Precise point positioning ambiguity fixed optimization method, device, equipment and medium

Abstract

The invention discloses a precise single-point positioning ambiguity fixed optimization method, device and equipment and a medium, and the method comprises the steps: obtaining original observation data of a mobile station, and carrying out the non-difference data preprocessing of the original observation data; performing PPP floating point solution calculation on the processed observation data to obtain a non-difference floating point solution of the current epoch of the mobile station; performing wide item ambiguity fixation on the non-difference floating point solution according to the MW combination observation value to obtain the final wide item ambiguity of the non-difference floating point solution; and performing narrow item ambiguity fixation on the non-difference floating point solution according to the final wide item ambiguity to obtain a PPP fixed solution of the non-difference floating point solution. The problems that an existing non-combined PPP-AR technology is low in ambiguity search efficiency, high in terminal load and poor in fixed solution continuity and stability in a complex scene can be solved.

Description

Technical Field

[0001] This invention relates to the field of satellite positioning technology, and in particular to a method, apparatus, equipment, and medium for fixing and optimizing ambiguity in precise single-point positioning. Background Technology

[0002] Precise Point Positioning Ambiguity Fixing (PPP-AR) is a key technology developed based on Precise Point Positioning (PPP). Its core principle is to fix the integer ambiguity of the carrier phase using high-precision products such as precise satellite orbits, precise satellite clock errors, satellite pseudorange, and carrier phase fractional deviation, thereby shortening convergence time and improving positioning accuracy. Existing non-combined PPP-AR technology is based on non-combined observation equations of pseudorange and carrier (with corrected satellite pseudorange and phase deviation). These equations are expanded at the rover's initial position using Taylor series to construct a non-combined PPP mathematical model. The final fixed solution is obtained by solving the non-combined PPP mathematical model through floating-point solution calculation, receiver-side phase deviation processing, ambiguity fixing, and fixed solution position calculation.

[0003] While existing non-combined PPP-AR technology can achieve high-precision positioning, its ambiguity search efficiency is low and the terminal computing power load is too high, making it difficult to meet the performance requirements of onboard terminal devices in practical applications. Furthermore, the PPP-AR algorithm has extremely high requirements for observation conditions, and in complex scenarios, it is prone to problems such as unstable satellite signal tracking and frequent cycle slips, which cannot maintain the continuity and stability of fixed solutions and affect the reliability of positioning results. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for fixing and optimizing ambiguity in precise single-point positioning, which can solve the problems of low ambiguity search efficiency, high terminal load, and poor continuity and stability of fixed solutions in complex scenarios in existing non-combined PPP-AR technology.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for fixing and optimizing precise single-point positioning ambiguity, comprising: Obtain the raw observation data from the rover station and perform undifferentiated data preprocessing on the raw observation data; The PPP floating-point solution is used to solve the processed observation data to obtain the non-difference floating-point solution for the current epoch of the rover station; The wide term ambiguity of the unequal floating-point solution is fixed based on the MW combined observations, and the final wide term ambiguity of the unequal floating-point solution is obtained. By fixing the narrow term ambiguity of the unequal floating-point solution based on the final wide term ambiguity, the PPP fixed solution of the unequal floating-point solution is obtained.

[0006] As an improvement to the above scheme, the step of acquiring the raw observation data from the rover station and performing non-differential data preprocessing on the raw observation data includes: Acquire the raw observation data from the rover station; the raw observation data includes raw unequal pseudorange observations and raw carrier phase observations; The PVT algorithm is used to process the original undifferentiated pseudorange observations, and pseudorange observations containing gross errors and anomalous satellites are removed to obtain the initial position, velocity, receiver clock error, effective pseudorange observations and satellite list of the rover station; External precision products and preset error models are used to correct the effective pseudorange observations and corresponding carrier phase observations, resulting in corrected observations and high-precision satellite positions; A cycle slip detection algorithm is used to detect cycle slips in the carrier phase observations in the corrected observations. The associated information of satellites with cycle slips is reset and satellite cycle slips are marked. The results of error correction observations, satellite cycle slip marks and reset associated information are obtained.

[0007] As an improvement to the above scheme, the step of performing PPP floating-point solution calculation on the processed observation data to obtain the non-difference floating-point solution for the current epoch of the mobile station includes: Based on the initial position of the rover station, the observations after error correction, and the high-precision satellite position, the non-difference, non-combined observation equation matrix of the rover station is constructed using a non-combined PPP mathematical model. Extended Kalman filtering estimation is performed on the non-differenced, non-combined observation equation matrix to obtain the non-differenced floating-point solution of the current epoch of the mobile station. The non-differenced floating-point solution includes floating-point solution parameters and floating-point solution covariance matrix.

[0008] As an improvement to the above scheme, the step of fixing the broad term ambiguity of the unequal floating-point solution based on the MW combined observations to obtain the final broad term ambiguity of the unequal floating-point solution includes: Based on the non-difference, non-combined observation equation matrix, MW combined observations are constructed using narrow-term pseudorange combination and wide-term carrier combination. The preliminary wide-term fixed ambiguity of the MW combined observations is obtained by fixing the wide-term ambiguity of the MW combined observations. Based on the initial wide term fixed ambiguity, the non-difference floating-point solution is subjected to ultra-wide term / wide term ambiguity fixation to obtain the final wide term ambiguity of the non-difference floating-point solution.

[0009] As an improvement to the above scheme, the step of fixing the broad term ambiguity of the MW combined observations to obtain the preliminary broad term fixed ambiguity of the MW combined observations includes: The mean of the MW combined observations is calculated by averaging over multiple epochs, and the non-difference wide term ambiguity of the MW combined observations is obtained. Reference stars are selected for each system and frequency, and an inter-satellite single-difference transformation matrix is ​​constructed to convert non-difference wide-term ambiguities into inter-satellite single-difference wide-term ambiguities. The inter-satellite single-difference wide-term ambiguities are rounded to obtain a fixed number of ambiguities for different reference stars. Select the reference star with the most fixed ambiguities as the target reference star to obtain the current epoch wide-term fixed ambiguity group of the MW combined observation; Compare the current epoch wide-term ambiguity fixed group with the historical wide-term ambiguity fixed group to classify the MW combined observations into common group, non-common group, historical group, and new fixed group; By removing non-common groups from the MW combined observations and checking the coordinate consistency between the historical groups and the new fixed groups of the MW combined observations, the preliminary wide term fixed ambiguity of the MW combined observations is obtained; wherein, the preliminary wide term fixed ambiguity includes the final wide term ambiguity and the corresponding covariance matrix of the MW combined observations.

