Single-difference-based gross error elimination method and device, electronic equipment and storage medium

Abstract

The invention provides a single-difference-based gross error elimination method and device, electronic equipment and a storage medium, and is applied to the technical field of navigation positioning. Original measurement data of a mobile station and original measurement data of a base station are obtained respectively; establishing a single-difference observation equation set by utilizing the original measurement data, solving the single-difference observation equation set by adopting a least square method to obtain a floating point solution of unknown parameters, further resolving the floating point solution to obtain a resolving result, and constructing a statistical test sequence based on the result for subsequent gross error detection; a statistical test amount is calculated through the statistical test sequence, and the test amount is compared with a preset threshold value. And if the statistical inspection amount is greater than or equal to the threshold value, indicating that the observation data quality meets the requirement or the gross error is not significant, performing resolving by using a first single-difference observation equation corresponding to the inspection amount, and finally obtaining an accurate positioning result. According to the invention, on the basis of single-difference positioning, effective identification and processing of gross errors are realized, so that the reliability and precision of positioning are improved.

Description

Technical Field

[0001] The present application relates to the technical field of navigation and positioning, and particularly to a method, device, electronic device and storage medium for rejecting gross errors based on single difference. Background Art

[0002] Satellite-based navigation and positioning technology has become one of the main ways to obtain spatio-temporal position information in human social activities due to its advantages such as strong real-time performance, wide coverage, and high positioning accuracy, meeting the needs of the public and various application industries. The Global Navigation Satellite System (GNSS) actually generally refers to all satellite navigation systems including the GPS (Global Positioning System) of the United States, the GLONASS (Global Navigation Satellite System) of Russia, the Galileo (Galileo satellite navigation system) of the European Union, and the BeiDou Navigation Satellite System (BDS) of China. It can provide users with all-weather, real-time, and high-precision three-dimensional absolute position, speed, and time information. As a major infrastructure for national geospatial and informatization, at the present stage, the BeiDou Navigation Satellite System (hereinafter referred to as BDS) has become an important part in fields such as intelligent transportation, intelligent terminals, national territorial space planning, resource exploration, forest fire prevention, marine fishery, precision surveying and mapping, location services, and precision timing. At the same time, it also plays an important role in the research of atmosphere and earth sciences such as earthquakes, water vapor, landslides, and ionosphere.

[0003] The main factors affecting the global satellite navigation and positioning accuracy can be divided into three categories: errors at the satellite end, errors in the propagation path, and errors at the receiving end. And the error correction methods of BDS are generally three: using model correction methods such as tropospheric dry delay and ionospheric delay; adding errors into parameter estimation, such as tropospheric wet delay and ionospheric vertical delay; using observation value combinations or differences to eliminate errors, such as ionospheric delay.

[0004] Obviously, reducing measurement errors is one of the measures to improve the BDS positioning accuracy. Differential positioning is a widely used and effective method to reduce or even eliminate various measurement errors, but it cannot eliminate the situation where the quality of the original observation signal of the receiver is poor and the error of the original observation value is large when the receiver antenna is blocked between buildings, in the shade of trees, etc. Identifying the original observations with large observation errors in differential positioning can not only improve the differential positioning accuracy, but also reduce the consumption of memory and computing resources in the embedded system.

[0005] However, in the existing technology, there is a lack of a method to effectively identify and eliminate the original observation quantities with large observation errors in differential positioning, while ensuring simpler modeling and less resource consumption. Summary of the Invention

[0006] In view of the deficiencies of the above-mentioned existing technology, the present application provides a gross error elimination method, device, electronic device and storage medium based on single difference, which is applied to the field of navigation and positioning technology, and has the beneficial effects of effectively identifying and eliminating the original observation quantities with large observation errors in differential positioning, improving the accuracy of differential positioning, and reducing resource consumption.

[0007] In a first aspect, a gross error elimination method based on single difference, the method includes the steps of: S1: Obtain the original measurement values of the mobile station and the reference station respectively; S2: Form a single-difference observation equation set according to the original measurement values; S3: Perform least squares on the single-difference observation equation set to solve the floating-point solution of the unknowns; S4: Calculate the solution result for the floating-point solution of the unknowns, and construct a statistical test sequence according to the solution result; S5: Calculate a statistical test quantity according to the statistical test sequence, and compare the constructed statistical test quantity with a preset test threshold; S6: When the statistical test quantity is greater than or equal to the preset test threshold, obtain the first single-difference observation equation corresponding to the statistical test quantity, and calculate the first single-difference observation equation to obtain precise positioning.

