Ambiguity adaptive resolving method and system based on ILS and BIE estimation

Abstract

The invention discloses an ambiguity adaptive resolving method and system based on ILS and BIE estimation. The method comprises the following steps: acquiring GNSS original observed quantity; constructing a pseudo-range and carrier phase double-difference observation equation, and calculating a floating point solution of an ambiguity parameter by using extended Kalman filtering; performing ambiguity fixation on the ambiguity subset through an ILS algorithm to obtain an ILS fixed solution, performing ambiguity fixed check on the ILS fixed solution, if the check is passed, obtaining a final solution, and if the check is not passed, performing ambiguity fixation through a BIE algorithm to obtain a BIE fixed solution: performing multiple screening on the ambiguity subset based on an observation value domain and a state domain to obtain an optimal ambiguity subset; searching the optimal ambiguity subset by using an improved SEVB algorithm to obtain a BIE multi-ambiguity candidate solution; and calculating a BIE fixed solution, and performing reliability verification on the BIE fixed solution to obtain a final fixed solution. According to the method, the ambiguity fixing success rate can be improved, meanwhile, the precision of a fixed solution is remarkably improved, and the ambiguity resolving efficiency is effectively optimized.

Description

Technical Field

[0001] The present application relates to the technical field of global navigation satellite system positioning technology, and in particular, to a method, system, storage medium, and electronic device for ambiguity adaptive resolution based on ILS and BIE estimation. Background Art

[0002] The key to high-precision positioning of GNSS (Global Navigation Satellite System) is integer ambiguity fixing. There are three types of estimators in existing theories: integer (I) estimator class, integer aperture (IA) estimator class, and integer equivariant (IB) estimator class. The relationship among them is . Among them, the integer least squares estimator of the I class has a rigorous theory and the highest fixing success rate. Its classic implementation is the Least Square Ambiguity Decorrelation Adjustment (LAMBDA) method based on ILS. One of the significant features of ILS estimation is that after fixing the ambiguity as an integer, a reliability test is performed to ensure the correctness of the fixing result. There are three common types of test methods: the test method based on the statistical distinguishability between the optimal and sub-optimal candidate solutions, the determination method based on the success rate / failure rate index in the theoretical framework of ambiguity estimation, and the comprehensive method combining the two. These methods can be unified into the IA class, and the failure rate can be controlled by setting the size and shape of the aperture domain. Currently, ILS estimation is widely used. Under good observation conditions, technologies such as Real-Time Kinematic (RTK) and Precision Point Positioning (PPP) based on this method can fix the ambiguity instantaneously or within a few minutes and achieve centimeter-level positioning accuracy.

[0003] In addition, to further improve the success rate of ambiguity fixing, Partial Ambiguity Resolution (PAR) is proposed, that is, a suitable subset is selected from the high-dimensional ambiguity set for fixing. Common subset sorting indicators include success rate, dilution of precision, elevation angle, signal-to-noise ratio, etc. However, in dynamic positioning in urban environments, affected by high occlusion, strong reflection, and frequent maneuvers, GNSS signals frequently experience problems such as intermittence, attenuation, and interruption. Non-modeled errors such as multipath and Non-Line-Of-Sight (NLOS) are prominent, and the data quality significantly deteriorates. The full ambiguity resolution (FAR) or partial ambiguity resolution method based on ILS estimation is vulnerable to abnormal observations, resulting in a low success rate of ambiguity fixing, thus generating a large number of low-precision floating-point solutions or incorrect fixed solutions, making it difficult to meet the requirements of high-precision positioning.

[0004] In recent years, optimal integer equivariant estimation has gradually attracted the attention of scholars. Through weighted fusion of all ambiguity candidate solutions, BIE estimation can obtain the optimal ambiguity estimate in the sense of Minimum Mean Squared Error (MMSE). The effectiveness of BIE estimation has been verified in technologies such as RTK, PPP, and PPP-RTK. It is generally believed that it can improve the convergence speed and provide positioning accuracy between the floating-point solution and the fixed solution. However, BIE estimation still has some limitations: First, BIE estimation can only output the floating-point solution of ambiguity, losing the integer property of ambiguity. Whether it can completely replace the ILS fixed solution under complex conditions remains to be clarified. Second, theoretically, BIE estimation requires an infinite number of groups of candidate solutions. How to quickly and reliably determine a finite number of BIE candidate solutions under complex conditions to reduce the ambiguity resolution time and ensure the high timeliness of real-time positioning remains to be studied. Finally, traditional BIE estimation assumes that the data follows a Gaussian distribution. However, in application scenarios such as complex observation environments and low-cost receivers, GNSS observations usually show obvious heavy-tail effects, resulting in too large quadratic forms of ambiguity residuals and unreasonable weight allocation among candidate solutions, seriously affecting the reliability of positioning results. Summary of the Invention