[0010] As an improvement to the above scheme, the step of fixing the super-wide term / wide term ambiguity of the unequal floating-point solution based on the initial wide term fixed ambiguity to obtain the final wide term ambiguity of the unequal floating-point solution includes: Based on factors such as the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a wide-term reference satellite is selected for each system and frequency point to construct an inter-satellite single-difference ultra-wide term / wide term transformation matrix, which converts the non-difference floating-point solution into an inter-satellite single-difference ultra-wide term / wide term solution. Extract the ultrawide term ambiguity from the inter-satellite single-difference ultrawide term / wide term solution, and use the Lambda algorithm to search for the fixed ultrawide term ambiguity; Substitute the ultrawide ambiguity back into the non-difference floating-point solution equation to obtain the wide ambiguity floating-point solution, and use the Lambda algorithm to search for the fixed wide ambiguity. Based on the conversion relationship between frequency points, the wide term ambiguity of each combination frequency point is calculated through ultra-wide term ambiguity and wide term ambiguity to obtain the wide term ambiguity group of the non-difference floating point solution; The wide term ambiguity group and the historical wide term ambiguity group are compared and divided into the wide term common group, the wide term non-common group, the wide term historical group, and the wide term new fixed group; The non-difference floating-point solution equation is updated based on the common group of the broad term ambiguities to obtain the coordinates of the first broad term fixed solution. Remove the non-common group of the broad terms ambiguity, add the broad terms ambiguity of the broad terms history group and the new fixed group of the broad terms to the broad terms common group of the broad terms ambiguity in turn, and update the non-difference floating-point solution equation to obtain the coordinates of the second broad term fixed solution; Verify the consistency between the coordinates of the first wide term fixed solution and the coordinates of the second wide term fixed solution. If they are inconsistent, remove the newly added wide term ambiguity to obtain the target wide term ambiguity of the non-difference floating-point solution. Verify the consistency of the coordinates of the initial wide term fixed fuzziness and the target wide term fuzziness. If they are inconsistent, remove the corresponding wide term fuzziness from the target wide term fuzziness to obtain the final wide term fuzziness of the non-difference floating-point solution.

[0011] As an improvement to the above scheme, the step of fixing the narrow term ambiguity of the unequal floating-point solution based on the final wide term ambiguity to obtain the PPP fixed solution of the unequal floating-point solution includes: Based on factors such as the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a narrow-term reference star is selected for each system and frequency point. If the narrow-term reference star is inconsistent with the wide-term reference star, the wide-term reference star of the final wide-term ambiguity is converted into a reference star through the inter-satellite single-difference conversion formula to obtain a new final wide-term ambiguity. Construct an inter-satellite single-difference narrow-term transformation matrix based on the narrow-term reference star, and obtain the first frequency point narrow-term floating-point solution based on the non-difference floating-point solution and the new final wide-term ambiguity. Based on the floating-point solution of the first frequency narrow term, the Lambda algorithm is used to search for the ambiguity of the fixed first frequency narrow term. Based on the frequency relationship between the new final wide-term ambiguity and the narrow-term ambiguity at the first frequency point, the narrow-term ambiguity at other frequency points is calculated to obtain the narrow-term ambiguity set of the non-difference floating-point solution. The narrow term ambiguity group is compared with the historical narrow term ambiguity group to divide the narrow term common group, narrow term non-common group, narrow term historical group and narrow term new fixed group; Eliminate the narrow term non-common group; update the non-difference floating-point solution equation based on the narrow term common group to obtain the first narrow term fixed coordinate of the narrow term common group; The narrow term history group and the narrow term new fixed group are added in sequence. The non-difference floating-point solution equation is updated to obtain the second narrow term fixed coordinates of the narrow term history group and the narrow term new fixed group. The consistency between the first narrow term fixed coordinates and the second narrow term fixed coordinates is checked. Substitute the narrow-term ambiguity that has passed the check back into the non-difference floating-point solution equation to calculate the final fixed solution of the non-difference floating-point solution; A virtual observation equation is constructed using the final broad term ambiguity. Kalman filtering is then applied to the final fixed solution of the unequal floating-point solution to obtain the PPP fixed solution of the unequal floating-point solution.

[0012] To achieve the above objectives, embodiments of the present invention provide a precise single-point positioning ambiguity fixing and optimization device, comprising: The observation data acquisition module is used to acquire the raw observation data of the rover station and perform non-differential data preprocessing on the raw observation data; The non-differenced floating-point solution module is used to perform PPP floating-point solution on the processed observation data to obtain the non-differenced floating-point solution of the rover station for the current epoch. The wide term result fixing module is used to fix the wide term ambiguity of the unequal floating-point solution based on the MW combined observations, and obtain the final wide term ambiguity of the unequal floating-point solution; The narrow term result fixing module is used to fix the narrow term ambiguity of the non-differenced floating-point solution based on the final wide term ambiguity, so as to obtain the PPP fixed solution of the non-differenced floating-point solution.

[0013] To achieve the above objectives, this invention provides a precise single-point positioning ambiguity fixing and optimization device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-mentioned precise single-point positioning ambiguity fixing and optimization method.

[0014] To achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described precise single-point positioning ambiguity fixing optimization method.

[0015] To achieve the above objectives, embodiments of the present invention also provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the above-described precise single-point positioning ambiguity fixing optimization method.

[0016] Compared with existing technologies, the present invention discloses a precise single-point positioning ambiguity fixing optimization method, apparatus, equipment, and medium. This method acquires raw observation data from a mobile station and performs non-differential data preprocessing on the raw observation data. It then calculates the non-differential floating-point solution for the current epoch of the mobile station using PPP floating-point solutions. Based on the combined MW observation values, it fixes the wide-term ambiguity of the non-differential floating-point solution to obtain the final wide-term ambiguity. Finally, it fixes the narrow-term ambiguity of the non-differential floating-point solution based on the final wide-term ambiguity to obtain the PPP fixed solution of the non-differential floating-point solution. This method can solve the problems of low ambiguity search efficiency, high terminal load, and poor continuity and stability of fixed solutions in complex scenarios that exist in existing non-combinatorial PPP-AR technologies. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a precise single-point positioning ambiguity fixing optimization method provided in an embodiment of the present invention; Figure 2 This is a flowchart of an algorithm for fixed fuzziness search provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a precision single-point positioning ambiguity fixing and optimization device provided in an embodiment of the present invention; Figure 4 This is a structural block diagram of a precision single-point positioning ambiguity fixing and optimization device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that the terms "comprising" and "specific" in this invention, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0020] Please see Figure 1 , Figure 1 This is a flowchart illustrating a precise single-point positioning ambiguity fixing and optimization method provided in an embodiment of the present invention. The precise single-point positioning ambiguity fixing and optimization method includes: S1, acquire the raw observation data from the rover station and perform non-differential data preprocessing on the raw observation data; S2, perform PPP floating-point solution calculation on the processed observation data to obtain the non-difference floating-point solution of the current epoch of the mobile station; S3, fix the wide term ambiguity of the unequal floating-point solution based on the MW combined observations to obtain the final wide term ambiguity of the unequal floating-point solution; S4. Based on the final wide-term ambiguity, the narrow-term ambiguity of the non-difference floating-point solution is fixed to obtain the PPP fixed solution of the non-difference floating-point solution.