[0008] A gross error elimination method based on single difference proposed by the present application aims to solve the problem of large errors in the original observation data in differential positioning to improve the positioning accuracy. The original measurement data of the mobile station and the reference station are obtained respectively; a single-difference observation equation set is established using these original measurement data. The single-difference technology can weaken the common errors in the satellite end and the propagation path; the least squares method is used to solve the single-difference observation equation set to obtain the floating-point solution of the unknowns, which is the preliminary calculation result; the floating-point solution of the unknowns is further calculated to obtain the calculation result, and a statistical test sequence is constructed based on this result for subsequent gross error detection; a statistical test quantity is calculated through the statistical test sequence, and this test quantity is compared with a preset threshold. If the statistical test quantity is greater than or equal to the threshold, it indicates that the quality of the observation data meets the requirements or the gross error is not significant, and then the first single-difference observation equation corresponding to this test quantity is used for calculation to finally obtain the precise positioning result. The entire process realizes the effective identification and processing of gross errors on the basis of single-difference positioning through statistical testing, thereby improving the reliability and accuracy of positioning.

[0009] Further, after step S6, it includes: S7: when the statistical test value is less than the preset test threshold, the second single-difference observation equation corresponding to the statistical test value is eliminated from the single-difference observation equation group to obtain a third single-difference observation equation; S8: Repeat the operations in step S3 to step S5 for the third single-difference observation equation until all the statistical test quantities corresponding to the third single-difference observation equation are greater than or equal to the preset test threshold, and then solve the third single-difference observation equation to obtain precise positioning.

[0010] The present application proposes a method for eliminating gross errors based on single difference. When it is judged that the statistical test amount is less than a preset test threshold, the second single difference observation equation corresponding to the statistical test amount is eliminated from the single difference observation equation group, thereby obtaining a new single difference observation equation group, i.e., the third single difference observation equation. Repeat the operations of steps S3 to S5 for this new third single difference observation equation, that is, re-solve the floating-point solution of the unknown number, obtain the solution result, and calculate and compare the statistical test amount. This repetitive process will continue until the statistical test amounts corresponding to all equations in the third single difference observation equation are greater than or equal to the preset test threshold, and then solve the current third single difference observation equation to obtain accurate positioning. This iterative elimination and re-solving mechanism makes the method more robust and have higher positioning accuracy when facing observation data containing gross errors.

[0011] Further, step S1 includes: S11: Construct GNSS original pseudorange observation model and original carrier phase observation model; S12: constructing observation models of the mobile station and the reference station respectively according to the GNSS original pseudorange observation model and the original carrier phase observation model; S13: using inter-station single difference to eliminate the error terms in the observation models of the mobile station and the reference station, thereby obtaining original measurement values ​​of the mobile station and the reference station.

[0012] This application proposes a method for eliminating gross errors based on single difference, which constructs the GNSS original pseudorange observation model and the original carrier phase observation model, which are the basic models of GNSS positioning, and provides a theoretical basis for the construction of subsequent observation models. Then, for the mobile station and the base station, based on the aforementioned GNSS original observation model, respective observation models are constructed, with the aim of describing the various influences on the signal during the propagation process. Finally, the observation models of the mobile station and the base station are processed by using the inter-station single difference technology, and the common error terms in the observation model of the mobile station are eliminated or weakened by using the known information of the base station, so as to obtain the relative original measurement values ​​between the mobile station and the base station. These original measurement values ​​have smaller errors and higher precision, which lays the foundation for the establishment of the subsequent single difference observation equation and the elimination of gross errors.

[0013] Further, step S2 includes: S21: Obtain the original measurement values of the mobile station and the reference station for multiple satellites; S22: Construct the original single-difference observation equation according to the multiple original measurement values; S23: Perform linearization processing on the original single-difference observation equation to obtain the multi-dimensional single-difference observation equation set.

[0014] A gross error rejection method based on single-difference proposed in this application can improve the reliability and accuracy of positioning by obtaining the original measurement values of the mobile station and the reference station for multiple satellites and using the observation data of multiple satellites. Perform linearization processing on the original single-difference observation equation to obtain the multi-dimensional single-difference observation equation. Linearization processing is a necessary step for least squares solution, which transforms the non-linear observation equation into a linear equation for subsequent calculation. Obtaining the multi-dimensional single-difference observation equation set means that the equation set can process the observation information from multiple satellites to form an equation set, so that multiple unknown parameters can be calculated to achieve more accurate positioning.

