Ambiguity resolving method and device, electronic equipment and storage medium

Abstract

The invention provides an ambiguity resolving method and device, electronic equipment and a storage medium, and is applied to the technical field of navigation and positioning. According to the technical scheme, a non-combinatorial observation equation is established, sequential least square resolving and a flexible ambiguity fixing strategy are combined, and the ambiguity resolution efficiency is improved. The problems of large calculation burden, low fixed reliability and high misjudgment probability in ambiguity solution are effectively solved. The non-combination observation equation reserves complete information of original observation data, the calculation burden is reduced through sequential least square solution, and the fixing reliability and success rate are improved by selecting different ambiguity fixing methods according to the number of satellites. Especially when the number of satellites is large, an EWL-WL-NL step-by-step fixing method is adopted, the advantages of BDS three-frequency signals are fully utilized, and the accuracy and efficiency of ambiguity fixing are further improved.

Description

Technical Field

[0001] This application relates to the technical field of navigation and positioning, and particularly relates to a method and device for ambiguity resolution, an electronic device, and a storage medium. Background Art

[0002] With the gradual development of the information industry, people's demand for spatio-temporal information in daily life has been increasing day by day. The global satellite navigation system, with its characteristics of all-weather and high efficiency, is widely used in fields such as geodetic surveying, earthquake and tsunami early warning, time service, geophysics, and vehicle and ship positioning. With the advent of the Internet + and intelligent era, people's requirements for the real-time, reliability, and accuracy of spatio-temporal information are getting higher and higher.

[0003] The Beidou satellite navigation system was successfully networked in 2020. Its characteristics of multiple satellites, multiple frequency points, and full coverage provide more solutions for users' real-time high-precision positioning; but at the same time, it also brings a huge computational burden. How to reasonably use this rich observation information is a current research hotspot.

[0004] Currently, the mainstream real-time high-precision positioning is real-time kinematic (RTK). Its differential methods are mainly single-difference, double-difference, and triple-difference. No matter which differential form it is, the essence is to achieve high-precision positioning by using carrier phase observations with millimeter-level accuracy. Therefore, the high-precision RTK result depends on the correct resolution of the carrier phase ambiguity. Currently, the research on triple-frequency ambiguity fixing algorithms is mainly divided into two types: the LAMBDA search method based on a geometric model and the TCAR method based on a non-geometric model.

[0005] TCAR uses the pseudorange observations of each satellite as the distance reference to resolve the ultra-wide lane ambiguity, uses the ultra-wide lane observations as the reference to resolve the wide lane ambiguity, and uses the wide lane observations as the reference to resolve the basic frequency point ambiguity. The key to fixing the ambiguity at each step is to use the floating-point solution direct rounding method for fixing, without involving the search for ambiguity; and there are two ways for the geometric model LAMBDA search method. One is to form ultra-wide lane, wide lane, and narrow lane observation equations and use filtering algorithms to solve their floating-point solutions, and then sequentially perform LAMBDA search for fixing; the other is to use all non-combined observations to construct observation equations for filtering and resolution, and then directly perform LAMBDA search to achieve fixing.

[0006] The non-geometric TCAR method is adopted: the simple fixed method of directly rounding the ambiguity is prone to the phenomenon of incorrect ambiguity judgment, and the error rate of ambiguity fixing, especially the second-step wide-lane ambiguity fixing, is large. For the LAMBDA method based on the geometric model: in the first case, satellite screening is required in each step and then the observation equation is constructed. Through multiple filtering solutions and ambiguity searches, the calculation is complex. Whether the latter-level fixing is correct depends extremely on the former, and whether to fix depends entirely on the Ratio-test criterion. A strict Ratio-test setting will lead to waste of observations, while a loose one is prone to incorrect ambiguity fixing; and the method of directly using the original observations has an extremely large amount of calculation, which will bring a great burden to the ambiguity search.

[0007] Therefore, in the prior art, there is a lack of a method that can reduce the calculation burden, improve the reliability of ambiguity fixing, and reduce the probability of incorrect judgment. Summary of the Invention

[0008] In view of the deficiencies of the above prior art, the present application provides an ambiguity resolution method, device, electronic device, and storage medium, which are applied to the field of navigation and positioning technology, and have the advantages of reducing the calculation burden, improving the reliability of ambiguity fixing, and reducing the probability of incorrect judgment.

[0009] In a first aspect, an ambiguity resolution method, the method includes the steps of: S1: Establish a non-combined BDS triple-frequency pseudorange equation and a carrier phase observation equation; S2: Perform sequential least squares solution on the BDS triple-frequency pseudorange equation and the carrier phase observation equation to obtain a floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; S3: Obtain the number of satellites participating in the solution at the current epoch. If the number of satellites is less than 5, use LAMBDA for ambiguity fixing according to the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; S4: If the number of satellites is greater than or equal to 5, use EWL-WL-NL to perform ambiguity fixing step by step according to the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix.

[0010] A method for ambiguity resolution provided by the present application. Through establishing non-combination observation equations, combining sequential least squares solution and a flexible ambiguity fixing strategy, it effectively solves the problems of large computational burden, low fixing reliability, and high misjudgment probability in ambiguity resolution. The non-combination observation equations retain the complete information of the original observation data, the sequential least squares solution reduces the computational burden, and selecting different ambiguity fixing methods according to the number of satellites improves the fixing reliability and success rate. Especially the EWL-WL-NL step-by-step fixing method adopted when the number of satellites is large makes full use of the advantages of the BDS triple-frequency signals, further improving the accuracy and efficiency of ambiguity fixing.

[0011] Further, step S1 includes: S11: Establish non-combination original BDS triple-frequency pseudorange equations and original carrier phase observation equations; S12: Based on the original BDS triple-frequency pseudorange equations and the original carrier phase observation equations, establish an inter-station single-difference observation equation set for satellites between two different stations; S13: Obtain a reference satellite, construct a double-difference ambiguity between the satellite and the reference satellite based on the inter-station single-difference observation equation set, and reconstruct the final BDS triple-frequency pseudorange equations and the carrier phase observation equations based on the double-difference ambiguity.