[0021] For example, such as Figure 2 As shown, Figure 2 This is a flowchart of an algorithm for fixed fuzziness search provided in an embodiment of the present invention. Figure 2The document demonstrates the complete workflow from data preprocessing / floating-point solution to ambiguity fixing. PVT Solution: Single-point positioning is performed on the raw pseudorange observations to obtain the rover's initial probabilistic position, velocity, and receiver clock bias, while simultaneously marking / removing gross errors. Model Correction: By combining precise satellite orbit, clock bias, and other products with ionospheric and tropospheric error models, pseudorange and carrier phase observations are corrected to eliminate systematic errors. Cycle Slip Detection: Cycle slips (integer cycle jumps caused by signal interruption) are detected in the carrier phase. If a cycle slip is detected, the relevant ambiguity information is reset to prevent abnormal propagation. Floating-Point Solution: Based on the corrected observations, extended Kalman filtering is used to estimate parameters such as rover position, clock bias, and non-differential floating-point ambiguities, providing a floating-point solution basis for subsequent ambiguity fixing. MW Combined Observations: MW combinations are constructed using multi-frequency carrier phase and pseudorange; their broad wavelength characteristics can initially constrain the ambiguity range. Undifferenced floating-point ambiguity and covariance matrix: Extract the floating-point values ​​of the undifferenced ambiguity and their covariance matrix from the floating-point solution to provide data support for subsequent inter-satellite single-difference conversion and ambiguity search.

[0022] The wide-term ambiguity fixing branch focuses on fixing ultra-wide terms and wide-term ambiguities. This is achieved through multi-epoch fusion, inter-satellite single-difference conversion, Lambda search, and historical fusion: MW combination gross error elimination and verification: Statistical analysis of multi-epoch MW combination values ​​is performed to eliminate gross errors and ensure the reliability of the initial wide-term ambiguity. Reference satellite selection and transformation matrix construction: Satellites with high elevation angles and stable signals are selected as reference satellites for each system and frequency point. An inter-satellite single-difference transformation matrix is ​​constructed to convert non-difference floating-point ambiguities into inter-satellite single-difference ambiguities, eliminating residual errors at the receiver end. Ultra-wide / wide-term ambiguity search and fixing: The Lambda algorithm is used to perform an integer search on the transformed ultra-wide and wide-term ambiguity floating-point solutions, and fusion verification is performed in conjunction with historical ambiguity groups (judging the consistency between the current fixing group and the historical group, and eliminating outliers). MW wide-term historical support: If there is ambiguity in the current wide-term fixing, historically successfully fixed wide-term ambiguity groups are used for assistance to improve fixing continuity.

[0023] The narrow-term fuzziness fixing branch, under the wide-term fuzziness constraint, completes the fixing of the narrow-term fuzziness. The process is similar to that of the wide-term fuzziness but more refined: Reference Star Conversion and Floating-Point Solution Update: If the narrow-term reference star differs from the wide-term reference star, a conversion matrix is ​​used to unify the reference star and update the covariance matrix of the non-differenced floating-point solution. Narrow-Term Ambiguity Search and Fixation: The Lambda algorithm is used to search for integer solutions for the converted narrow-term ambiguity floating-point solution, while historical narrow-term ambiguity groups are combined for fusion verification. Constraint Equations and PPP Fixed Solution Generation: If the narrow-term fixation is successful, the fixed ambiguities are substituted back into the observation equations to solve the final PPP fixed solution; if it fails, it reverts to the PPP floating-point solution to ensure positioning continuity.

[0024] It is worth noting that, based on the non-combined observation equations of pseudorange and carrier wave (with satellite pseudorange and phase deviation corrected), the initial position of the rover is determined using the Taylor formula. Expanding on this point, we can obtain the non-combinatorial PPP mathematical model as follows: , In the formula, For frequency Undifferenced pseudorange observations; For frequency Undifferenced pseudorange observations; For frequency Undifferentiated carrier phase observations; For frequency Undifferentiated carrier phase observations; and For frequency marking, For satellite identification, For mobile site identification, and For frequency and The corresponding carrier wavelength; The frequency correlation coefficient, , and Frequency and The frequency value; To determine the initial position of the mobile station Calculated satellite-to-Earth distance; It is a satellite The direction cosine of the mobile station; Here are the receiver position parameters to be estimated; For receiver clock bias; For satellite clock bias; For satellite The corresponding tropospheric projection function; For zenith moisture delay; For satellite In frequency Ionospheric delay at the location; For mobile stations In frequency and Receiver endcode deviation between; and Mobile stations In frequency and Phase deviation at the receiver end; and Satellites In frequency and Carrier phase integer ambiguity at the location.

[0025] make , , Simplifying the above equations yields the simplified non-combinatorial PPP mathematical model (simplified non-combinatorial PPP observation equations): , In the formula, For the integrated satellite ionospheric delay parameters; For the integrated satellite In frequency Integer ambiguity at the location; For the integrated satellite In frequency Integer ambiguity at the location; The solution process of PPP-AR technology includes four parts: (1) Floating-point solution calculation: If we consider the case of S navigation systems, N frequencies, and M satellites, we can obtain a total of 2*N*M observation equations according to the above formula. The parameters to be estimated include 3 coordinate parameters, S receiver clock bias parameters, 1 zenith tropospheric parameter, M ionospheric parameters, and N*M ambiguity parameters.

[0026] Based on the principle of indirect adjustment, the above observation equations can be written in the form of a matrix as follows: , In the formula, To observe the residual vector; The design matrix represents the partial derivative matrix of the observation with respect to each parameter to be estimated, reflecting the linear relationship between each parameter to be estimated and the observation. Let be the vector of parameters to be estimated. , Location of the receiver; for Receiver clock bias of a navigation system; for Ionospheric delay of a satellite; The overall ambiguity for each satellite and each frequency; Let be the observation vector, representing the set of differential pseudorange and carrier phase observations; When the number of observations satisfies 2*N*M≥3+1+S+M+N*M, the extended Kalman filter algorithm can be used to estimate all the parameters to be estimated. Its mathematical model includes two parts: state update and measurement update. , , In the formula, and Let be the state vector and its covariance matrix of the previous epoch; This is the predicted value for the current epoch; Update the state matrix; Update the noise array for the state; For the new information vector; The noise matrix of the observation vector; The specific solution process of the extended Kalman filter algorithm is as follows: , , , , , In the formula, This is the covariance matrix predicted for the current epoch; Update the state matrix The transpose of the matrix; For designing a matrix The transpose of the matrix; The new information covariance matrix; and Let the state vector and its covariance matrix be the state vector of the current epoch. This is the gain matrix; It is an identity matrix.