[0015] Further, step S4 includes: S41: Solve the floating-point solution of the unknowns to obtain the solution result, where the solution result at least includes: the geometric distance between the position of the mobile station and the satellite, the clock difference single-difference between the mobile station and the reference station, and the floating-point solution of the ambiguity between the reference station and the satellite; S42: Construct the statistical test sequence according to the geometric distance between the position of the mobile station and the satellite, the clock difference single-difference between the mobile station and the reference station, and the floating-point solution of the ambiguity between the reference station and the satellite.

[0016] Further, step S5 includes: S51: Obtain the median, median absolute deviation of the statistical test sequence, and the values to be tested in each single-difference observation equation; S52: Construct an equation for calculating the statistical test quantity according to the value to be tested, the median, and the median absolute deviation, and calculate the statistical test quantity.

[0017] Further, in step S3, The form of the floating-point solution of the unknowns is: , where is the design matrix of all unknowns in the single-difference observation equation; is the weight matrix composed of the weights of each single-difference observation equation; contains all the observation vectors in the single-difference observation equation, and T is the transpose matrix.

[0018] In a second aspect, a gross error rejection device based on single difference, the device comprising: An acquisition module: configured to acquire the original measurement values of the mobile station and the reference station respectively; A formation module: configured to form a single difference observation equation set according to the original measurement values; A first solution module: configured to perform least squares on the single difference observation equation set to solve the floating-point solution of the unknowns; A construction module: configured to solve the floating-point solution of the unknowns to obtain a solution result, and construct a statistical test sequence according to the solution result; A second solution module: configured to calculate a statistical test quantity according to the statistical test sequence, and compare the constructed statistical test quantity with a preset test threshold; A third solution module: configured to, when the statistical test quantity is greater than or equal to the preset test threshold, acquire a first single difference observation equation corresponding to the statistical test quantity, and solve the first single difference observation equation to obtain precise positioning.

[0019] In a third aspect, the present application provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect above are run.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the method provided in the first aspect above are run.

[0021] Beneficial effects: The gross error rejection method, device, electronic device and storage medium based on single difference proposed in the present application aim to solve the problem of large errors in the original observation data in differential positioning, so as to improve the positioning accuracy. The original measurement data of the mobile station and the reference station are acquired respectively; a single difference observation equation set is established using these original measurement data, and the single difference technology can weaken the common errors in the satellite end and the propagation path; the least squares method is used to solve the single difference observation equation set to obtain the floating-point solution of the unknowns, which is the preliminary solution result; the floating-point solution of the unknowns is further solved to obtain a solution result, and a statistical test sequence is constructed based on this result for subsequent gross error detection; a statistical test quantity is calculated through the statistical test sequence, and this test quantity is compared with a preset threshold. If the statistical test quantity is greater than or equal to the threshold, it indicates that the quality of the observation data meets the requirements or the gross error is not significant, then the first single difference observation equation corresponding to this test quantity is used for solution, and finally a precise positioning result is obtained. The entire process realizes the effective identification and processing of gross errors on the basis of single difference positioning through statistical tests, thereby improving the reliability and accuracy of positioning. Description of the Drawings

[0022] Figure 1 Schematic flowchart of a gross error rejection method based on single difference proposed in this application.

[0023] Figure 2 Schematic structural diagram of a gross error rejection device based on single difference proposed in this application.

[0024] Figure 3 Schematic structural diagram of the electronic device provided in this application.

[0025] Figure 4 Positioning result obtained by resolving single difference observables using the prior art.

[0026] Figure 5 Positioning result obtained by resolving after using a gross error rejection method based on single difference proposed in this application.

[0027] Reference numeral description: 201, acquisition module; 202, construction module; 203, first calculation module; 204, construction module; 205, second calculation module; 206, third calculation module; 3, electronic device; 301, processor; 302, memory; 303, communication bus. Detailed implementation manners

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and marked in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0029] It should be noted that: Similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first", "second", etc. are only used for distinguishing descriptions, and cannot be understood as indicating or implying relative importance.

[0030] In the prior art, there is a lack of a method that can effectively identify and reject the original observables with large observation errors in differential positioning, and has a simpler modeling and less resource consumption.

[0031] To solve this problem, this application proposes a gross error rejection method, device, electronic device, and storage medium based on single difference. Specifically: Please refer toFigure 1 、In the first aspect, a method for eliminating gross errors based on single difference, the method includes the steps: S1: Obtain the original measurement values of the mobile station and the reference station respectively; S2: Form a single-difference observation equation set according to the original measurement values; S3: Perform least squares on the single-difference observation equation set to solve the floating-point solution of the unknowns; S4: Calculate the solution of the floating-point solution of the unknowns to obtain the calculation result, and construct a statistical test sequence according to the calculation result; S5: Calculate the statistical test quantity according to the statistical test sequence, and compare the constructed statistical test quantity with the preset test threshold; S6: When the statistical test quantity is greater than or equal to the preset test threshold, obtain the first single-difference observation equation corresponding to the statistical test quantity, and calculate the first single-difference observation equation to obtain precise positioning.

