process the double-difference ambiguities according to the corrections corresponding to the double-difference ambiguities in the inspection information to obtain target double-difference ambiguities.

[0121] In some embodiments of the present application, the processing module is specifically configured to eliminate the double-difference ambiguities with non-zero corrections to obtain double-difference ambiguities with zero corrections; or correct the first double-difference ambiguity according to the correction of the first double-difference ambiguity to obtain the target double-difference ambiguity, where the target double-difference ambiguity includes the corrected first double-difference ambiguity and the double-difference ambiguity with zero correction, and the first double-difference ambiguity is the double-difference ambiguity with non-zero correction.

[0122] In some embodiments of the present application, the device 200 further includes:

[0123] A calculation module, configured to calculate the non-differential atmospheric delay value of each ground-based augmentation station in the target area according to the non-differential ambiguity result of each ground-based augmentation station in the target area and the target double-difference ambiguity.

[0124] A modeling module, configured to perform atmospheric modeling on the target area according to the non-differential atmospheric delay value to obtain atmospheric model parameters in the target area.

[0125] A broadcast module, configured to broadcast the atmospheric model parameters for the terminal to perform positioning according to the atmospheric model parameters.

[0126] In some embodiments of the present application, the calculation module is specifically configured to determine a reference benchmark according to the target double-difference ambiguity in the target area and the non-difference ambiguity results of each ground-based augmentation station; restore the true value of the non-difference ambiguity of each ground-based augmentation station according to the reference benchmark; and calculate the non-difference atmospheric delay value of each ground-based augmentation station in the target area under the fixed solution condition of the satellite.

[0127] In some embodiments of the present application, the solution module 202 is specifically configured to determine pseudorange observables and phase observables according to GNSS observation data and observation value domain space correction parameters; perform inter-station differencing and inter-satellite differencing on the pseudorange observables and phase observables respectively to obtain double-difference observation equations; and eliminate the differences in ionospheric effects and tropospheric effects of two satellites relative to two stations in the double-difference observation equations according to the observation value domain space correction parameters to obtain double-difference ambiguities.

[0128] Figure 2 Each module / unit in the shown device 200 has the function of implementing Figure 1 each step therein and can achieve its corresponding technical effects. For the sake of brevity, it will not be described in detail here.

[0129] In the embodiments of the present application, according to the ground-based augmentation stations, GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by ground observation stations in the target area can be obtained. According to the GNSS observation data, the satellite broadcast ephemeris, and the observation value domain space correction parameters, baseline solution is performed to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch; M target baselines that meet the preset conditions in the baseline of the ground observation station are formed into N closed loops, and the preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers; based on the integer linear programming method and target constraint conditions, the double-difference ambiguities of the N closed loops are globally checked, and the minimum value of the objective function is solved based on the simplex method and the branch and bound method to obtain the check information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any one of the closed loops is 0, and the objective function is the sum of the absolute values of all the corrections. In this way, the accuracy of solving the ambiguity is high, and the incorrect double-difference ambiguity can be quickly located. Moreover, the closed loops are checked by using the integer linear programming method. Then, in the process of solving the double-difference ambiguity, the solution efficiency can increase linearly with the increase of the baseline and the closed loop, avoiding solving the double-difference ambiguity by an iterative method, improving the solution efficiency, and meeting the requirements of high precision and fast positioning.

[0130] Figure 3 The hardware structure diagram of the electronic device provided by the embodiments of the present application is shown.

[0131] As shown Figure 3 in the figure, the electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0132] Specifically, the above-mentioned processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present application.

[0133] The memory 302 may include a mass storage for data or instructions. By way of example and not limitation, the memory 302 may include a Hard Disk Drive (HDD), a floppy disk drive, a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. In one example, the memory 302 may include removable or non-removable (or fixed) media, or the memory 302 is a non-volatile solid-state memory. The memory 302 may be inside or outside the integrated gateway disaster recovery device.

[0134] In one example, the memory 302 may be a Read Only Memory (ROM). In one example, the ROM may be a mask-programmed ROM, a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), an Electrically Rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0135] The memory 302 may include a read-only memory (ROM), a random access memory (RAM), a disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present disclosure.

[0136] The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement Figure 1 the method / steps in the embodiments shown in Figure 2 the figure, and achieves the corresponding technical effects achieved by the method / steps executed in the example shown in the figure. For the sake of brevity, the description is not repeated here.

[0137] In one example, the electronic device may further include a communication interface 303 and a bus 310. Among them, as Figure 3 shown, the processor 301, the memory 302, and the communication interface 303 are connected through the bus 310 and complete communication with each other.

[0138] The communication interface 303 is mainly used to implement communication between various modules, devices, units, and / or devices in the embodiments of the present application.

[0139] The bus 310 includes hardware, software, or both, and couples the components of the electronic device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses or a combination of two or more of these. In a suitable case, the bus 310 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0140] The electronic device may perform the baseline blur check method in the embodiments of the present application based on the currently intercepted spam messages and the messages reported by the user, so as to implement the combination of Figure 1 , Figure 2 the baseline blur check method and device described.

[0141] In addition, in combination with the baseline blur check method in the above embodiments, the embodiments of the present application may provide a computer storage medium to implement. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by the processor, any one of the baseline blur check methods in the above embodiments is implemented.

[0142] It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application.