[0027] (2) Phase deviation processing at the receiver end: According to the observation equation, due to the influence of pseudorange and phase deviation at the receiver end, the floating-point ambiguity obtained by Kalman filtering is... It lacks integer cycle characteristics and cannot be directly used for integer cycle locking. It can be eliminated by using inter-satellite single difference.

[0028] Reference satellites are selected for each system and frequency point to construct ambiguity transformation matrices. Convert the non-difference ambiguity to inter-satellite single-difference ambiguity using the following formula: , In the formula, Deambiguity for inter-satellite single-difference floating-point calculations; The covariance matrix for inter-satellite single-difference floating-point ambiguity resolution; Let be the covariance matrix of the non-differenced comprehensive fuzziness; Ambiguity transformation matrix The transpose of the matrix; (3) Fixed ambiguity Based on the inter-satellite single-difference floating-point deambiguity obtained in the second step and its covariance Ambiguity search can be performed using the reduced correlation least squares method (Lambda / MLambda).

[0029] , In the formula, Solve the integer ambiguity of the fixed inter-satellite single-difference floating-point function; This is a candidate integer ambiguity vector; It is a set of integers, representing the range of ambiguity values, which are integers in physical terms; (4) Calculation of fixed solution location: When the whole week blur Once successfully locked, the fixed inter-satellite single-difference integer ambiguity is substituted back into the normal equations, which can directly update the position parameters and the remaining floating-point solution parameters.

[0030] , , , In the formula, is the covariance submatrix of the non-fuzziness parameter; The covariance submatrix of the ambiguity parameters; Let be the cross-covariance submatrix of the unambiguous and the ambiguous. for The transpose of the matrix; The optimal estimate of the non-ambiguity parameters after fixing the ambiguity; This is the covariance matrix of the non-ambiguity parameters after fixing the ambiguity.

[0031] The above solution process is the solution method of the PPP-AR algorithm. Since the number of observations (number of satellites, number of frequencies) is positively correlated with the positioning accuracy, the terminal algorithm will try to select more observations to participate in the calculation process within a certain range. Therefore, this solution method can also be called the full solution method.

[0032] Specifically, step S1 includes: S11, acquire the raw observation data of the rover station; wherein, the raw observation data includes the raw unequal pseudorange observations and the raw carrier phase observations; S12, the PVT algorithm is used to process the original undifferentiated pseudorange observations, and pseudorange observations containing gross errors and abnormal satellites are deleted to obtain the initial position, velocity, receiver clock error, effective pseudorange observations and satellite list of the rover station; S13 uses external precision products and a preset error model to correct the effective pseudorange observations and the corresponding carrier phase observations, and obtains the corrected observations and high-precision satellite positions. S14. The cycle slip detection algorithm is used to detect cycle slips in the carrier phase observations in the corrected observations. The associated information of satellites with cycle slips is reset and satellite cycle slips are marked. The results of error correction observations, satellite cycle slip marks and associated information reset are obtained.

[0033] For example, the pseudorange single-point positioning (PVT) algorithm is used to process the unequal pseudorange observations to calculate the probabilistic position coordinates, velocity, and receiver clock bias of the rover station. Simultaneously, pseudorange observations and satellites with gross errors are marked or deleted. Using precise satellite orbit, satellite clock bias, pseudorange, and phase deviation products in the SSR state domain, combined with error models for the ionosphere, troposphere, phase entanglement, and tides, high-precision satellite position and satellite clock bias are calculated to correct the unequal pseudorange and carrier phase. The model-corrected unequal non-combined observations are used for carrier phase cycle slip detection and marking. If a cycle slip is detected, the MW information, ultrawide term, wide term, and narrow term ambiguity information related to the satellite need to be reset.

[0034] Specifically, step S2 includes: S21. Based on the initial position of the rover station, the observations after error correction, and the high-precision satellite position, the non-combined PPP mathematical model is used to construct the non-difference non-combined observation equation matrix of the rover station. S22, perform extended Kalman filter estimation on the non-difference, non-combined observation equation matrix to obtain the non-difference floating-point solution of the current epoch of the mobile station, where the non-difference floating-point solution includes floating-point solution parameters and floating-point solution covariance matrix.

[0035] For example, the initial position of the mobile station is obtained by solving the PVT using the Taylor formula based on the model-corrected, non-differential, non-combined observations. Expanding at this point and integrating the parameters, we obtain the observation equation for the non-difference, non-combination PPP: , In the formula, For frequency Undifferenced pseudorange observations; For frequency Undifferenced pseudorange observations; For frequency Undifferentiated carrier phase observations; For frequency Undifferentiated carrier phase observations; and These are frequency markers, For satellite identification, For mobile station identification; To determine the initial position of the mobile station Calculated satellite-to-Earth distance; For satellite The direction cosine of the mobile station; Here are the receiver position parameters to be estimated; For receiver clock bias; For satellite clock bias; For satellite The corresponding tropospheric projection function; For zenith moisture delay; For including receiver pseudorange Ionospheric delay parameters (integrated satellite) Ionospheric delay parameters); and These are integer ambiguity parameters that include receiver pseudorange and phase bias, respectively. For the integrated satellite In frequency Integer ambiguity at the location; For the integrated satellite In frequency Integer ambiguity at the location; If we consider the case of S navigation systems, N frequencies, and M satellites, according to the principle of indirect adjustment, the above observation equations can be written in the form of the following matrix: , In the formula, To observe the residual vector; The design matrix represents the partial derivative matrix of the observation with respect to each parameter to be estimated, reflecting the linear relationship between each parameter to be estimated and the observation. Let be the vector of parameters to be estimated. , Location of the receiver; for Receiver clock bias of a navigation system; for Ionospheric delay of a satellite; The overall ambiguity for each satellite and each frequency; Let be the observation vector, representing the set of differential pseudorange and carrier phase observations; The parameters to be estimated include: mobile station location. Clock bias of receivers in each system tropospheric zenith moisture content ( ), ionospheric slant delay of each satellite Carrier phase ambiguity for each satellite at each frequency .