[0032] Among them, in step S1, the original measurement values are obtained by constructing a GNSS original pseudorange observation model and an original carrier phase observation model, and respectively constructing the observation models of the mobile station and the reference station according to the models; the inter-station single difference is adopted to eliminate the error terms in the observation models of the mobile station and the reference station, so as to obtain the original measurement values of the mobile station and the reference station.

[0033] Further, step S1 includes: S11: Construct a GNSS original pseudorange observation model and an original carrier phase observation model; S12: Respectively construct the observation models of the mobile station and the reference station according to the GNSS original pseudorange observation model and the original carrier phase observation model; S13: Adopt the inter-station single difference to eliminate the error terms in the observation models of the mobile station and the reference station, so as to obtain the original measurement values of the mobile station and the reference station.

[0034] Among them, the expression of the GNSS original pseudorange observation model is: The expression of the original carrier phase observation model is:

[0035] In the formula: 、 Are the pseudorange measurement value and the carrier measurement value respectively; Is the geometric distance from the receiver antenna to the satellite antenna; 、 Are the receiver clock error and the satellite clock error respectively; 、 Are the tropospheric delay error and the ionospheric delay error respectively; 、 are the receiver pseudorange hardware delay and the satellite pseudorange hardware delay, respectively; and are the receiver phase hardware delay and the satellite phase hardware delay, respectively; is the carrier phase wavelength; is the satellite ephemeris error; and are the noises of the pseudorange and carrier measurements, respectively; is the integer ambiguity.

[0036] Among them, the observation models of the reference station and the mobile station are similar to the above GNSS raw pseudorange observation model and the raw carrier phase observation model. Therefore, the observation models of the mobile station and the reference station can be constructed according to the GNSS raw pseudorange observation model and the raw carrier phase observation model. Further, the error terms in the observation models of the mobile station and the reference station are eliminated by using the single difference between stations, so as to obtain the raw measurement values of the mobile station and the reference station. Among them, the error terms at least include hardware delay error, satellite clock error, tropospheric and ionospheric delays; the functional model of the single difference between stations is:

[0037] In the formula: is the difference between the pseudorange observation value of the mobile station and the pseudorange observation value of the reference station; is the difference between the carrier phase observation value of the mobile station and the carrier phase observation value of the reference station; is the difference between the geometric distance from the receiver antenna of the mobile station to the satellite antenna and the geometric distance from the receiver antenna of the reference station to the satellite antenna; is the receiver clock error of the mobile station, is the receiver clock error of the reference station; is the difference between the tropospheric delay error of the mobile station and the tropospheric delay error of the reference station; is the difference between the ionospheric delay error of the mobile station and the tropospheric delay error of the reference station; is the carrier phase wavelength; is the integer ambiguity; is the pseudorange observation error term after single difference between stations, is the carrier phase observation error term after single difference between stations.

[0038] Among them, the raw measurement values at least include , , , , and , ; is the pseudorange observation value of the mobile station, is the pseudorange observation value of the reference station; is the geometric distance from the receiver antenna of the mobile station to the satellite antenna, the geometric distance from the receiver antenna of the reference station to the satellite antenna, is the carrier phase observation value of the mobile station, is the carrier phase observation value of the reference station.

[0039] In step S2, the original measurement values are used to construct the original single-difference observation equation; the original single-difference observation equation is linearly processed to obtain a multi-dimensional single-difference observation equation.

[0040] Furthermore, step S2 includes: S21: Obtain the original measurement values of the mobile station and the reference station for multiple satellites; S22: Construct the original single-difference observation equation according to multiple original measurement values; S23: Linearly process the original single-difference observation equation to obtain a multi-dimensional single-difference observation equation set.

[0041] Among them, the original measurement values that the mobile station and the reference station can receive for multiple satellites at least include the pseudorange observation value from the i-th satellite to the mobile station , the carrier phase observation value from the i-th satellite to the mobile station , the pseudorange observation value from the i-th satellite to the reference station , the carrier phase observation value from the i-th satellite to the reference station .