[0005] The embodiments of the present application provide an ambiguity adaptive resolution method, system, storage medium, and electronic device based on ILS and BIE estimation, which can improve the success rate of ambiguity fixing, significantly enhance the accuracy of the fixed solution, and effectively optimize the ambiguity resolution efficiency.

[0006] The embodiments of the present application provide an ambiguity adaptive resolution method based on ILS and BIE estimation, including: Obtain GNSS raw observations; Construct the pseudo-range and carrier-phase double-difference observation equations based on the GNSS raw observations, and use the extended Kalman filter to calculate the floating-point solution of the ambiguity parameters; Construct an ambiguity subset based on the floating-point solution of the ambiguity parameters, perform ambiguity fixing on the ambiguity subset through the ILS algorithm to obtain the ILS fixed solution, and conduct an ambiguity fixing verification on the ILS fixed solution. If the verification is passed, the ILS fixed solution is used as the final solution. If the verification fails, perform ambiguity fixing through the BIE algorithm to obtain the BIE fixed solution; The process of obtaining the BIE fixed solution by performing ambiguity fixing through the BIE algorithm includes: Perform multiple screenings on the ambiguity subset based on the observation value domain and the state domain to obtain the optimal ambiguity subset; Use the improved SEVB algorithm based on weight decay constraint to search for the optimal ambiguity subset to obtain the BIE multi-ambiguity candidate solutions; Obtain the BIE fixed solution based on the BIE multi-ambiguity candidate solutions, and conduct a reliability verification on the BIE fixed solution to obtain the final fixed solution.

[0007] Furthermore, for the above-mentioned ambiguity adaptive resolution method based on ILS and BIE estimation, wherein, the process of constructing the pseudo-range and carrier-phase double-difference observation equations based on the GNSS raw observations and using the extended Kalman filter to calculate the floating-point solution of the ambiguity parameters includes: Construct the pseudo-range double-difference observation equation and the carrier-phase double-difference observation equation based on the GNSS raw observations to obtain the double-difference observation values; Construct the initial state vector and covariance matrix, and define the state transition matrix and the observation matrix; Update the observation values of each epoch through the Kalman filter algorithm to obtain the floating-point solution of the ambiguity parameters.

[0008] Furthermore, for the above-mentioned ambiguity adaptive resolution method based on ILS and BIE estimation, wherein, the process of constructing the ambiguity subset based on the floating-point solution of the ambiguity parameters, performing ambiguity fixing on the ambiguity subset through the ILS algorithm to obtain the ILS fixed solution, and conducting an ambiguity fixing verification on the ILS fixed solution includes: Construct an ambiguity rejection index based on the floating-point solution and the covariance matrix; Perform integer Gauss transformation on the floating-point solution to obtain the integer transformation matrix; Search for the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution through the SEVB algorithm for the integer transformation matrix; Based on the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution, perform ambiguity fixing verification. If the verification fails, eliminate one ambiguity according to the ambiguity elimination index, and re-execute the steps of performing integer Gaussian transformation and searching through the SEVB algorithm until the ambiguity is successfully fixed or the number of remaining ambiguities does not meet the requirements.

[0009] Further, in the above ambiguity adaptive resolution method based on ILS and BIE estimation, wherein, the ambiguity fixing verification of the ILS fixed solution includes: Use the Ratio test algorithm and the PIFD test algorithm to perform ambiguity fixing verification on the ILS fixed solution respectively; Among them, using the Ratio test algorithm for ambiguity fixing verification includes: Calculate the optimal ambiguity and the sub-optimal ambiguity, calculate the Ratio value based on the optimal ambiguity and the sub-optimal ambiguity. When the Ratio value is greater than the preset test threshold, the corresponding ILS fixed solution passes the verification.

[0010] Further, in the above ambiguity adaptive resolution method based on ILS and BIE estimation, wherein, the multiple screening of the ambiguity subset based on the observation value domain and the state domain to obtain the optimal ambiguity subset includes: Perform PIFD test, fixed solution consistency test and candidate solution consistency test on the ambiguity subset respectively.