[0036] When the number of observations satisfies 2*N*M≥3+1+S+M+N*M, the extended Kalman filter algorithm can be used to estimate all the parameters to be estimated. Its mathematical model includes two parts: state update and measurement update. , , In the formula, and Let be the state vector and its covariance matrix of the previous epoch; This is the predicted value for the current epoch; Update the state matrix; Update the noise array for the state; For the new information vector; The noise matrix of the observation vector; The specific solution process of the extended Kalman filter algorithm is as follows: , , , , , In the formula, This is the covariance matrix predicted for the current epoch; Update the state matrix The transpose of the matrix; For designing a matrix The transpose of the matrix; The new information covariance matrix; and Let the state vector and its covariance matrix be the state vector of the current epoch. This is the gain matrix; It is an identity matrix.

[0037] Specifically, step S3 includes: S31, based on the non-difference non-combined observation equation matrix, construct the MW combined observation value using narrow-term pseudorange combination and wide-term carrier combination; S32, the initial wide-term fixed ambiguity of the MW combined observations is obtained by fixing the wide-term ambiguity of the MW combined observations; S33. Based on the initial wide term fixed ambiguity, the non-difference floating-point solution is subjected to ultra-wide term / wide term ambiguity fixation to obtain the final wide term ambiguity of the non-difference floating-point solution.

[0038] For example, based on the model-corrected non-difference non-combination observation equations, narrow-term pseudorange combination and wide-term carrier combination are used to construct... Combined observations.

[0039] , Broad wavelength of MW combination for: , Observation noise of MW combination for: , In the formula, and Frequency and The frequency value, and Frequency and Corresponding carrier phase observation noise; and Frequency and The corresponding pseudorange observation noise.

[0040] Specifically, step S32 includes: S321, the mean of the MW combined observations is calculated by averaging multiple epochs, and the non-difference wide term ambiguity of the MW combined observations is obtained; S322, select reference stars for each system and frequency, construct an inter-star single-difference transformation matrix, convert non-difference wide-term ambiguity into inter-star single-difference wide-term ambiguity, and round the inter-star single-difference wide-term ambiguity to count the fixed number of ambiguities of different reference stars; S323, Select the reference star with the most fixed ambiguities as the target reference star, and obtain the current epoch wide-term fixed ambiguity group of the MW combination observation; S324 compares the current epoch wide-term ambiguity fixed group with the historical wide-term ambiguity fixed group to classify the MW combined observations into common group, non-common group, historical group, and new fixed group; S325, remove the non-common groups of MW combined observations, check the coordinate consistency between the historical groups and the new fixed groups of MW combined observations, and obtain the preliminary wide term fixed ambiguity of MW combined observations; wherein, the preliminary wide term fixed ambiguity includes the final wide term ambiguity and the corresponding covariance matrix of MW combined observations.

[0041] For example, the MW observation equations only retain broad term ambiguities, which can be accurately obtained by averaging over multiple epochs to get the unequal broad term ambiguities and variances. Reference satellites are selected system-by-system and frequency-by-frequency, and inter-satellite single differences are used to eliminate residual receiver-side errors in the unequal broad term ambiguities, restoring the integer characteristics of the unequal broad term ambiguities. The mean of the unequal broad term ambiguities is rounded down, fixing the floating-point ambiguity values ​​to integers. The rounding criteria are as follows: , in, week, ; In the formula, The integer ambiguity of the non-difference wide term; Parameters for the floating-point solution of the MW combined wide-term ambiguity; This is a rounding function; The rounded residual of the non-differential wide term ambiguity; This is a reliability index for fixed unequal wide term ambiguities, used to determine the reliability of the rounding result of the unequal wide term ambiguity. When the threshold is met, the rounded ambiguity is the correct integer value; This is a reliability calculation function, which represents the calculation of the reliability of the rounded result based on observation noise and rounded residual.

[0042] Using each satellite as a reference, the remaining satellites are statistically analyzed for fixed ambiguities. Then, the satellite with the highest number of fixed non-differential broad term ambiguities is selected as the reference, and the remaining satellites are statistically analyzed for fixed ambiguities again to obtain the current epoch's broad term ambiguity fixed group. Fix the current epoch. With historical wide-item fuzziness fixed group The comparison is performed to divide the data into common groups (equal integer values), non-common groups, historical groups, and new fixed groups. Non-common groups are eliminated, and historical groups and new fixed groups are checked sequentially based on the common groups to obtain the final broad term fuzziness groups (preliminary broad term fixed fuzziness).

[0043] Specifically, step S33 includes: S331, based on the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, select a wide-term reference star for each system and frequency point, construct an inter-satellite single-difference ultra-wide term / wide term transformation matrix, and convert the non-difference floating-point solution into an inter-satellite single-difference ultra-wide term / wide term solution; S332, extract the ultrawide term ambiguity from the inter-satellite single-difference ultrawide term / wide term solution, and use the Lambda algorithm to search for the fixed ultrawide term ambiguity; S333, substitute the ultra-wide ambiguity back into the non-difference floating-point solution equation to obtain the wide ambiguity floating-point solution, and use the Lambda algorithm to search for the fixed wide ambiguity; S334, based on the conversion relationship between frequency points, calculate the wide term ambiguity of each combined frequency point through ultra-wide term ambiguity and wide term ambiguity to obtain the wide term ambiguity group of the non-difference floating point solution; S335 compares the wide term ambiguity group and the historical wide term ambiguity group to divide the wide term common group, wide term non-common group, wide term historical group and wide term new fixed group; S336, update the non-difference floating-point solution equation based on the common group of wide term ambiguities to obtain the coordinates of the first wide term fixed solution; S337, remove the non-common group of the wide terms ambiguity, add the wide terms ambiguity of the wide terms history group and the new fixed group of the wide terms to the common group of the wide terms ambiguity in turn, and update the non-difference floating-point solution equation to obtain the coordinates of the second wide term fixed solution; S338, verify the consistency between the coordinates of the first wide term fixed solution and the coordinates of the second wide term fixed solution. If they are inconsistent, remove the newly added wide term ambiguity and obtain the target wide term ambiguity of the non-difference floating-point solution. S339. Verify the consistency of the coordinates of the initial wide term fixed fuzziness and the target wide term fuzziness. If they are inconsistent, remove the corresponding wide term fuzziness from the target wide term fuzziness to obtain the final wide term fuzziness of the non-difference floating-point solution.

[0044] For example, based on factors such as the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a wide-term reference star is selected for each system and each frequency point. Using the selected reference star, an inter-satellite single-difference ultrawide / wide-term transformation matrix is ​​constructed, and the non-difference, non-combinatorial floating-point solution is transformed... and Convert to single-difference ultrawide / wide term solution and From what was obtained and Extracting floating-point solutions of ultrawide fuzzy terms and Searching for fixed ultrawide fuzziness using the Lambda algorithm .