[0042] Construct the original single-difference observation equation according to the above multiple original measurement values, and linearly process the original single-difference observation equation. The processing process includes: First, linearly process the original single-difference observation equation of the mobile station at the reference point as: Equation (1); Secondly, the coordinates of the reference station are known, is the linearization of the equation observation value, so the linear processing process of the original single-difference observation equation of the reference station is: Equation (2); Among them, is the pseudorange observation value from the i-th satellite to the mobile station; is the carrier phase observation value of the i-th satellite to the mobile station; , , are the unit vector components of the i-th satellite in the coordinate system, representing the position direction of the satellite relative to the mobile station; , , is the coordinate offset of the mobile station relative to the reference point; is the error term of the pseudorange observation; is the error term of the carrier phase observation; is the carrier phase observation value of the i-th satellite to the mobile station; is the integer ambiguity of the carrier phase of the mobile station; In the formula: , , where is the mobile station coordinate; is the reference point coordinate.

[0043] Among them, is the pseudorange observation value of the i-th satellite to the reference station; is the carrier phase observation value of the i-th satellite to the reference station; is the geometric distance of the i-th satellite to the reference station; is the error term of the pseudorange observation; is the error term of the carrier phase observation; is the integer ambiguity.

[0044] Then, according to the above two formulas of formula (1) and formula (2), the linearized single-difference observation equation can be finally obtained as:

[0045] Simplifying this formula, the single-difference observation equation can be obtained as: Among them, is the difference between the pseudorange observation value of the i-th satellite to the mobile station and the pseudorange observation value of the i-th satellite to the reference station; is the difference between the carrier phase observation value of the i-th satellite to the mobile station and the carrier phase observation value of the i-th satellite to the reference station; The difference between the geometric distance from the i-th satellite to the mobile station and the geometric distance from the i-th satellite to the reference station; The difference between the receiver clock error of the mobile station and the receiver clock error of the reference station; The integer ambiguity; The pseudo-range observation error term after single-difference between stations, The carrier-phase observation error term after single-difference between stations.

[0046] When the receivers of the mobile station and the reference station receive common-view satellites, the multi-dimensional single-difference observation equation system constructed by the above basic equations is:

[0047] In step S3, the least squares adjustment is performed on the finally constructed multi-dimensional single-difference observation equation system in step S2 to obtain the floating-point solution of the unknowns.

[0048] Furthermore, in step S3, The form of the floating-point solution of the unknowns is: , where The design matrix of all unknowns in the single-difference observation equation; The weight matrix formed by the weights of each single-difference observation equation; Contains all the observation vectors in the single-difference observation equation, and T is the transpose matrix.

[0049] Specifically, when performing the single-difference gross error rejection method, it is first necessary to construct the single-difference observation equation.

[0050] Specifically, when performing the single-difference gross error rejection method, it is first necessary to construct the single-difference observation equation. The establishment of this equation system is based on the original measurement values of the mobile station and the reference station, which are usually obtained by GNSS receivers. Subsequently, in order to solve the floating-point solution of the unknowns in the equation system, the least squares method is adopted. During the least squares solution process, the design matrix is constructed to describe the relationship between the unknown parameters and the observed values in the observation equation. The weight matrix is established to reasonably allocate the weights of different observation equations in the solution, usually determined according to the accuracy of the observed values. For example, higher-precision observed values will be assigned greater weights. The observation vector is composed of all the observed values. By substituting the design matrix, the weight matrix, and the observation vector into the proposed formula, the floating-point solution of the unknowns can be calculated. This solution is the best estimate based on the least squares criterion, which provides the necessary data support for the construction of the subsequent statistical test sequence, and thus lays the foundation for the effective rejection of gross errors and the final precise positioning.

[0051] In step S4, the solution results at least include: the geometric distance between the position of the mobile station and the satellite, the single difference of clock errors between the mobile station and the reference station, and the floating-point solution of the ambiguity between the reference station and the satellite; the statistical test sequence is constructed according to the solution results.

[0052] Further, step S4 includes: S41: Solve the floating-point solution of the unknowns to obtain the solution results, and the solution results at least include: the geometric distance between the position of the mobile station and the satellite, the single difference of clock errors between the mobile station and the reference station, and the floating-point solution of the ambiguity between the reference station and the satellite; S42: Construct a statistical test sequence according to the geometric distance between the position of the mobile station and the satellite, the single difference of clock errors between the mobile station and the reference station, and the floating-point solution of the ambiguity between the reference station and the satellite.

[0053] Among them, the statistical test sequence is:.

[0054] Among them, n is the satellite number, n = 1, 2, 3... i... n; is the geometric distance between the mobile station and the nth satellite, is the geometric distance between the reference station and the nth satellite; , respectively represent the test statistics of the pseudo-range observation value and the carrier phase observation value of the nth satellite for the mobile station; represents the single difference of the inter-station clock errors between the mobile station and the reference station; is the integer ambiguity.