[0012] A method for ambiguity resolution provided by the present application. By establishing original BDS triple-frequency pseudorange equations and original carrier phase observation equations, it retains all the information of the original observations, providing a complete data source for subsequent differential processing; by taking the difference of the observations of the same satellite at two stations, it can effectively eliminate or weaken the errors at the satellite end, improving the accuracy of the inter-station single-difference observation equation set; by selecting a reference satellite and constructing a double-difference ambiguity, it can further eliminate or weaken the errors at the receiver end. The final observation equations reconstructed based on the double-difference ambiguity greatly reduce the influence of systematic errors, improving the accuracy and reliability of ambiguity resolution.

[0013] Further, in step S2, the floating-point solution matrix of the parameters to be estimated is: , represents the floating-point solution of the ambiguity, represents non-ambiguity parameters, including but not limited to: coordinates XYZ, ionospheric delay, tropospheric delay, receiver clock error, pseudorange hardware delay, and carrier phase hardware delay; The variance-covariance matrix is: , where is the variance-covariance matrix, including four sub-matrices , , and , is the floating-point solution of the ambiguity is the variance-covariance matrix of is the floating-point solution of the ambiguity and the non-ambiguity parameters is the variance-covariance matrix between them; is is the transpose matrix of is the non-ambiguity parameter is the variance-covariance matrix of

[0014] A method for resolving ambiguity provided by this application. The construction of the floating-point solution matrix and variance-covariance matrix of the parameters to be estimated can effectively improve the positioning accuracy and reduce the computational burden by accurately calculating the floating-point solution of the ambiguity and non-ambiguity parameters. Through a reasonably designed variance-covariance matrix, more reliable parameter estimation can be provided during the ambiguity resolution process, thereby improving the performance and accuracy of the overall positioning system.

[0015] Further, in step S3, the formula for fixing using LAMBDA is: , where is the optimal integer ambiguity solution vector obtained through the LAMBDA method search; is the candidate integer ambiguity solution vector in the search space, belonging to the integer set Z;. is the floating-point ambiguity solution vector, obtained by linearly transforming the floating-point ambiguity solution , that is , where is a design matrix used to transform the floating-point ambiguity solution into a new coordinate system; is is the variance-covariance matrix after transformation, used to describe the accuracy information of, and the calculation formula is: ; Step S3 includes: S31: After searching using LAMBDA, taking the Ratio-test value greater than 3 as the basis for successful fixing, after successful fixing, in turn, correct the non-ambiguity parameters, and the specific formula is as follows: , where is the corrected non-ambiguity parameter is the variance-covariance matrix of is the corrected non-ambiguity parameter is the ambiguity parameter after being fixed by LAMBDA; is the non-ambiguity parameter without correction and the floating-point ambiguity solution vector The covariance matrix between; is the transposed matrix of.

[0016] A method for ambiguity resolution provided by this application, by using the LAMBDA method to fix in the case of a small number of satellites (less than 5), avoids the problems of missing accuracy and reliability caused by complex calculations faced by the LAMBDA method when the number of satellites is large. The use of the LAMBDA method to fix in the case of less than 5 satellites proposed by this application not only improves the success rate of ambiguity fixing, but also further improves the overall positioning accuracy through the correction of non-ambiguity parameters. The innovation of this method lies in its organic combination of ambiguity fixing and parameter correction, forming a more complete and reliable ambiguity resolution process.

[0017] Further, step S4 includes: S41: If the number of satellites is greater than or equal to 5, screen the ambiguity parameters with variances less than a preset variance according to the variance-covariance matrix; S42: Combine the ambiguity parameters of B2 frequency band and B3 frequency band in an optimal combination manner to form an EWL ambiguity floating-point solution, and its corresponding variance-covariance matrix, and perform EWL ambiguity fixing by directly rounding; S43: After the EWL ambiguity is fixed, correct the accuracy of the ambiguity parameters of the B3 frequency band according to the EWL ambiguity floating-point solution and the corresponding variance-covariance matrix; S44: Combine the corrected ambiguity parameters of the B3 frequency band and the ambiguity parameters of the B1 frequency band in an optimal combination manner to form a WL ambiguity floating-point solution, an NL ambiguity floating-point solution, and their respective corresponding variance-covariance matrices; S45: Perform WL ambiguity fixing according to the WL ambiguity floating-point solution and its corresponding variance-covariance matrix; after obtaining the WL fixed solution, correct the floating-point solution of NL, and perform NL ambiguity fixing according to the corrected NL ambiguity floating-point solution and its corresponding variance-covariance matrix.

[0018] Further, in step S42, the ambiguity parameter of the B2 frequency band is , the ambiguity parameter of the B3 frequency band is , the EWL ambiguity floating-point solution is , the EWL ambiguity The specific calculation formula for fixing is: , is the ambiguity floating-point solution, is the transformation matrix, n represents the satellite number, represents the ambiguity floating-point solution of satellite No. 1 at the B1 frequency band, Denote the float solution of the ambiguity of satellite No. n at frequency B1; Denote the float solution of the ambiguity of satellite No. 1 at frequency B2, Denote the float solution of the ambiguity of satellite No. n at frequency B2; Denote the float solution of the ambiguity of satellite No. 1 at frequency B3, Denote the float solution of the ambiguity of satellite No. n at frequency B3; The corresponding variance-covariance matrix is: , where, is the variance-covariance matrix of the float solution of the ambiguity , is 's variance-covariance matrix, is 's transpose matrix.

[0019] Furthermore, in step S43, the formula for correcting the accuracy of the ambiguity parameter at frequency B3 according to the EWL float solution of the ambiguity and the corresponding variance-covariance matrix is: , , ; where, is the variance-covariance matrix between the ambiguity parameter at frequency B3 and the EWL ambiguity, is 's transpose matrix, is the corrected float solution of the ambiguity at frequency B3, is the corrected variance-covariance matrix corresponding to at frequency B3.

[0020] In a second aspect, an ambiguity resolution device runs the steps of any of the above methods. The device includes: A construction module: used to establish non-combined BDS triple-frequency pseudorange equations and carrier phase observation equations; A calculation module: used to perform sequential least squares solution on the BDS triple-frequency pseudorange equations and the carrier phase observation equations to obtain a matrix of float solutions of parameters to be estimated and its corresponding variance-covariance matrix; A first fixing module: used to obtain the number of satellites participating in the solution in the current epoch. If the number of satellites is less than 5, perform ambiguity fixing using LAMBDA according to the matrix of float solutions of parameters to be estimated and its corresponding variance-covariance matrix; A second fixing module: used to, if the number of satellites is greater than or equal to 5, perform ambiguity fixing step by step using EWL-WL-NL according to the matrix of float solutions of parameters to be estimated and its corresponding variance-covariance matrix.