[0045] , In the formula, The fixed integer ambiguity of the ultrawide term; This is the candidate integer ambiguity vector for the ultrawide term; Parameters for the floating-point solution of the ultrawide ambiguity; Let be the covariance matrix of the ultrawide ambiguity.

[0046] Substituting the fixed ultrawide ambiguity back into the non-difference floating-point solution equation, we obtain and extract the ultrawide ambiguity floating-point solution. and .

[0047] , , , Using the Lambda algorithm to search for fixed-width fuzziness .

[0048]

[0049] In the formula, It is the covariance matrix of the single-difference ultrawide term / wide term; Parameters for the floating-point solution of the single-difference ultrawide term / wide term; Parameters for the floating-point solution of the broad term ambiguity; The covariance matrix of the broad term ambiguity; This refers to the fixed wide-term integer ambiguity (wide-term ambiguity). This is the candidate integer ambiguity vector for the broad term.

[0050] Using ultra-wide term ambiguity and wide term ambiguity, the wide term ambiguity of each combination frequency point is calculated based on the conversion relationship between frequency points, and the wide term ambiguity group is replaced. The current epoch is fixed with a wide-term fuzzy group. With historical ambiguity group The algorithm compares and divides the wide terms into common groups (equal integer values), non-common groups, historical groups, and newly fixed groups. Based on the common groups, the non-differenced floating-point solution equations are updated to obtain the coordinates of the first fixed wide term solution. The non-common groups are eliminated, and the ambiguities of the historical and newly fixed groups are added to the common groups sequentially. The equations (non-differenced floating-point solution equations) are updated to obtain the coordinates of the second fixed wide term solution. The consistency between the first and second fixed wide term solution coordinates is checked; if they are inconsistent, the newly added wide term ambiguities are removed to obtain the target wide term ambiguity of the non-differenced floating-point solution. The initial fixed wide term ambiguities and the target wide term ambiguities are checked against the fixed MW values ​​to obtain the final wide term ambiguities of the non-differenced floating-point solution.

[0051] Specifically, step S4 includes: S41. Based on the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a narrow-term reference star is selected for each system and frequency point. If the narrow-term reference star is inconsistent with the wide-term reference star, the wide-term reference star of the final wide-term ambiguity is converted into a reference star through the inter-satellite single-difference conversion formula to obtain a new final wide-term ambiguity. S42, construct the inter-satellite single-difference narrow-term transformation matrix based on the narrow-term reference star, and obtain the first frequency point narrow-term floating-point solution based on the non-difference floating-point solution and the new final wide-term ambiguity; S43, based on the floating-point solution of the first frequency narrow term, use the Lambda algorithm to search for the ambiguity of the fixed first frequency narrow term; S44. Based on the frequency relationship between the new final wide-term ambiguity and the narrow-term ambiguity at the first frequency point, calculate the narrow-term ambiguity at other frequency points to obtain the narrow-term ambiguity group of the non-difference floating-point solution. S45, compare the narrow term ambiguity group and the historical narrow term ambiguity group to divide the narrow term common group, narrow term non-common group, narrow term historical group and narrow term new fixed group; S46, eliminate narrow term non-common groups; update the non-difference floating-point solution equation based on the narrow term common groups to obtain the first narrow term fixed coordinates of the narrow term common groups; S47, add the narrow term history group and the narrow term new fixed group in sequence, update the non-difference floating point solution equation to obtain the second narrow term fixed coordinates of the narrow term history group and the narrow term new fixed group, and check the consistency between the first narrow term fixed coordinates and the second narrow term fixed coordinates; S48, substitute the narrow-term ambiguity that has passed the check back into the non-difference floating-point solution equation to calculate the final fixed solution of the non-difference floating-point solution; S49. The virtual observation equation is constructed using the final wide term ambiguity. Kalman filtering is applied to the final fixed solution of the non-differenced floating-point solution to obtain the PPP fixed solution of the non-differenced floating-point solution.

[0052] For example, based on factors such as the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a narrow-term reference star is selected system-by-system and frequency-by-frequency. Considering that the narrow-term reference star and the wide-term reference star may be inconsistent, the selected narrow-term reference star is used to transform the wide-term reference star with fixed wide-term ambiguity. Based on the selected narrow-term reference star, an inter-satellite single-difference narrow-term transformation matrix is ​​constructed, and the fixed wide-term ambiguity is substituted into the equation to obtain the narrow-term floating-point solution for the first frequency. and From what was obtained and The Lambda algorithm is used to search for narrow-term ambiguity at a fixed first frequency point. .

[0053] , In the formula, The floating-point solution parameters for the narrow-term ambiguity of the first frequency point; The covariance matrix of the narrow-term ambiguity at the first frequency point; The fixed first frequency point narrow term integer ambiguity (first frequency point narrow term ambiguity); This is the candidate integer ambiguity vector for the first frequency point narrow term.

[0054] Using the new final broad term ambiguity and the obtained first frequency point narrow term ambiguity, the narrow term ambiguity of each combined frequency point is calculated according to the conversion relationship between frequency points. .

[0055] Fixed narrow ambiguity group at the current epoch With historical narrow fuzziness group The system is divided into four groups: narrow term common groups (equal integer values), narrow term non-common groups, narrow term historical groups, and narrow term new fixed groups. Based on the narrow term common groups, the normal equations are updated according to the update method of the wide term ambiguities to obtain the narrow term fixed solution coordinates (first narrow term fixed coordinates). The narrow term non-common groups are removed, and the narrow term ambiguities from the narrow term historical groups and the narrow term new fixed groups are sequentially added to the narrow term common groups. The normal equations are updated to obtain new narrow term fixed solution coordinates (second narrow term fixed coordinates). The consistency between the first and second narrow term fixed coordinates is checked; if they are inconsistent, the newly added narrow term ambiguities are removed, and the fixed narrow term ambiguities are substituted back into the normal equations to obtain the final fixed solution of the non-difference floating-point solution.

[0056] , , , In the formula, The covariance matrix for narrow-term ambiguity; Parameters for the floating-point solution of narrow-term ambiguity; For the updated narrow-term ambiguity floating-point solution parameters; This is the updated narrow-term ambiguity covariance matrix; The fixed narrow term integer ambiguity (narrow term ambiguity); This is a narrow-term candidate integer ambiguity vector.

[0057] A virtual observation equation is constructed based on the broad term ambiguity, and Kalman filtering is applied to the non-difference, non-combination floating-point solution to improve the accuracy of the floating-point solution. This embodiment of the invention employs a method of successive search using ultra-broad terms, broad terms, and narrow terms to significantly reduce the dimensionality of the ambiguity to be searched, improve search efficiency, reduce the computational load on the terminal, and make the multi-frequency, multi-mode PPP-AR algorithm suitable for low-cost, low-power onboard terminals. The method of constraining narrow term ambiguities with ultra-broad term / broad term ambiguities improves the success rate of narrow term ambiguity search. By fusing the search ambiguity group of the current epoch with the historical ambiguity group, the continuity and stability of the fixed solution are improved, making it suitable for complex observation environments such as vehicle-mounted dynamics.