[0011] Further, in the above ambiguity adaptive resolution method based on ILS and BIE estimation, wherein, performing the PIFD test on the ambiguity subset includes: Calculate the PIFD value and the root mean square error of the fixed solution of the ambiguity subset, and eliminate the ambiguity subset corresponding to the values exceeding the PIFD threshold and the error threshold; Performing the fixed solution consistency test on the ambiguity subset includes: Calculate the frequency of occurrence of the fixed solution of the ambiguity subset after performing the PIFD test, identify the fixed solution with the highest frequency of occurrence, and eliminate the ambiguity subset inconsistent with the fixed solution; Performing the candidate solution consistency test on the ambiguity subset includes: Calculate the Euclidean distance between the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution, and eliminate the ambiguity subset with the Euclidean distance greater than the distance threshold.

[0012] Further, in the above ambiguity adaptive resolution method based on ILS and BIE estimation, wherein, using the improved SEVB algorithm based on weight decay constraint to search the optimal ambiguity subset to obtain the BIE multi-ambiguity candidate solution includes: Search for integer ambiguity candidate solutions that satisfy the first condition in the optimal ambiguity subset, where the first condition is:

[0013] where denotes the weight factor of the -th integer ambiguity candidate solution, denotes the floating-point solution of the ambiguity after integer Gaussian transformation, is a constant.

[0014] Furthermore, for the above-mentioned ambiguity adaptive resolution method based on ILS and BIE estimation, where the method further includes: Introduce the Laplace distribution to construct the BIE weight model: Assume that the GNSS observation obeys the Laplace distribution, and its probability density function can be expressed as:

[0015] where and respectively denote the GNSS observation and its variance matrix, denotes the determinant operator, denotes the exponential function operator, and respectively denote the coefficient matrices of the ambiguity parameter and the baseline parameter ; denotes the scale factor of the Laplace distribution; Then the BIE estimators of the ambiguity parameter and the baseline parameter can be expressed as:

[0016]

[0017] where

[0018] where the weight factor satisfies , denotes -dimensional integer space.

[0019] The embodiment of the present application also provides an ambiguity adaptive resolution system based on ILS and BIE estimation, including: An acquisition module, configured to acquire GNSS raw observations; A processing module, configured to construct a double-difference observation equation of pseudorange and carrier phase based on the GNSS raw observations, and calculate a floating-point solution of the ambiguity parameter using an extended Kalman filter; A solving module, configured to construct an ambiguity subset based on the floating-point solution of the ambiguity parameter, perform ambiguity fixing on the ambiguity subset through the ILS algorithm to obtain an ILS fixed solution, perform ambiguity fixing verification on the ILS fixed solution, if the verification is passed, use the ILS fixed solution as the final solution, if the verification is not passed, perform ambiguity fixing through the BIE algorithm to obtain a BIE fixed solution; The obtaining of the BIE fixed solution by performing ambiguity fixing through the BIE algorithm includes: Performing multiple screenings on the ambiguity subset based on the observation value domain and the state domain to obtain an optimal ambiguity subset; Using an improved SEVB algorithm based on weight decay constraint to search for the optimal ambiguity subset to obtain a BIE multi-ambiguity candidate solution; Obtaining a BIE fixed solution based on the BIE multi-ambiguity candidate solution, and performing reliability verification on the BIE fixed solution to obtain a final fixed solution.

[0020] An embodiment of the present application further provides a computer-readable storage medium, in which multiple instructions are stored, and the instructions are suitable for being loaded by a processor to execute any one of the above-mentioned ambiguity adaptive solving methods based on ILS and BIE estimation.

[0021] An embodiment of the present application further provides an electronic device, including a processor and a memory, the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used for the steps in any one of the above-mentioned ambiguity adaptive solving methods based on ILS and BIE estimation.