[0021] In a third aspect, the present application provides an electronic device, including a processor and a memory. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect above are run.

[0022] In a fourth aspect, 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 steps in the method provided in the first aspect above are run.

[0023] Beneficial effects: A method, device, electronic device and storage medium for ambiguity resolution proposed in the present application. Through establishing a non-combination observation equation, combining sequential least squares solution and a flexible ambiguity fixing strategy, the problems of large computational burden, low fixing reliability and high misjudgment probability in ambiguity resolution are effectively solved. The non-combination observation equation retains the complete information of the original observation data, the sequential least squares solution reduces the computational burden, and the selection of different ambiguity fixing methods according to the number of satellites improves the reliability and success rate of fixing. Especially, the EWL-WL-NL step-by-step fixing method adopted when the number of satellites is large makes full use of the advantages of the BDS triple-frequency signal, further improving the accuracy and efficiency of ambiguity fixing. Description of the Drawings

[0024] Figure 1 It is a schematic flowchart of a method for ambiguity resolution proposed in the present application.

[0025] Figure 2 It is a schematic structural diagram of a device for ambiguity resolution proposed in the present application.

[0026] Figure 3 It is a schematic structural diagram of the electronic device provided in the present application.

[0027] Figure 4 It is an algorithm flowchart of a method for ambiguity resolution proposed in the present application.

[0028] Label description: 201, construction module; 202, calculation module; 203, first fixing module; 204, second fixing module; 3, electronic device; 301, processor; 302, memory; 303, communication bus. Detailed Embodiments

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to 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. 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.

[0030] It should be noted that similar reference numerals and letters denote 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.

[0031] In the application of the global satellite navigation system, the multi-satellite, multi-frequency point, and full-coverage characteristics of the Beidou satellite navigation system provide new possibilities for real-time high-precision positioning. However, these rich observation information also bring a huge computational burden. Existing real-time kinematic (RTK) positioning technologies mainly rely on the correct solution of carrier phase ambiguities, but there are some problems with current triple-frequency ambiguity fixing algorithms. The TCAR method based on the geometry-free model is prone to ambiguity misjudgment, especially with a high error rate when fixing the wide-lane ambiguity. The LAMBDA method based on the geometric model is computationally complex, with a low fixing rate or prone to incorrect ambiguity fixing. These problems seriously affect the positioning accuracy and reliability of the system and restrict the application of high-precision positioning technologies in various fields.

[0032] To solve this problem, the present application proposes an ambiguity resolution method, device, electronic device, and storage medium. Specifically: Please refer to Figure 1 、 Figure 4 , in the first aspect, an ambiguity resolution method, the method includes the steps of: S1: Establish a non-combined BDS triple-frequency pseudorange equation and a carrier phase observation equation; S2: Perform sequential least squares solution on the BDS triple-frequency pseudorange equation and the carrier phase observation equation to obtain a floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; S3: Obtain the number of satellites participating in the solution at the current epoch. If the number of satellites is less than 5, use LAMBDA for ambiguity fixing according to the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; S4: If the number of satellites is greater than or equal to 5, based on the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix, the ambiguity fixing is performed step by step using EWL-WL-NL.

[0033] Among them, in step S1, the satellite parameters for establishing the non-combined BDS triple-frequency pseudorange equation and carrier phase observation equation are obtained by the receiver.

[0034] Among them, in step S2, the sequential least squares solution is a recursive estimation method that can process the observation data step by step without processing all the data at once. Specifically, it can be implemented using a Kalman filter. This method can effectively reduce the computational burden and is especially suitable for processing a large amount of real-time observation data.

[0035] Among them, in step S3, the LAMBDA method is a commonly used integer ambiguity estimation technique. Specifically, it can be implemented using an integer least squares search algorithm. This method can still effectively perform ambiguity search and fixing when the number of satellites is small.

[0036] Among them, in step S4, the step-by-step fixing of EWL-WL-NL refers to the process of first fixing the Extra-Wide-Lane (EWL) ambiguity, then the Wide-Lane (WL) ambiguity, and finally the Narrow-Lane (NL) ambiguity. Specifically, it can be fixed in the order from long wavelength to short wavelength. This method makes full use of the characteristics of the BDS triple-frequency signal and can improve the reliability and efficiency of ambiguity fixing.

[0037] This application proposes a flexible ambiguity resolution strategy, which selects different ambiguity fixing methods according to the number of satellites participating in the resolution. When the number of satellites is small, the LAMBDA method is used for ambiguity fixing; when the number of satellites is large, the step-by-step fixing method of EWL-WL-NL is used. This strategy fully considers the characteristics under different observation conditions and effectively balances the computational efficiency and fixing reliability.

[0038] The working principle of this application can be described in detail as follows: First, establish the non-combined BDS triple-frequency pseudorange equation and carrier phase observation equation. This step retains the complete information of the original observation data and provides a reliable data basis for subsequent resolution. Then, perform sequential least squares solution on these equations. This solution method can process the observation data step by step and effectively reduces the computational burden. Through the solution, the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix are obtained, which provides the necessary information for subsequent ambiguity fixing.

[0039] Next, the system obtains the number of satellites involved in the solution after the current epoch. If the number of satellites is less than 5, the LAMBDA method is used for ambiguity fixing. The LAMBDA method can still effectively perform ambiguity search and fixing in the case of a small number of satellites through the integer least squares search algorithm. If the number of satellites is greater than or equal to 5, the EWL-WL-NL step-by-step fixing method is adopted. This method first fixes the ultra-wide lane ambiguity, then the wide lane ambiguity, and finally the narrow lane ambiguity. This step-by-step fixing method makes full use of the characteristics of the BDS triple-frequency signal and can improve the reliability and efficiency of ambiguity fixing.

[0040] The reason for choosing this flexible ambiguity fixing strategy is that when the number of satellites is small, the search space of the LAMBDA method is relatively small and the computational efficiency is high; while when the number of satellites is large, the EWL-WL-NL step-by-step fixing method can better utilize the advantages of multi-frequency observations and improve the fixing success rate. The choice of this strategy has a significant impact on the overall performance of the system and can maintain a high ambiguity fixing success rate and positioning accuracy under different observation conditions.