[0055] In step S5, the median of the statistical test sequence, the median of the absolute deviation, and the test values in each single-difference observation equation; the equation of the statistical test quantity is constructed according to the test value, the median, and the median of the absolute deviation and the statistical test quantity is calculated.

[0056] Further, step S5 includes: S51: Obtain the median of the statistical test sequence, the median of the absolute deviation, and the test values in each single-difference observation equation; S52: Construct an equation for calculating the statistical test quantity according to the test value, the median, and the median of the absolute deviation, and calculate the statistical test quantity.

[0057] Among them, the equation for calculating the statistical test quantity is: ; In the formula: is the jth statistical test quantity; is the j-th statistical value to be tested; is the median of the test sequence; is the proportionality coefficient; is the median of the absolute deviation. When in the test sequence calculated by there is a condition: , where: is the test threshold.

[0058] Among them contains and , where j is and the total quantity of , that is, j = 1, 2, 3... n... 2n.

[0059] In step S6, when the statistical test quantity is greater than or equal to the preset test threshold, the first single-difference observation equation corresponding to the statistical test quantity is obtained, and the first single-difference observation equation is solved to obtain precise positioning.

[0060] Furthermore, after step S6, it includes: S7: When the statistical test quantity is less than the preset test threshold, the second single-difference observation equation corresponding to the statistical test quantity is removed from the single-difference observation equation set to obtain the third single-difference observation equation; S8: Repeat the operations in steps S3 to S5 for the third single-difference observation equation until all the statistical test quantities corresponding to the third single-difference observation equation are greater than or equal to the preset test threshold, and then solve the third single-difference observation equation to obtain precise positioning.

[0061] Among them, if the i-th group of the second single-difference observation equation is removed, the remaining third single-difference observation equation is:

[0062] Both m and n are satellite numbers.

[0063] The subsequent iterative process includes repeatedly executing steps S3 to S5, that is, re-solving the floating-point solution of the unknowns for the third single-difference observation equation, obtaining the solution result, and calculating and comparing the statistical test quantity. This iterative process is carried out cyclically, and its termination condition is that when the statistical test quantities corresponding to all equations in the third single-difference observation equation are greater than or equal to the preset test threshold. When this condition is met, the iteration stops, and the system will perform the final calculation on the current third single-difference observation equation to obtain the precise positioning result. Through this mechanism of iterative rejection and recalculation, observation equations with large gross errors can be effectively identified and rejected. Even when the initial statistical test quantity does not reach the preset threshold, the observation equation set can be continuously optimized, thereby improving the accuracy of the final positioning. The rejection operation in step S6 prepares a more reliable data basis for subsequent higher-precision positioning calculations, while the iterative calculation in step S7 ensures that after the gross errors are rejected, positioning calculations can be performed based on purer observation data, and finally a more precise positioning result can be obtained.

[0064] Please refer to Figure 4 、 Figure 5 , which are the positioning results obtained by using the existing technology to solve the single-difference observation equation and the positioning result after using a gross error rejection method based on single-difference provided by this application respectively. After using the method of this application, the positioning accuracy is significantly improved.

[0065] Please refer to Figure 2 , on the second aspect, a gross error rejection device based on single-difference, the device includes: Acquisition module 201: used to acquire the original measurement values of the mobile station and the reference station respectively; Assembly module 202: used to assemble the single-difference observation equation set according to the original measurement values; First calculation module 203: used to perform least squares on the single-difference observation equation set to solve the floating-point solution of the unknowns; Construction module 204: used to calculate the solution result for the floating-point solution of the unknowns, obtain the solution result, and construct a statistical test sequence according to the solution result; Second calculation module 205: used to calculate the statistical test quantity according to the statistical test sequence, and compare the constructed statistical test quantity with the preset test threshold; Third calculation module 206: used to obtain the first single-difference observation equation corresponding to the statistical test quantity and calculate the first single-difference observation equation when the statistical test quantity is greater than or equal to the preset test threshold to obtain precise positioning.

[0066] Among them, the acquisition module 201 performs the acquisition operation of the original measurement values, and the observation data of the mobile station and the reference station are preliminarily collected therefrom.

[0067] The function of the formation module 202 is to receive the original measurement values from the acquisition module and establish a single-difference observation equation set based on these data, laying the equation foundation for the subsequent solution process.