[0058] This invention discloses a precise single-point positioning ambiguity fixing optimization method. It acquires the original observation data from a rover station and performs undifferentiated data preprocessing on the original observation data. The processed observation data is then used to calculate the PPP floating-point solution to obtain the undifferentiated floating-point solution for the current epoch of the rover station. Based on the combined MW observation values, the wide-term ambiguity of the undifferentiated floating-point solution is fixed to obtain the final wide-term ambiguity of the undifferentiated floating-point solution. Finally, based on the final wide-term ambiguity, the narrow-term ambiguity of the undifferentiated floating-point solution is fixed to obtain the PPP fixed solution of the undifferentiated floating-point solution. This method can solve the problems of low ambiguity search efficiency, high terminal load, and poor continuity and stability of fixed solutions in complex scenarios that exist in existing non-combinatorial PPP-AR technologies.

[0059] See Figure 3 , Figure 3 This is a schematic diagram of the structure of a precision single-point positioning ambiguity fixing and optimization device 10 provided in an embodiment of the present invention. The precision single-point positioning ambiguity fixing and optimization device 10 includes: The observation data acquisition module 11 is used to acquire the raw observation data of the rover station and perform non-differential data preprocessing on the raw observation data; The non-difference floating-point solution module 12 is used to perform PPP floating-point solution on the processed observation data to obtain the non-difference floating-point solution of the current epoch of the mobile station. The wide term result fixing module 13 is used to fix the wide term ambiguity of the unequal floating-point solution based on the MW combined observations, and obtain the final wide term ambiguity of the unequal floating-point solution; Narrow term result fixing module 14 is used to fix the narrow term ambiguity of the non-difference floating-point solution according to the final wide term ambiguity, so as to obtain the PPP fixed solution of the non-difference floating-point solution.

[0060] The precision single-point positioning ambiguity fixing and optimization device 10 provided in this embodiment of the invention can realize all the processes of the precision single-point positioning ambiguity fixing and optimization method of the above embodiments. The functions and technical effects of each module in the device are the same as those of the precision single-point positioning ambiguity fixing and optimization method of the above embodiments, and will not be repeated here.

[0061] See Figure 4 , Figure 4 This is a schematic diagram of the structure of a precise single-point positioning ambiguity fixing and optimization device 20 provided in an embodiment of the present invention. The precise single-point positioning ambiguity fixing and optimization device 20 of this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the above-described precise single-point positioning ambiguity fixing and optimization method embodiment. Alternatively, when the processor 21 executes the computer program, it implements the functions of each module in the above-described precise single-point positioning ambiguity fixing and optimization device embodiment.

[0062] For example, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the precise single-point positioning ambiguity fixing and optimization device 20.

[0063] The precise single-point positioning ambiguity fixing and optimization device 20 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The precise single-point positioning ambiguity fixing and optimization device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the precise single-point positioning ambiguity fixing and optimization device 20 and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the precise single-point positioning ambiguity fixing and optimization device 20 may also include input / output devices, network access devices, buses, etc.

[0064] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the precise single-point positioning ambiguity fixing and optimization device 20, connecting all parts of the device through various interfaces and lines.

[0065] The memory 22 can be used to store the computer program and / or modules. The processor 21 implements various functions of the precise single-point positioning ambiguity fixing optimization device 20 by running or executing the computer program and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0066] The module integrated into the precise single-point positioning ambiguity fixing optimization device 20, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0067] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0068] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the precise single-point positioning ambiguity fixing optimization method as described in the above embodiments.

[0069] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for fixing and optimizing ambiguity in precise single-point positioning, characterized in that, include: Obtain the raw observation data from the rover station and perform undifferentiated data preprocessing on the raw observation data; The PPP floating-point solution is used to solve the processed observation data to obtain the non-difference floating-point solution for the current epoch of the rover station; The wide term ambiguity of the unequal floating-point solution is fixed based on the MW combined observations, and the final wide term ambiguity of the unequal floating-point solution is obtained. By fixing the narrow term ambiguity of the unequal floating-point solution based on the final wide term ambiguity, the PPP fixed solution of the unequal floating-point solution is obtained.

2. The precise single-point positioning ambiguity fixing and optimization method as described in claim 1, characterized in that, The process of acquiring the raw observation data from the rover station and performing undifferentiated data preprocessing on the raw observation data includes: Acquire the raw observation data from the rover station; the raw observation data includes raw unequal pseudorange observations and raw carrier phase observations; The PVT algorithm is used to process the original undifferentiated pseudorange observations, and pseudorange observations containing gross errors and anomalous satellites are removed to obtain the initial position, velocity, receiver clock error, effective pseudorange observations and satellite list of the rover station; External precision products and preset error models are used to correct the effective pseudorange observations and corresponding carrier phase observations, resulting in corrected observations and high-precision satellite positions; A cycle slip detection algorithm is used to detect cycle slips in the carrier phase observations in the corrected observations. The associated information of satellites with cycle slips is reset and satellite cycle slips are marked. The results of error correction observations, satellite cycle slip marks and reset associated information are obtained.

3. The precise single-point positioning ambiguity fixing and optimization method as described in claim 2, characterized in that, The step of performing PPP floating-point solution calculation on the processed observation data to obtain the non-difference floating-point solution for the current epoch of the mobile station includes: Based on the initial position of the rover station, the observations after error correction, and the high-precision satellite position, the non-difference, non-combined observation equation matrix of the rover station is constructed using a non-combined PPP mathematical model. Extended Kalman filtering estimation is performed on the non-differenced, non-combined observation equation matrix to obtain the non-differenced floating-point solution of the current epoch of the mobile station. The non-differenced floating-point solution includes floating-point solution parameters and floating-point solution covariance matrix.

4. The precise single-point positioning ambiguity fixing and optimization method as described in claim 3, characterized in that, The step of fixing the broad term ambiguity of the unequal floating-point solution based on the MW combined observations to obtain the final broad term ambiguity of the unequal floating-point solution includes: Based on the non-difference, non-combined observation equation matrix, MW combined observations are constructed using narrow-term pseudorange combination and wide-term carrier combination. The preliminary wide-term fixed ambiguity of the MW combined observations is obtained by fixing the wide-term ambiguity of the MW combined observations. Based on the initial wide term fixed ambiguity, the non-difference floating-point solution is subjected to ultra-wide term / wide term ambiguity fixation to obtain the final wide term ambiguity of the non-difference floating-point solution.