[0022] The ambiguity adaptive resolution method, system, storage medium, and electronic device based on ILS and BIE estimation provided by the present application construct an algorithm framework for the adaptive processing of ILS and BIE estimation to solve the problem that it is difficult to fix the ambiguity in complex environments; propose a triple screening strategy for the observation value range and the state domain to select the optimal ambiguity subset for BIE estimation to improve the reliability of ambiguity resolution; introduce weight decay constraints to improve the SEVB ambiguity search algorithm, realizing the dynamic search and determination of BIE candidate solutions, effectively reducing the computational complexity while ensuring the estimation accuracy; introduce the Laplace distribution to construct a BIE weight model to solve the problem of unreasonable Gaussian weight allocation caused by significant non-modeling errors in observation values; based on the error propagation law, solve the BIE solution variance under the Laplace distribution and check the BIE fixed solution, and it will only be accepted and output when the accuracy of the BIE solution exceeds the floating-point solution to ensure the reliability of the ambiguity fixed solution. The present invention can improve the success rate of ambiguity fixing, significantly improve the accuracy of the fixed solution, and effectively improve the efficiency of ambiguity resolution, thereby realizing the fast and reliable fixing of ambiguity in real-time precise GNSS positioning in urban complex environments. Description of the Drawings

[0023] The following, in conjunction with the drawings, through a detailed description of the specific embodiments of the present application, will make the technical solutions and other beneficial effects of the present application obvious.

[0024] Figure 1 It is a flowchart of the ambiguity adaptive resolution method based on ILS and BIE estimation provided by an embodiment of the present application.

[0025] Figure 2 It is another flowchart of the ambiguity adaptive resolution method based on ILS and BIE estimation provided by an embodiment of the present application.

[0026] Figure 3 It is a flowchart of the improved SEVB algorithm provided by an embodiment of the present application.

[0027] Figure 4 It is a schematic structural diagram of the ambiguity adaptive resolution system based on ILS and BIE estimation provided by an embodiment of the present application.

[0028] Figure 5 It is a schematic structural diagram of the electronic device provided by an embodiment of the present application. Detailed Embodiments

[0029] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0030] The embodiments of the present application provide a method, system, storage medium, and electronic device for ambiguity adaptive resolution based on ILS and BIE estimation. The ambiguity adaptive resolution system based on ILS and BIE estimation provided by the embodiments of the present application can be integrated into an electronic device, which can be a device such as a terminal or a server. Among them, the terminal can include a tablet computer, a notebook computer, a personal computer (PC), a microprocessing box, or other devices, etc.

[0031] Please refer to Figure 1 With Figure 2 , Figure 1 is a flowchart of the ambiguity adaptive resolution method based on ILS and BIE estimation provided by the embodiments of the present application, Figure 2 is another flowchart of the ambiguity adaptive resolution method based on ILS and BIE estimation provided by the embodiments of the present application, which is applied to an electronic device. The ambiguity adaptive resolution method based on ILS and BIE estimation includes the following steps: S1, Obtain GNSS raw observations.

[0032] Among them, the GNSS raw observations include pseudorange and carrier phase.

[0033] S2, Based on the GNSS raw observations, construct a double-difference observation equation for pseudorange and carrier phase, and use the extended Kalman filter to calculate the floating-point solution of the ambiguity parameter.

[0034] In one embodiment, step S2 includes the following steps: S21, Based on the GNSS raw observations, construct a double-difference observation equation for pseudorange and a double-difference observation equation for carrier phase to obtain double-difference observations; S22, Construct an initial state vector and a covariance matrix, and define a state transition matrix and an observation matrix; S23, Update the observations of each epoch through the Kalman filter algorithm to obtain the floating-point solution of the ambiguity parameter.

[0035] Specifically, based on the GNSS raw observation pseudorange and carrier phase, first perform the inter-station difference of the observations between two stations (to obtain the single difference value), and then select a reference star according to characteristics such as elevation angle and signal-to-noise ratio. Take the difference between the single difference values of other non-reference stars and the single difference value of the reference star to obtain the double-difference observation equation. Subsequently, use the extended Kalman filter to estimate the position and ambiguity parameters to obtain the RTK float solution.

[0036] Among them, in the stage of calculating the float solution, set a certain cut-off signal-to-noise ratio (such as 30 dB-Hz) to eliminate significantly abnormal observations.

[0037] S3. Based on the float solution of the ambiguity parameters, construct an ambiguity subset. Fix the ambiguity of the ambiguity subset through the ILS algorithm to obtain the ILS fixed solution, and conduct an ambiguity fixing verification on the ILS fixed solution. If the verification is passed, take the ILS fixed solution as the final solution. If the verification is not passed, fix the ambiguity through the BIE algorithm to obtain the BIE fixed solution.