[0041] In the above process of this application, non-combined BDS triple-frequency pseudorange equations and carrier phase observation equations are proposed for ambiguity resolution. However, in this process, directly establishing non-combined original observation equations may lead to problems such as high computational complexity and inaccurate models. In addition, the original observation equations contain many error sources, such as atmospheric delay, receiver clock bias, etc. If these errors cannot be effectively eliminated or weakened, they will affect the accuracy and reliability of ambiguity resolution.

[0042] To solve this problem, further, step S1 includes: S11: Establish non-combined original BDS triple-frequency pseudorange equations and original carrier phase observation equations; S12: Based on the original BDS triple-frequency pseudorange equations and original carrier phase observation equations, establish an inter-station single-difference observation equation set between two different stations of the satellites; S13: Obtain a reference satellite, construct a double-difference ambiguity between the satellite and the reference satellite based on the inter-station single-difference observation equation set, and reconstruct the final BDS triple-frequency pseudorange equations and carrier phase observation equations based on the double-difference ambiguity.

[0043] This application gradually optimizes the observation equation in three steps to improve the accuracy and efficiency of ambiguity resolution. First, the original observation equation is established to provide basic data for subsequent processing. This step preserves all the information of the original observations and provides a complete data source for subsequent differential processing. Second, the single-difference processing between stations is introduced. By taking the difference of the observations of the same satellite at two stations, the errors at the satellite end, such as satellite clock error and orbit error, can be effectively eliminated or reduced. This step significantly improves the accuracy of the observation equation. Finally, the double-difference processing is further introduced. By selecting a reference satellite and constructing double-difference ambiguities, the errors at the receiver end can be further eliminated or reduced. Based on the final observation equation reconstructed from double-difference ambiguities, the influence of systematic errors is greatly reduced, and the accuracy and reliability of ambiguity resolution are improved.

[0044] Specifically, the original BDS triple-frequency pseudorange equation is as follows: , where s represents the satellite, r represents the receiver, and c represents the speed of light; B1, B2, and B3 respectively represent different frequency points; 、 、 respectively represent the pseudorange at B1 frequency point, the pseudorange at B2 frequency point, and the pseudorange at B3 frequency point, with the unit of m; represents the distance between the satellite antenna phase center and the receiver antenna phase center, with the unit of m; 、 respectively represent the receiver clock error and the satellite clock error, with the unit of s; represents the ionospheric frequency factor from the satellite to the receiver; is the zenith tropospheric delay from the satellite to the receiver, with the unit of m; 、 、 are respectively the tropospheric projection functions at B1 frequency point, B2 frequency point, and B3 frequency point; 、 、 are respectively the slant ionospheric delays from the satellite to the receiver at B1 frequency point, B2 frequency point, and B3 frequency point, with the unit of s; 、 、 respectively represent the receiver-end pseudorange hardware delays at B1 frequency point, B2 frequency point, and B3 frequency point, with the unit of s; 、 、 respectively represent the satellite - side pseudorange hardware delays at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of s; and and are respectively the slant - path ionospheric delays from the satellite to the receiver at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of s; and and respectively represent the receiver - side pseudorange hardware delays at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of s; and and respectively represent the satellite - side pseudorange hardware delays at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of s; , where and and respectively represent the carrier - phase observations at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of m; and and respectively represent the receiver - side carrier - phase hardware delays at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of s; and and respectively represent the satellite - side carrier - phase hardware delays at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of s; and and respectively represent the carrier wavelengths at B1 frequency band, B2 frequency band, and B3 frequency band; and and respectively represent the integer ambiguities from the satellite to the receiver at B1 frequency band, B2 frequency band, and B3 frequency band; represents the carrier - phase systematic error from the satellite to the receiver, and this systematic error includes at least: phase - center correction, tidal correction, general - relativity effect, and antenna phase - wrapping error; and and respectively represent the unmodeled errors of the carrier - phase from the satellite to the receiver at B1 frequency band, B2 frequency band, and B3 frequency band, with the unit of m.

[0045] The difference of the synchronous observation values of satellite s between receiver r and station b is the single difference r-b between stations. The single difference observation equation set includes the BDS triple-frequency single difference pseudorange equation and the single difference carrier phase observation equation, specifically as follows: ; ; Among them, represents the single difference pseudorange at B1 frequency point, represents the single difference pseudorange at B2 frequency point, represents the single difference pseudorange at B3 frequency point; is the difference between the distance between the satellite antenna phase center and the receiver antenna phase center, and the distance between the satellite antenna phase center and the antenna phase center of station b; represents the difference between the receiver and the observation station clock biases; represents the difference between the ionospheric frequency factors from the satellite to the receiver and from the satellite to the observation station; represents the difference between the zenith tropospheric delay from the satellite to the receiver and the zenith tropospheric delay from the satellite to the observation station; 、 、 represents the difference between the slant ionospheric delays from the satellite to the receiver at B1, B2, and B3 frequency points and the slant ionospheric delays from the satellite to the observation station; 、 、 represents the difference between the pseudorange hardware delays at the receiver end and the pseudorange hardware delays at the observation station at B1, B2, and B3 frequency points; represents the difference between the pseudorange systematic errors between the receiver and the observation station; 、 、 respectively represent the errors that cannot be modeled in the BDS triple-frequency single difference pseudorange equations at B1, B2, and B3 frequency points.

[0046] 、 、 respectively represent the differences in the carrier phase observations from the satellite to the receiver and from the satellite to the observation station at B1, B2, and B3 frequency points; 、 、 respectively represent the differences in carrier phase hardware delays between the receiver ends and the observation station ends of the B1 frequency point, B2 frequency point, and B3 frequency point; 、 、 respectively represent the single-difference ambiguities between stations of the B1 frequency point, B2 frequency point, and B3 frequency point; represents the difference in systematic errors of carrier phases between the satellite and the receiver and between the satellite and the measuring station; , , respectively represent the differences in unmodeled errors of carrier phases between the receiver and the observation station of the B1 frequency point, B2 frequency point, and B3 frequency point.

[0047] Among them, the obtained reference satellite is , and the double-difference ambiguity can be constructed through the reference satellite 、 、 , and constructing the double-difference ambiguity can eliminate its non-integer characteristics, and then the final BDS triple-frequency pseudorange equation and carrier phase observation equation are reconstructed and obtained by using the double-difference ambiguity. Specifically, the BDS triple-frequency pseudorange equation is:

[0048] The carrier phase observation equation is: .