[0068] The first solution module 203 is designed to process the single-difference observation equation set generated by the formation module, and calculate the floating-point solution of the unknowns using the least squares method, which is a preliminary estimate of the positioning parameters.

[0069] The function of the construction module 204 is to deeply solve the floating-point solution output by the first solution module, obtain the solution results, and construct a statistical test sequence based on these results, which is used for subsequent gross error detection.

[0070] The second solution module 205 is responsible for receiving the statistical test sequence, calculating the statistical test quantity, and comparing this test quantity with a preset test threshold to determine whether there is a gross error.

[0071] The third solution module 206 is activated when the second solution module 205 determines that there is no gross error. It can obtain the first single-difference observation equation associated with the over-threshold statistical test quantity and re-solve this equation to finally achieve precise positioning.

[0072] Specifically, the working principle of the gross error rejection device is as follows: First, through the acquisition module 201, the GNSS original observation data from the mobile station and the reference station are collected respectively, such as pseudorange and carrier phase observation values. Subsequently, the formation module uses these original observation values to construct an inter-station single-difference observation equation set to weaken or eliminate the common errors in the satellite terminal and the propagation path. The first solution module uses the least squares method to solve the single-difference observation equation set to obtain the floating-point solution of the unknowns including the position of the mobile station, the receiver clock error, and the ambiguity. The construction module further calculates the solution results such as the distance between the mobile station and the satellite, the single-difference of the inter-station clock error, and the floating-point solution of the ambiguity based on these floating-point solutions, and constructs a statistical test sequence using these results, for example, a test sequence can be constructed based on the residuals. The second solution module calculates the statistical test quantity, for example, a robust statistical test quantity can be constructed using the median and the median absolute deviation, and compares this test quantity with a preset threshold. If the test quantity exceeds the threshold, it indicates that there is no gross error in the corresponding observation equation. At this time, the third solution module 206 is triggered, and it will re-solve the observation equation without gross error, improve the positioning accuracy by eliminating the influence of gross errors, and finally output a precise positioning result.

[0073] In some specific embodiments, the acquisition module 201 can be implemented by GNSS receiver hardware, which is used to receive satellite signals and output raw measurement values. The formation module 202 and the first solution module 203 can be implemented by a processor executing corresponding program instructions in a memory. The processor is configured with a least squares solution algorithm. The construction module 204 can also be implemented by a processor executing program instructions, where the statistical test sequence can be constructed as a residual sequence. The calculation of the statistical test quantity in the second solution module 205 can be performed in the following manner, for example: First, calculate the median and the median absolute deviation of the statistical test sequence. Then, for each single-difference observation equation, calculate the difference between the corresponding value to be tested and the median, and divide this difference by the median absolute deviation to obtain the statistical test quantity. The preset test threshold can be set according to the actual application scenario and the required gross error detection sensitivity. When the third solution module 206 detects that the statistical test quantity is greater than or equal to the preset threshold, it acquires the corresponding first single-difference observation equation and performs solution again using the least squares method to obtain the final accurate positioning result. Through the collaborative work of the above modules, the device can effectively identify and eliminate gross errors in single-difference positioning and improve the positioning accuracy.

[0074] Figure 3 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The present application provides an electronic device 3, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not marked). The memory 302 stores computer-readable instructions executable by the processor 301. When the electronic device runs, the processor 301 executes the computer-readable instructions to perform the methods in any optional implementation manner of the above embodiments to implement the following functions: respectively acquire the raw measurement values of the mobile station and the reference station; form a single-difference observation equation set according to the raw measurement values; perform least squares on the single-difference observation equation set to solve the floating-point solution of the unknowns; perform solution on the floating-point solution of the unknowns to obtain a solution result, and construct a statistical test sequence according to the solution result; calculate a statistical test quantity according to the statistical test sequence, and compare the constructed statistical test quantity with a preset test threshold; when the statistical test quantity is greater than or equal to the preset test threshold, acquire the first single-difference observation equation corresponding to the statistical test quantity, and perform solution on the first single-difference observation equation to obtain accurate positioning.

[0075] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method in any optional implementation manner of the above embodiment is executed to implement the following functions: respectively obtain the original measurement values of the mobile station and the reference station; form a single-difference observation equation set according to the original measurement values; perform least squares on the single-difference observation equation set to solve the floating-point solution of the unknowns; perform calculation on the floating-point solution of the unknowns to obtain a calculation result, and construct a statistical test sequence according to the calculation result; calculate a statistical test quantity according to the statistical test sequence, and compare the constructed statistical test quantity with a preset test threshold; when the statistical test quantity is greater than or equal to the preset test threshold, obtain a first single-difference observation equation corresponding to the statistical test quantity, and perform calculation on the first single-difference observation equation to obtain precise positioning.