5. The precise single-point positioning ambiguity fixing and optimization method as described in claim 4, characterized in that, The process of fixing the broad term ambiguity of the MW combined observations to obtain the preliminary broad term fixed ambiguity of the MW combined observations includes: The mean of the MW combined observations is calculated by averaging over multiple epochs, and the non-difference wide term ambiguity of the MW combined observations is obtained. Reference stars are selected for each system and frequency, and an inter-satellite single-difference transformation matrix is ​​constructed to convert non-difference wide-term ambiguities into inter-satellite single-difference wide-term ambiguities. The inter-satellite single-difference wide-term ambiguities are rounded to obtain a fixed number of ambiguities for different reference stars. Select the reference star with the most fixed ambiguities as the target reference star to obtain the current epoch wide-term fixed ambiguity group of the MW combined observation; Compare the current epoch wide-term ambiguity fixed group with the historical wide-term ambiguity fixed group to classify the MW combined observations into common group, non-common group, historical group, and new fixed group; By removing non-common groups from the MW combined observations and checking the coordinate consistency between the historical groups and the new fixed groups of the MW combined observations, the preliminary wide term fixed ambiguity of the MW combined observations is obtained; wherein, the preliminary wide term fixed ambiguity includes the final wide term ambiguity and the corresponding covariance matrix of the MW combined observations.

6. The precise single-point positioning ambiguity fixing and optimization method as described in claim 5, characterized in that, The step of fixing the super-wide term / wide term ambiguity of the unequal floating-point solution based on the initial wide term fixed ambiguity to obtain the final wide term ambiguity of the unequal floating-point solution includes: Based on factors such as the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a wide-term reference satellite is selected for each system and frequency point to construct an inter-satellite single-difference ultra-wide term / wide term transformation matrix, which converts the non-difference floating-point solution into an inter-satellite single-difference ultra-wide term / wide term solution. Extract the ultrawide term ambiguity from the inter-satellite single-difference ultrawide term / wide term solution, and use the Lambda algorithm to search for the fixed ultrawide term ambiguity; Substitute the ultrawide ambiguity back into the non-difference floating-point solution equation to obtain the wide ambiguity floating-point solution, and use the Lambda algorithm to search for the fixed wide ambiguity. Based on the conversion relationship between frequency points, the wide term ambiguity of each combination frequency point is calculated through ultra-wide term ambiguity and wide term ambiguity to obtain the wide term ambiguity group of the non-difference floating point solution; The wide term ambiguity group and the historical wide term ambiguity group are compared and divided into the wide term common group, the wide term non-common group, the wide term historical group, and the wide term new fixed group; The non-difference floating-point solution equation is updated based on the common group of the broad term ambiguities to obtain the coordinates of the first broad term fixed solution. Remove the non-common group of the broad terms ambiguity, add the broad terms ambiguity of the broad terms history group and the new fixed group of the broad terms to the broad terms common group of the broad terms ambiguity in turn, and update the non-difference floating-point solution equation to obtain the coordinates of the second broad term fixed solution; Verify the consistency between the coordinates of the first wide term fixed solution and the coordinates of the second wide term fixed solution. If they are inconsistent, remove the newly added wide term ambiguity to obtain the target wide term ambiguity of the non-difference floating-point solution. Verify the consistency of the coordinates of the initial wide term fixed fuzziness and the target wide term fuzziness. If they are inconsistent, remove the corresponding wide term fuzziness from the target wide term fuzziness to obtain the final wide term fuzziness of the non-difference floating-point solution.

7. The precise single-point positioning ambiguity fixing optimization method as described in claim 5, characterized in that, The step of fixing the narrow term ambiguity of the unequal floating-point solution based on the final wide term ambiguity to obtain the PPP fixed solution of the unequal floating-point solution includes: Based on factors such as the fixed ambiguity of the previous epoch, the cycle slip of the current epoch, and the satellite elevation angle, a narrow-term reference star is selected for each system and frequency point. If the narrow-term reference star is inconsistent with the wide-term reference star, the wide-term reference star of the final wide-term ambiguity is converted into a reference star through the inter-satellite single-difference conversion formula to obtain a new final wide-term ambiguity. Construct an inter-satellite single-difference narrow-term transformation matrix based on the narrow-term reference star, and obtain the first frequency point narrow-term floating-point solution based on the non-difference floating-point solution and the new final wide-term ambiguity. Based on the floating-point solution of the first frequency narrow term, the Lambda algorithm is used to search for the ambiguity of the fixed first frequency narrow term. Based on the frequency relationship between the new final wide-term ambiguity and the narrow-term ambiguity at the first frequency point, the narrow-term ambiguity at other frequency points is calculated to obtain the narrow-term ambiguity set of the non-difference floating-point solution. The narrow term ambiguity group is compared with the historical narrow term ambiguity group to divide the narrow term common group, narrow term non-common group, narrow term historical group and narrow term new fixed group; Eliminate the narrow term non-common group; update the non-difference floating-point solution equation based on the narrow term common group to obtain the first narrow term fixed coordinate of the narrow term common group; The narrow term history group and the narrow term new fixed group are added in sequence. The non-difference floating-point solution equation is updated to obtain the second narrow term fixed coordinates of the narrow term history group and the narrow term new fixed group. The consistency between the first narrow term fixed coordinates and the second narrow term fixed coordinates is checked. Substitute the narrow-term ambiguity that has passed the check back into the non-difference floating-point solution equation to calculate the final fixed solution of the non-difference floating-point solution; A virtual observation equation is constructed using the final broad term ambiguity. Kalman filtering is then applied to the final fixed solution of the unequal floating-point solution to obtain the PPP fixed solution of the unequal floating-point solution.

8. A precision single-point positioning ambiguity fixing and optimization device, characterized in that, include: The observation data acquisition module is used to acquire the raw observation data of the rover station and perform non-differential data preprocessing on the raw observation data; The non-differenced floating-point solution module is used to perform PPP floating-point solution on the processed observation data to obtain the non-differenced floating-point solution of the rover station for the current epoch. The wide term result fixing module is used to fix the wide term ambiguity of the unequal floating-point solution based on the MW combined observations, and obtain the final wide term ambiguity of the unequal floating-point solution; The narrow term result fixing module is used to fix the narrow term ambiguity of the non-differenced floating-point solution based on the final wide term ambiguity, so as to obtain the PPP fixed solution of the non-differenced floating-point solution.

9. A precision single-point positioning ambiguity fixing and optimization device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the precise single-point positioning ambiguity fixing optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the precise single-point positioning ambiguity fixing optimization method as described in any one of claims 1-7.

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