[0049] The technical solution of this application effectively reduces the computational burden of processing a large amount of data at one time and improves the computational efficiency through step-by-step processing. At the same time, by retaining the original observation value information and improving the data quality through differential processing, the balance between information retention and error elimination is achieved. In addition, by constructing the double-difference ambiguity, the complexity of ambiguity resolution is simplified, which is beneficial to subsequent integer ambiguity fixing.

[0050] Through the above BDS triple-frequency pseudorange equation and carrier phase observation equation, sequential least squares solution is performed on them, and the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix can be obtained. Among them, the parameters to be estimated include ambiguity parameters and non-ambiguity parameters.

[0051] Furthermore, in step S2, the floating-point solution matrix of the parameters to be estimated is: , represents the float solution of ambiguity, represents the non-ambiguity parameters, including but not limited to: coordinates XYZ, ionospheric delay, tropospheric delay, receiver clock bias, pseudorange hardware delay, and carrier phase hardware delay; The variance-covariance matrix is: , where, is the variance-covariance matrix, including four sub-matrices , , and , is the variance-covariance matrix of the float solution of ambiguity , is the variance-covariance matrix between the float solution of ambiguity and the non-ambiguity parameters ; is the transpose matrix of and is the variance-covariance matrix of the non-ambiguity parameters

[0052] The method for constructing the float solution matrix and variance-covariance matrix of the parameters to be estimated proposed in this application aims to improve the positioning accuracy and reduce the computational burden. This method can effectively improve the performance and accuracy of the positioning system by accurately calculating the float solution of ambiguity and non-ambiguity parameters.

[0053] Specifically, the float solution matrix of the parameters to be estimated is divided into two parts: the float solution of ambiguity and the non-ambiguity parameters . This separation enables the system to process and optimize these two types of parameters separately, thereby improving the overall positioning accuracy.

[0054] In the variance-covariance matrix , describes the accuracy of the float solution of ambiguity, reflects the accuracy of the non-ambiguity parameters, 、 represents the correlation between the float solution of ambiguity and the non-ambiguity parameters. This accuracy description enables the system to better evaluate and optimize the estimation results of each parameter. The system can provide more reliable parameter estimation during the ambiguity resolution process without performing multiple filtering solutions and complex ambiguity searches. This not only reduces the computational burden but also improves the efficiency of the entire positioning process.

[0055] Furthermore, in step S3, the formula for fixing using LAMBDA is: , where, is the optimal integer ambiguity solution vector obtained by searching with the LAMBDA method; is a candidate integer ambiguity solution vector in the search space and belongs to the integer set Z; is the floating-point ambiguity solution vector, which is obtained by performing a linear transformation on the floating-point ambiguity solution matrix i.e., , where is a design matrix used to transform the floating-point ambiguity solution into a new coordinate system; is the variance-covariance matrix after transformation, which is used to describe the precision information of the floating-point ambiguity solution vector , and the calculation formula is: ; Step S3 includes: S31: After searching with LAMBDA, taking the Ratio-test value greater than 3 as the basis for successful fixing, after successful fixing, conversely correct the non-ambiguity parameter, and the specific formula is as follows: , where is the corrected non-ambiguity parameter 's variance-covariance matrix, is the corrected non-ambiguity parameter, is the ambiguity parameter after being fixed by LAMBDA; is the uncorrected non-ambiguity parameter and the changed floating-point ambiguity solution 's variance-covariance matrix; is 's transpose matrix.

[0056] Among them, the LAMBDA search formula is used to find the optimal integer ambiguity vector solution in the integer space. This formula achieves this goal by minimizing the weighted squared difference between the floating-point ambiguity solution and the integer ambiguity. This method can effectively search for the best solution in the integer space, thereby improving the accuracy of ambiguity fixing.

[0057] Taking the Ratio-test judgment criterion with a value greater than 3 as the basis for successful fixing. This criterion can effectively balance the fixing success rate and reliability, reducing the probability of incorrect fixing. By setting an appropriate threshold, this method can improve the fixing success rate while ensuring the correctness of ambiguity fixing.

[0058] The ambiguity parameter correction is carried out after the ambiguity is successfully fixed. By correcting the ambiguity parameter through a specific formula, the accuracy of the ambiguity parameter can be improved, thereby improving the overall positioning accuracy. This correction takes into account the influence of the ambiguity fixing result on other parameters, making the entire solution process more accurate.

[0059] The variance-covariance matrix update is carried out simultaneously. Updating the variance-covariance matrix of the ambiguity parameter helps to more accurately evaluate the uncertainty of the corrected parameter.

[0060] In the above process of this application, when the number of satellites is less than 5, the ambiguity parameter is fixed by the LAMBDA method to improve the reliability of ambiguity fixing and reduce the misjudgment probability. However, in this process, when the number of satellites is greater than or equal to 5, the computational complexity of this method is too large and it overly relies on the accuracy of the previous fixation, resulting in a low success rate of ambiguity fixing and a heavy computational burden.

[0061] To solve this problem, further, step S4 includes: S41: If the number of satellites is greater than or equal to 5, screen the ambiguity parameters with variances less than the preset variance according to the variance-covariance matrix; S42: Combine the ambiguity parameters of B2 frequency band and B3 frequency band in an optimal combination manner to form the EWL ambiguity float solution, and its corresponding variance-covariance matrix, and perform EWL ambiguity fixing by directly rounding; S43: After the EWL ambiguity is fixed, correct the accuracy of the ambiguity parameter of B3 frequency band according to the EWL ambiguity float solution and its corresponding variance-covariance matrix; S44: Combine the corrected ambiguity parameter of B3 frequency band and the ambiguity parameter of B1 frequency band in an optimal combination manner to form the WL ambiguity float solution, NL ambiguity float solution, and their respective corresponding variance-covariance matrices; S45: Perform WL ambiguity fixing according to the WL ambiguity float solution and its corresponding variance-covariance matrix; after obtaining the WL fixed solution, correct the float solution of NL, and perform NL ambiguity fixing according to the corrected NL ambiguity float solution and its corresponding variance-covariance matrix.