[0076] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0077] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces. The indirect coupling or communication connection of the devices or units may be in an electrical, mechanical or other form.

[0078] In addition, the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] Furthermore, each functional module in various embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0080] 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.

[0081] Any modifications made as described above are only examples of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and variations. All equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for eliminating gross errors based on single difference, characterized in that: The method comprises the steps of: S1: Get the original measurement values ​​of the mobile station and the base station respectively; S2: constructing a single-difference observation equation group according to the original measurement values; S3: performing least squares on the single-difference observation equation group to obtain a floating-point solution for the unknown number; S4: Solving the floating-point solution of the unknown number to obtain a solution result, and constructing a statistical test sequence according to the solution result; S5: Calculating a statistical test amount according to the statistical test sequence, and comparing the constructed statistical test amount with a preset test threshold; S6: When the statistical test value is greater than or equal to a preset test threshold, a first single-difference observation equation corresponding to the statistical test value is obtained, and the first single-difference observation equation is solved to obtain precise positioning.

2. The method for eliminating gross errors based on single difference according to claim 1, characterized in that: Step S6 includes: S7: when the statistical test value is less than the preset test threshold, the second single-difference observation equation corresponding to the statistical test value is eliminated from the single-difference observation equation group to obtain a third single-difference observation equation; S8: Repeat the operations in step S3 to step S5 for the third single-difference observation equation until all the statistical test quantities corresponding to the third single-difference observation equation are greater than or equal to the preset test threshold, and then solve the third single-difference observation equation to obtain precise positioning.

3. The method for eliminating gross errors based on single difference according to claim 1, characterized in that: Step S1 includes: S11: Construct GNSS original pseudorange observation model and original carrier phase observation model; S12: constructing observation models of the mobile station and the reference station respectively according to the GNSS original pseudorange observation model and the original carrier phase observation model; S13: using inter-station single difference to eliminate the error terms in the observation models of the mobile station and the reference station, thereby obtaining original measurement values ​​of the mobile station and the reference station.

4. The method for eliminating gross errors based on single difference according to claim 3, characterized in that: Step S2 includes: S21: Acquire original measurement values ​​of the mobile station and the reference station for multiple satellites; S22: constructing an original single-difference observation equation according to the plurality of original measurement values; S23: Linearizing the original single-difference observation equation to obtain a multi-dimensional group of single-difference observation equations.

5. The method for eliminating gross errors based on single difference according to claim 4, characterized in that: Step S4 includes: S41: Solving the floating-point solution of the unknown number to obtain the solution result, wherein the solution result at least includes: a geometric distance between the position of the mobile station and the satellite, a single difference of the clock difference between the mobile station and the reference station, and a floating-point solution of the ambiguity between the reference station and the satellite; S42: Construct the statistical check sequence according to the geometric distance between the position of the mobile station and the satellite, the single difference of the clock difference between the mobile station and the reference station, and the floating point solution of the ambiguity between the reference station and the satellite.

6. The method for eliminating gross errors based on single difference according to claim 5, characterized in that: Step S5 includes: S51: Obtain the median of the statistical test sequence, the median of the absolute deviation, and the value to be tested in each single-difference observation equation; S52: constructing an equation for calculating the statistical test value according to the value to be tested, the median and the median of the absolute deviation, and calculating and obtaining the statistical test value.

7. The method for eliminating gross errors based on single difference according to claim 1, characterized in that: In step S3, The form of the floating-point solution of the unknown number is: ,in is the design matrix of all unknowns in the single-difference observation equation; A weight matrix composed of weight combinations of each single-difference observation equation; Contains all observation vectors in the single-difference observation equation, and T is the transposed matrix.

8. A gross error elimination device based on single difference, characterized in that: The device comprises: Acquisition module: used to obtain the original measurement values ​​of the mobile station and the base station respectively; A building module: used for building a single-difference observation equation according to the original measurement value; A first solving module: used for performing least squares on the single-difference observation equation to solve the floating-point solution of the unknown number; Construction module: used for solving the floating point solution of the unknown number to obtain the solution result, and constructing a statistical test sequence according to the solution result; A second solving module: used for calculating the statistical test amount according to the statistical test sequence, and comparing the constructed statistical test amount with a preset test threshold; The third solving module is used to obtain a first single-difference observation equation corresponding to the statistical test quantity when the statistical test quantity is greater than or equal to a preset test threshold, and solve the first single-difference observation equation to obtain precise positioning.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are executed.

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