[0143] The functional blocks shown in the above-described block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. A "machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0144] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0145] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus enable the implementation of the functions / operations specified in one or more blocks of the flowchart and / or block diagram. Such a processor may be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0146] As described above, the above is only the specific implementation manner of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present application, and these modifications or substitutions should all be covered within the protection scope of the present application.

Claims

1. A method for checking baseline ambiguity, characterized in that Including: Obtaining GNSS observation data, satellite broadcast ephemeris, and observation value domain space correction parameters collected by ground observation stations within a target area; Performing baseline solution based on the GNSS observation data, the satellite broadcast ephemeris, and the observation value domain space correction parameters to obtain double-difference ambiguities of the baseline corresponding to the ground observation stations at each epoch; Forming N closed loops with M target baselines in the baseline of the ground observation stations that meet a preset condition, where the preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers; Based on the integer linear programming method and target constraint conditions, overall checking the double-difference ambiguities of the N closed loops, and solving the minimum value of the objective function based on the simplex method and the branch and bound method to obtain the checking information of each double-difference ambiguity. The target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any one of the closed loops is 0, and the objective function is the sum of the absolute values of all the corrections.

2. The method according to claim 1, characterized in that, The method further includes: Processing the double-difference ambiguities according to the corrections corresponding to the double-difference ambiguities in the checking information to obtain target double-difference ambiguities.

3. The method according to claim 2, wherein The processing the double-difference ambiguities according to the corrections corresponding to the double-difference ambiguities in the checking information to obtain target double-difference ambiguities specifically includes: Eliminating double-difference ambiguities with non-zero corrections to obtain double-difference ambiguities with zero corrections; Or, Correcting the first double-difference ambiguity according to the correction of the first double-difference ambiguity to obtain the target double-difference ambiguity. The target double-difference ambiguity includes the corrected first double-difference ambiguity and double-difference ambiguities with zero corrections. The first double-difference ambiguity is a double-difference ambiguity with non-zero corrections.

4. The method according to claim 3, characterized in that, After processing the double-difference ambiguities according to the corrections corresponding to the double-difference ambiguities in the checking information, the method further includes: Calculating the non-differential atmospheric delay values of each ground-based augmentation station in the target area according to the non-differential ambiguity results of each ground-based augmentation station in the target area and the target double-difference ambiguities; Performing atmospheric modeling on the target area according to the non-differential atmospheric delay values to obtain atmospheric model parameters in the target area; Broadcasting the atmospheric model parameters for a terminal to perform positioning according to the atmospheric model parameters.

5. The method according to claim 4, wherein The calculating the non-differential atmospheric delay values of each ground-based augmentation station in the target area according to the non-differential ambiguity results of each ground-based augmentation station in the target area and the target double-difference ambiguities includes: Determining a reference benchmark according to the target double-difference ambiguities in the target area and the non-differential ambiguity results of each ground-based augmentation station; Restoring the true value of the non-differential ambiguity of each ground-based augmentation station according to the reference benchmark; Calculating the non-differential atmospheric delay values of each ground-based augmentation station in the target area according to the true value of the non-differential ambiguity under the fixed solution condition of the satellite.

6. The method according to claim 1, wherein Performing baseline solution according to the GNSS observation data, the satellite broadcast ephemeris, and the observed value domain spatial correction parameters to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch, specifically including: Determining the pseudorange observation and the phase observation according to the GNSS observation data and the observed value domain spatial correction parameters; Performing inter-station difference and inter-satellite difference on the pseudorange observation and the phase observation respectively to obtain double-difference observation equations; Eliminating the difference of the ionospheric effect and the difference of the tropospheric effect of two satellites relative to two stations in the double-difference observation equations according to the observed value domain spatial correction parameters to obtain the double-difference ambiguity.

7. A baseline ambiguity verification device, characterized in that Including: An acquisition module, configured to acquire GNSS observation data, satellite broadcast ephemeris, and observed value domain spatial correction parameters collected by a ground observation station in a target area; A solution module, configured to perform baseline solution according to the GNSS observation data, the satellite broadcast ephemeris, and the observed value domain spatial correction parameters to obtain the double-difference ambiguity of the baseline corresponding to the ground observation station at each epoch; A composition module, configured to form N closed loops from M target baselines that meet preset conditions in the baseline of the ground observation station, where the preset condition is that the angle between adjacent target baselines is greater than a preset angle threshold, and both M and N are positive integers; An inspection module, configured to perform overall inspection on the double-difference ambiguities of the N closed loops based on the integer linear programming method and target constraint conditions, and solve the minimum value of the objective function based on the simplex method and the branch and bound method to obtain the inspection information of each double-difference ambiguity, where the target constraint condition is that the sum of the absolute values of the corrections corresponding to the target baselines in any one of the closed loops is 0, and the objective function is the sum of the absolute values of all the corrections.

8. The device according to claim 7, characterized in that, The device further includes: A processing module, configured to process the double-difference ambiguity according to the correction corresponding to the double-difference ambiguity in the inspection information to obtain a target double-difference ambiguity.

9. An electronic device, characterized in that, The device includes: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the baseline ambiguity inspection method according to any one of claims 1-6.

10. A computer storage medium, characterized in that, Computer program instructions are stored on the computer storage medium, and when the computer program instructions are executed by the processor, the baseline ambiguity inspection method according to any one of claims 1-6 is implemented.

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