[0062] Among them, considering that the ambiguity accuracy of the newly launched satellite and the re-initialized ambiguity after cycle slips is relatively low and difficult to be accurately fixed, and at the same time, the variance-covariance matrix of the ambiguity float solution, as one of the indicators to measure the ambiguity estimation accuracy, can fully reflect the closeness between the ambiguity float solution and the fixed solution. When the float solution has a smaller variance, it means that it is closer to the fixed solution and the possibility of correct fixation is greater. Based on this, the float solution screening threshold is obtained according to a large amount of measured data, and the triple-frequency ambiguities that meet the accuracy requirements are screened out from the satellites participating in the filtering solution before fixation. The variance screening principle is the ambiguity parameter with a variance less than the preset variance, and the ambiguity parameter of the preset variance can specifically be 0.76.

[0063] Among them, the ambiguity parameters of the B2 frequency point and the B3 frequency point are combined into the EWL ambiguity float solution in an optimal combination manner. The optimal combination manner can be selected according to the characteristics of different frequency points and the observation conditions. For example, methods such as weighted average and least squares method can be used. This step not only improves the accuracy of the EWL ambiguity but also lays a foundation for the subsequent fixation of the WL and NL ambiguities.

[0064] After the EWL ambiguity is fixed, by using the information of the fixed EWL ambiguity to improve the accuracy of the ambiguity parameter of the B3 frequency point, a feedback mechanism is formed, effectively improving the accuracy of the overall solution.

[0065] The corrected ambiguity parameter of the B3 frequency point and the ambiguity parameter of the B1 frequency point are combined into the WL and NL ambiguity float solutions in an optimal combination manner, making full use of the relationship between different frequency points. Through reasonable combination, the accuracy and reliability of the ambiguity solution are further improved.

[0066] Among them, the WL and NL ambiguities are fixed respectively. This step-by-step fixation method not only reduces the computational complexity but also improves the success rate of ambiguity fixation.

[0067] In practical applications, the technical solution of this application can be executed according to the following steps: First, when the number of satellites is greater than or equal to 5, the system will automatically trigger the ambiguity solution method of this application. The system first analyzes the variance-covariance matrix and sets an appropriate ambiguity parameter of the preset variance. For example, the ambiguity parameter of the preset variance can be set to 0.76. Then, the system will check the variance of each ambiguity parameter one by one, retain the parameters with a variance less than 0.76, and the other parameters will not participate in the subsequent calculation temporarily. This step can effectively reduce the computational burden and at the same time ensure that the ambiguity parameters participating in the solution have high reliability.

[0068] Next, the system combines the ambiguity parameters of the B2 frequency point and the B3 frequency point.

[0069] In step S42, the ambiguity parameter of the B2 frequency point is , the ambiguity parameter of the B3 frequency point is , the floating-point solution of the EWL ambiguity is , the EWL ambiguity The specific calculation formula for fixing is: , where is the floating-point solution of the ambiguity, is the transformation matrix, n represents the satellite number, represents the floating-point solution of the ambiguity of satellite No. 1 at the B1 frequency point, represents the floating-point solution of the ambiguity of satellite No. n at the B1 frequency point; represents the floating-point solution of the ambiguity of satellite No. 1 at the B2 frequency point, represents the floating-point solution of the ambiguity of satellite No. n at the B2 frequency point; represents the floating-point solution of the ambiguity of satellite No. 1 at the B3 frequency point, represents the floating-point solution of the ambiguity of satellite No. n at the B3 frequency point; Its corresponding variance-covariance matrix is: , where is the variance-covariance matrix of the floating-point solution of the ambiguity , is 's variance-covariance matrix, is 's transposed matrix.

[0070] By accurately calculating the floating-point solution of the EWL ambiguity and its variance-covariance matrix, the success rate and reliability of ambiguity fixing can be significantly improved. This method not only considers the calculation accuracy but also takes into account the calculation efficiency, effectively solving the problems of insufficient accuracy or excessive computational complexity that may exist in traditional methods.

[0071] The system then directly rounds and fixes the EWL ambiguity. Since the wavelength of the EWL ambiguity is relatively long, the method of direct rounding can usually obtain a high success rate of fixing, and the Boostrapping success rate is used as the judgment basis. The formula for the Boostrapping success rate is as follows: , where is the complementary error function, is the EWL integer ambiguity rounded after epoch-to-epoch smoothing, and its expression is: , where x ; is the fixed solution, is regarded as a correct fix only when it is greater than the set prior threshold, and the threshold is set to 0.999.

[0072] In step S43, after fixation, the system uses the information of the EWL ambiguity to correct the accuracy of the ambiguity parameter of the B3 frequency point. The specific correction formula is: ， , ; where is the variance-covariance matrix between the ambiguity parameter of the B3 frequency point and the EWL ambiguity, is 's transpose matrix, is the floating-point solution of the ambiguity after correction of the B3 frequency point, is the variance-covariance matrix corresponding to after correction of the B3 frequency point.

[0073] After correction, the system combines the ambiguity parameters of the B3 frequency point and the B1 frequency point again to obtain the floating-point solutions of the WL and NL ambiguities.

[0074] At this time, the after being corrected by the ultra-wide-lane ambiguity and the original ambiguity are combined in an optimal combination method to form the floating-point solution of the wide-lane (WL) ambiguity , the floating-point solution of the narrow-lane (NL) ambiguity . The specific formula is similar to that of the ultra-wide-lane and is:

[0075]

[0076]

[0077]

[0078] where is the floating-point solution of the WL ambiguity, is the transformation matrix, is 's transpose matrix, is the variance-covariance matrix corresponding to , is the floating-point solution of the NL ambiguity, is the transformation matrix, is 's transpose matrix, is the variance-covariance matrix corresponding to .

[0079] Regarding its WL ambiguity and NL ambiguity as the overall ambiguity N, the floating-point solution parameters and variance matrix of N are:

[0080] After performing the Z-transform on it, the LAMBDA minimum variance method is used to search for the ambiguity, that is, it satisfies the following formula:

[0081] wherein, ， ， ， ， ， ， ; wherein, is the floating-point solution of the overall ambiguity after Z-transform and correction; is the floating-point solution of the WL ambiguity after Z-transform and correction, is the floating-point solution of the NL ambiguity after Z-transform; is the floating-point solution of the WL ambiguity that has undergone Z-transform but not been corrected, is the floating-point solution of the WL ambiguity after Z-transform; is the floating-point solution of the NL ambiguity that has undergone Z-transform but not been corrected, is the floating-point solution of the NL ambiguity after Z-transform; is the variance-covariance matrix corresponding to ; is and the variance-covariance matrix between; is the variance-covariance matrix corresponding to ; is the floating-point solution of the NL ambiguity after correction, is the variance-covariance matrix corresponding to ;

[0082] First, fix the WL ambiguity. Determine whether the WL ambiguity is fixed according to the Ratio-test value. When it is greater than 4, it is fixed.