[0038] In one embodiment, constructing the ambiguity subset based on the float solution of the ambiguity parameters in step S3, fixing the ambiguity of the ambiguity subset through the ILS algorithm to obtain the ILS fixed solution, and conducting an ambiguity fixing verification on the ILS fixed solution includes: S31. Based on the float solution and the covariance matrix, construct an ambiguity rejection index.

[0039] Specifically, construct the ambiguity rejection index according to the float ambiguity and its variance-covariance matrix. Considering that in a complex environment, the correlation between the signal-to-noise ratio and the observation quality is stronger, this paper adopts a partial ambiguity fixing method based on the signal-to-noise ratio. Specifically, in the data preprocessing stage, set a cut-off signal-to-noise ratio of 30 dB-Hz to eliminate significantly abnormal observations; in the ambiguity fixing stage, sort the float ambiguities according to the signal-to-noise ratio. The lower the signal-to-noise ratio, the later the sorting, and they are preferentially rejected when the ambiguity cannot be fixed.

[0040] S32. Perform an integer Gauss transformation on the float solution to obtain an integer transformation matrix.

[0041] S33. Search the integer transformation matrix through the SEVB algorithm to obtain the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution.

[0042] The SEVB algorithm uses a depth - first method to search layer by layer. The size of the search interval within each layer depends on the search space and the conditional variance corresponding to that layer. In the first search, the search space is set to infinity. The first ambiguity candidate group is obtained by sequential rounding from the nth layer to the 1st layer. The objective function value of this group of solutions is used as the new search space size. Then, continue the search within the updated search space. If there is still a solution in the 1st layer, another candidate group is obtained; if there is no solution, return to the next integer point in the 2nd layer. And so on. Whenever the objective function value of a new candidate group is less than the current space size, update the value of the current space, realizing the continuous contraction of the search space.

[0043] S34. Based on the optimal integer ambiguity candidate solution and the sub - optimal integer ambiguity candidate solution, conduct ambiguity fixing verification. If the verification fails, eliminate one ambiguity according to the ambiguity elimination index, and re - execute the steps of integer Gauss transformation and search through the SEVB algorithm until the ambiguity is successfully fixed or the number of remaining ambiguities does not meet the requirements.

[0044] Specifically, the ambiguity fixing verification includes Ratio test and PIFD test. If the test is passed, output the ILS fixed solution; otherwise, record the fixed information of the current ambiguity subset (including recording the values of the current Ratio test and PIFD test, as well as the number of fixed phases and the number of fixed satellites). After eliminating one ambiguity according to the ambiguity elimination index, re - execute steps S33 - S34 until the ambiguity is successfully fixed or the number of remaining ambiguities does not meet the requirements.

[0045] In one embodiment, the ambiguity fixing verification for the ILS fixed solution includes: S341. Use the Ratio test algorithm and the PIFD (double - difference carrier - phase inter - frequency difference) test algorithm to conduct ambiguity fixing verification for the ILS fixed solution respectively.

[0046] Among them, using the Ratio test algorithm for ambiguity fixing verification includes: Calculate the optimal ambiguity and the sub - optimal ambiguity, calculate the Ratio value based on the optimal ambiguity and the sub - optimal ambiguity. When the Ratio value is greater than the preset test threshold, the corresponding ILS fixed solution passes the verification.

[0047] Specifically, the core idea of the Ratio test is to judge the difference between the optimal and sub - optimal ambiguity candidate solutions, which can be defined as: (1) Among them,, ; and respectively represent the floating - point solution of the ambiguity and its variance matrix, and respectively represent the optimal ambiguity candidate solution and the sub - optimal ambiguity candidate solution, represents the test threshold. The larger the test threshold, the higher the reliability of the fixed solution, which can generally be given by experience.

[0048] Since the Ratio test is prone to two types of errors: removing true solutions and accepting false solutions, the PIFD test is used to further improve the reliability of the fixed solution. Its definition is shown in Equation (3). If the optimal candidate solution passes the test, the ILS fixed solution is output; otherwise, the fixed information of the current ambiguity subset is recorded. After removing one ambiguity according to the index, the ILS estimation is performed again until the ambiguity is fixed successfully or the number of remaining ambiguities does not meet the requirements.

[0049] Among them, obtaining the BIE fixed solution by fixing the ambiguity through the BIE algorithm in step S3 includes: S35, performing multiple screenings on the ambiguity subset based on the observation value domain and the state domain to obtain the optimal ambiguity subset.