[0083] At this time, if the WL ambiguity is not fixed, the filtering floating-point solution of this epoch will be and ; conversely, the Z-transform value of the WL integer ambiguity can be obtained , substitute it into the above formula, and the Z-transform value of the corrected narrow-lane ambiguity can be obtained , determine whether the NL ambiguity is fixed according to the Ratio-test value. When it is greater than 3, it is fixed.

[0084] If both the WL and NL ambiguities are fixed, the fixed NL ambiguity can be back-substituted into the equation to obtain the fixed solution of the non-ambiguity parameters. The formula is as follows:

[0085] , where is the corrected non-ambiguity parameter, is the non-ambiguity parameter without correction is the variance-covariance matrix between and the NL floating solution vector is 's derivative, is the floating solution of the fixed NL ambiguity; is the floating solution of the NL ambiguity. is the variance-covariance matrix corresponding to the non-ambiguity parameter , is the corrected variance-covariance matrix, is 's transpose matrix.

[0086] Conversely, if the WL ambiguity is fixed and the NL ambiguity is not fixed, the fixed solution of the non-ambiguity parameter corrected by the fixed solution of the WL ambiguity is output for this epoch. The formula is as follows:

[0087] Through this step-by-step fixing method, the technical solution of the present application can establish a close connection between each frequency point, make full use of multi-frequency observation information, and thus significantly improve the success rate and accuracy of ambiguity fixing while reducing the computational complexity.

[0088] Please refer to Figure 2 , an ambiguity resolution device that operates according to the steps of any of the above methods. The device includes: Construction module 201: used to establish non-combined BDS triple-frequency pseudorange equations and carrier phase observation equations; Calculation module 202: used to perform sequential least squares solution on the BDS triple-frequency pseudorange equations and carrier phase observation equations to obtain the floating solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; First fixing module 203: used to obtain the number of satellites participating in the solution for the current epoch. If the number of satellites is less than 5, LAMBDA is used for ambiguity fixing according to the floating solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; Second fixing module 204: used to perform ambiguity fixing step by step using EWL-WL-NL according to the floating solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix if the number of satellites is greater than or equal to 5.

[0089] Among them, in practical applications, the construction module 201 can establish non-combined BDS triple-frequency pseudorange equations and carrier phase observation equations in various ways. For example, the original observation equations can be established first, then the single-difference observation equation set between stations can be constructed, and finally the double-difference ambiguity can be constructed by selecting a reference satellite. The difference between this method and the traditional double-difference model is that on the basis of solving the non-integer characteristics of the ambiguity, the original information is further retained, and the selection of the reference satellite eliminates the receiver-end error of the ambiguity and makes it recover the integer characteristics.

[0090] When the calculation module 202 performs sequential least squares solution, different weight strategies can be adopted. For example, the weights of the observations can be allocated according to the satellite elevation angle or signal-to-noise ratio to improve the accuracy and reliability of the solution. In addition, robust estimation methods can also be considered to deal with possible abnormal observations.

[0091] When the first fixing module 203 adopts the LAMBDA method, different search strategies and verification criteria can be set. For example, improved LAMBDA methods such as MLAMBDA or RLAMBDA can be adopted to improve the search efficiency. In terms of verification criteria, in addition to the commonly used Ratio-test, methods such as F-test or projection statistics can also be considered to improve the reliability of fixing.

[0092] When the second fixing module 204 performs step-by-step fixing of EWL-WL-NL, different combination methods and fixing strategies can be adopted. For example, in the EWL fixing stage, different frequency point combinations can be selected to obtain the optimal EWL observations. In the WL and NL fixing stages, a partial fixing strategy can be adopted, that is, some ambiguities with high reliability are fixed first, and then the remaining ambiguities are fixed to improve the overall fixing success rate.

[0093] Through this modular and flexible design, the ambiguity resolution device can adapt to different observation environments and application requirements, while ensuring high precision, significantly improving the calculation efficiency and the reliability of ambiguity fixing, and solving the problems of large calculation burden, low reliability of ambiguity fixing and high misjudgment probability in the prior art.

[0094] Figure 3A 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 execute the method in any optional implementation manner of the above embodiments to achieve the following functions: establishing a non-combined BDS triple-frequency pseudorange equation and a carrier phase observation equation; performing sequential least squares solution on the BDS triple-frequency pseudorange equation and the carrier phase observation equation to obtain a floating-point solution matrix of parameters to be estimated and its corresponding variance-covariance matrix; obtaining the number of satellites participating in the solution at the current epoch. If the number of satellites is less than 5, perform ambiguity fixing using LAMBDA according to the floating-point solution matrix of parameters to be estimated and its corresponding variance-covariance matrix; if the number of satellites is greater than or equal to 5, perform ambiguity fixing step by step using EWL-WL-NL according to the floating-point solution matrix of parameters to be estimated and its corresponding variance-covariance matrix.

[0095] 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, it executes the method in any optional implementation manner of the above embodiments to achieve the following functions: establishing a non-combined BDS triple-frequency pseudorange equation and a carrier phase observation equation; performing sequential least squares solution on the BDS triple-frequency pseudorange equation and the carrier phase observation equation to obtain a floating-point solution matrix of parameters to be estimated and its corresponding variance-covariance matrix; obtaining the number of satellites participating in the solution at the current epoch. If the number of satellites is less than 5, perform ambiguity fixing using LAMBDA according to the floating-point solution matrix of parameters to be estimated and its corresponding variance-covariance matrix; if the number of satellites is greater than or equal to 5, perform ambiguity fixing step by step using EWL-WL-NL according to the floating-point solution matrix of parameters to be estimated and its corresponding variance-covariance matrix.

[0096] 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 (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disk.

[0097] 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 only 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 is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0098] 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.

[0099] Furthermore, in each embodiment of the present application, the functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0100] In this article, 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 such actual relationship or order between these entities or operations.

[0101] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for ambiguity resolution, characterized in that, The method includes the steps of: S1: Establish non-combined BDS triple-frequency pseudorange equations and carrier phase observation equations; S2: Perform sequential least squares solution on the BDS triple-frequency pseudorange equations and the carrier phase observation equations to obtain the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; S3: Obtain the number of satellites participating in the solution at the current epoch. If the number of satellites is less than 5, perform ambiguity fixing using LAMBDA based on the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; S4: If the number of satellites is greater than or equal to 5, perform ambiguity fixing step by step using EWL-WL-NL based on the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix.

2. The ambiguity resolution method according to claim 1, wherein, Step S1 includes: S11: Establish non-combined original BDS triple-frequency pseudorange equations and original carrier phase observation equations; S12: Based on the original BDS triple-frequency pseudorange equations and the original carrier phase observation equations, establish the single-difference observation equation set between stations for the satellites between two different stations; S13: Obtain the reference satellite, construct the double-difference ambiguity between the satellite and the reference satellite based on the single-difference observation equation set between stations, and reconstruct the final BDS triple-frequency pseudorange equations and the carrier phase observation equations based on the double-difference ambiguity.

3. The ambiguity resolution method according to claim 2, wherein In step S2, the floating-point solution matrix of the parameter to be estimated is: , denotes the floating-point solution of the ambiguity, denotes the non-ambiguity parameters, including but not limited to: coordinates XYZ, ionospheric delay, tropospheric delay, receiver clock offset, pseudorange hardware delay, and carrier phase hardware delay; The variance-covariance matrix is as follows: , where is the variance-covariance matrix, including four sub-matrices , , and . is the variance-covariance matrix of the float solution of the ambiguity . is the variance-covariance matrix between the float solution of the ambiguity and the non-ambiguity parameter ; is the transpose matrix of , is the variance-covariance matrix of the non-ambiguity parameter .

4. A method for ambiguity resolution according to claim 3, characterized in that, In step S3, the formula for performing fixation using LAMBDA is: , where is the optimal integer ambiguity solution vector obtained by searching with the LAMBDA method; is a candidate integer ambiguity solution vector in the search space and belongs to the integer set Z; is the float ambiguity solution vector, obtained by performing a linear transformation on the float ambiguity solution i.e., , where is a design matrix used to transform the float ambiguity solution to a new coordinate system; is the variance-covariance matrix after transformation, which is used to describe the accuracy information of the float solution vector of the ambiguity , and the calculation formula is: ; Step S3 includes: S31: After performing a search using LAMBDA, take the Ratio-test value greater than 3 as the basis for successful fixation. After successful fixation, conversely correct the non-ambiguity parameters. The specific formula is as follows: , where is the variance-covariance matrix of the corrected ambiguity parameters , is the corrected ambiguity parameter is the floating-point solution of the ambiguity after being fixed by LAMBDA; is the uncorrected ambiguity parameter and the variance-covariance matrix between the floating-point solution vector of the ambiguity ; is the transposed matrix of 5. A method for ambiguity resolution according to claim 1, characterized in that, Step S4 includes: S41: If the number of satellites is greater than or equal to 5, screen the ambiguity parameters with variances less than the preset variance according to the variance-covariance matrix; S42: Combine the ambiguity parameters of the B2 frequency band and the B3 frequency band in the optimal combination method to obtain the floating-point solution of the EWL ambiguity and its corresponding variance-covariance matrix, and perform EWL ambiguity fixation by directly rounding; S43: After EWL ambiguity fixation, correct the accuracy of the ambiguity parameters of the B3 frequency band according to the floating-point solution of the EWL ambiguity and its corresponding variance-covariance matrix; S44: Combine the corrected ambiguity parameters of the B3 frequency band and the ambiguity parameters of the B1 frequency band in the optimal combination method to obtain the floating-point solutions of the WL ambiguity, the NL ambiguity, and their respective corresponding variance-covariance matrices; S45: Perform WL ambiguity fixation according to the floating-point solution of the WL ambiguity and its corresponding variance-covariance matrix; after obtaining the WL fixed solution, correct the floating-point solution of the NL, and perform NL ambiguity fixation according to the corrected floating-point solution of the NL ambiguity and its corresponding variance-covariance matrix.

6. The ambiguity resolution method according to claim 5, characterized in that In step S42, the ambiguity parameter of B2 frequency point is , the ambiguity parameter of B3 frequency point is , the floating-point solution of EWL ambiguity is , the specific calculation formula for fixing the EWL ambiguity is: ​ , where is the float solution of the ambiguity, is the transformation matrix, n represents the satellite number, represents the float solution of the ambiguity of satellite No. 1 at B1 frequency point, represents the float solution of the ambiguity of satellite No. n at B1 frequency point; represents the float solution of the ambiguity of satellite No. 1 at B2 frequency point, represents the float solution of the ambiguity of satellite No. n at B2 frequency point; represents the float solution of the ambiguity of satellite No. 1 at B3 frequency point, represents the float solution of the ambiguity of satellite No. n at B3 frequency point; The corresponding variance-covariance matrix is as follows: , where is the float solution of the ambiguity variance-covariance matrix, is variance-covariance matrix, is transpose matrix of 7. A method for ambiguity resolution according to claim 6, characterized in that, In step S43, the formula for correcting the accuracy of the ambiguity parameters of the B3 frequency band according to the floating-point solution of the EWL ambiguity and its corresponding variance-covariance matrix is: , , ; where, is the variance-covariance matrix between the ambiguity parameter of B3 frequency point and the EWL ambiguity, is the transpose matrix of is the floating-point solution of the ambiguity after correction of B3 frequency point, is the variance-covariance matrix corresponding to after correction of B3 frequency point.

8. An ambiguity resolution device that executes the steps of the method according to any one of claims 1-7, characterized in that The device includes: A construction module: used to establish non-combined BDS triple-frequency pseudorange equations and carrier phase observation equations; Computing module: It is used to perform sequential least squares solution on the BDS triple-frequency pseudorange equation and the carrier phase observation equation to obtain the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; First fixing module: It is used to obtain the number of satellites participating in the solution at the current epoch. If the number of satellites is less than 5, LAMBDA is used for ambiguity fixing according to the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix; Second fixing module: It is used to, if the number of satellites is greater than or equal to 5, perform ambiguity fixing step by step using EWL-WL-NL according to the floating-point solution matrix of the parameters to be estimated and its corresponding variance-covariance matrix.

9. An electronic device, characterized in that, It includes a processor and a memory. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in any one of claims 1-7 are run.

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

Images