[0050] Among them, performing multiple screenings on the ambiguity subset is a triple screening of the observation value domain and the state domain, specifically including: performing the PIFD test, the fixed - solution consistency test, and the candidate - solution consistency test on the ambiguity subset respectively.

[0051] In one embodiment, step S35 includes the following steps: S351, performing the PIFD test on the ambiguity subset, including: Calculating the PIFD value and the root - mean - square error of the fixed solution of the ambiguity subset, and removing the ambiguity subset corresponding to exceeding the PIFD threshold and the error threshold.

[0052] Specifically, let the float solution of each ambiguity subset be , where represents that this subset has removed ambiguities compared to the full set, represents the maximum number of removable ambiguities.

[0053] In short - baseline relative positioning, by using inter - station and inter - satellite differencing, errors such as satellite - side and receiver - side clock errors and hardware delays can be effectively eliminated, and the influence of atmospheric errors and orbit errors can be weakened. Its double - difference carrier - phase observation equation can be expressed as: (2) Among them, represents the inter - station and inter - satellite differencing operation, represents the carrier - phase observation value, represents the geometric distance between the satellite and the receiver, represents the phase wavelength, represents the phase integer - week ambiguity, Represents the phase observation noise, including multipath errors, atmospheric residual errors, etc.

[0054] Taking the dual-frequency observations as an example, after obtaining the integer ambiguity by search, the formula (2) can be differenced between frequency points 1 and 2 to obtain: (3) When the multipath interference is weak and the ambiguity is fixed correctly, the right end of formula (3) is a mixed random noise with a very small magnitude. The multipath effects with serious influence or wrongly fixed ambiguities can be excluded by setting a threshold. In this embodiment, the PIFD and its root mean square (RMS) error of all subset fixed solutions are calculated, and the subsets with excessive errors are removed: (4) Wherein, Represents the PIFD RMS corresponding to subset , Represents the test threshold.

[0055] It should be noted that this strategy is adopted for both the ILS fixed check and the BIE subset selection to ensure the reliability of the fixed solution.

[0056] S352, perform a fixed solution consistency test on the ambiguity subsets, including: Calculate the frequency of occurrence of the fixed solutions of the ambiguity subsets after the PIFD test, identify the fixed solution with the highest frequency of occurrence, and remove the ambiguity subsets inconsistent with the fixed solution.

[0057] Specifically, after the observation value range screening, there are still a large number of qualified ambiguity subsets. The state domain screening can be introduced to further refine the subsets. The core idea is to test the consistency of the fixed solutions among the ambiguity subsets. In practical applications, due to the accuracy differences among the ambiguity parameters and the different model strengths of each subset, the fixed values of the overlapping ambiguities may not be exactly the same among different subsets. To screen out the ambiguity subsets with higher reliability, the Frequency of occurrence of the fixed solutions in different subsets can be counted, the fixed solution with the highest frequency of occurrence is identified, and all ambiguity subsets inconsistent with this fixed solution are removed. Suppose The fixed solutions (i.e., the optimal candidate solutions) in different subsets are respectively , where the fixed solution with the highest frequency of occurrence is , then the subset set after screening can be expressed as: (5) S353, perform a candidate solution consistency test on the ambiguity subsets, including: Calculate the Euclidean distance between the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution, and eliminate the ambiguity subset with the Euclidean distance greater than the distance threshold.

[0058] Specifically, the consistency check is a supplement to the state domain screening. The core idea is to evaluate the difference between the optimal and sub-optimal candidate solutions within the ambiguity subset. The smaller the difference, the higher the reliability of the fixed solution. Since the ILS estimation performed first provides the optimal and sub-optimal candidate solutions, the present invention directly calculates the Euclidean distance between the two for evaluation. If the difference is significant, it is considered that the ambiguity fixation is unreliable, and the subset is eliminated. Assume that the optimal and sub-optimal candidate solutions of different subsets are and respectively, then the set of subsets after screening can be expressed as: (6) where, represents the Euclidean distance between two vectors, represents the test threshold.

[0059] Through the triple screening strategy of the above observation value domain and state domain, if no candidate solution passes the test, the floating-point solution is selected for output to avoid the risk of BIE error estimation; if multiple candidate solutions pass the screening, the ambiguity subset with the best geometric configuration is preferentially selected for BIE estimation to enhance the model strength.

[0060] S36. Use the improved SEVB algorithm based on weight decay constraint to search for the optimal ambiguity subset to obtain the BIE multi-ambiguity candidate solution.

[0061] Search for the integer ambiguity candidate solution that satisfies the first condition in the optimal ambiguity subset. The first condition is:

[0062] where, represents the weight factor of the th integer ambiguity candidate solution, represents the floating-point solution of the ambiguity after integer Gaussian transformation, is an integer.

[0063] Specifically, when fusing the ILS and BIE estimators, special attention must be paid to the computational efficiency of BIE because obtaining its theoretical estimate requires an infinite number of sets of ambiguity candidate solutions in the integer space. To ensure the feasibility of BIE estimation in practical applications, approximate processing must be introduced, that is, the weighted fusion of BIE is completed within a finite integer set. The key lies in minimizing the resulting accuracy loss. This process is mainly divided into two steps: Search and determination of integer ambiguity candidates. Usually, we assume that the quadratic form of ambiguity residuals follows a central chi-square distribution and determine the search range by selecting a significance level. Subsequently, we search for all integer ambiguity candidates within the defined region to complete the BIE estimation. Although this method is somewhat adapted to the GNSS model to a certain extent, it may not be stable enough in practical applications.

[0064] The present invention improves the SEVB algorithm. By introducing additional constraint information, it can dynamically search for and determine BIE candidates. Its basic idea is to include only candidates that satisfy the following conditions in the BIE estimation: (7) where, represents the weight factor of the th integer ambiguity candidate solution, represents the floating-point solution of the ambiguity after integer Gaussian transformation, and there is .

[0065] This condition indicates that the weight factor of the candidate solution to be excluded is at most times that of the optimal candidate solution. The smaller (8) where: (9) where, represents the optimal ambiguity candidate solution, and there is , represents the scale factor of the Laplace distribution, which can be given empirically, can be dynamically calculated according to the current minimum quadratic form of ambiguity residuals during the search process, and the search space defined by it will also be dynamically shrunk during the search process. Once the optimal candidate solution is found, the final size of the search space and the candidates required for BIE will also be determined accordingly.

[0066] The advantage of this method is that it can simultaneously complete the search and determination of BIE candidates, significantly improving the search efficiency while ensuring accuracy. More advantageously, in some specific scenarios (for example, when the ILS estimation is calculated prior to the BIE estimation), since the optimal candidate solution has been determined, the search process for BIE candidates can be further accelerated. Based on whether the ILS estimation is performed before the BIE estimation, the following two cases will be discussed and in Figure 2Are highlighted in dark green and light green respectively: Case 1: Directly perform BIE estimation. Similar to the SEVB algorithm, the search space size is set to infinity in the initial stage, and rounding is performed sequentially from the nth level to the 1st level to obtain the first integer candidate solution. Whenever a new candidate solution is found, its residual quadratic form needs to be checked whether it is the smallest. If the condition is satisfied, is recalculated to dynamically shrink the search space. This process continues until all qualified candidate solutions are found.

[0067] Case 2: First perform ILS estimation, and then perform BIE estimation. The optimal integer candidate solution obtained from the ILS estimation will be used as the starting point for searching the BIE candidate solutions. The search space size is determined according to Equation (9) and remains unchanged throughout the search process. All integer candidate solutions included therein will be incorporated into the BIE estimation.

[0068] Figure 3 is the flowchart of the improved SEVB algorithm provided by the embodiments of the present application. As Figure 3 shown, the improved SEVB algorithm defines a weight decay factor to control the weights of the candidate solutions, ensuring that the influence of the excluded candidate solutions on the BIE estimation results can be ignored, thereby limiting the search space of the integer candidate solutions to a finite set of integers. The algorithm takes the unit lower triangular matrix , diagonal matrix , real ambiguity vector and the number of ambiguities as inputs, and finally outputs integer ambiguity candidate solutions, which are sorted and stored in the matrix in ascending order of the residual quadratic form. Figure 3 In , represents the sequential conditional estimate of the scalar , represents rounding the value to the nearest integer, maxDist represents the size of the current search space, represents the level where the current search node is located, count represents the number of candidate solutions that have been searched so far, is used to calculate , which is initialized as a -dimensional zero matrix, represents the sign function, returns when , otherwise it returns , represents the sequential conditional estimate of the vector

[0069] S37. Obtain the BIE fixed solution based on the BIE multiple ambiguity candidate solutions, perform reliability verification on the BIE fixed solution, and obtain the final fixed solution.

[0070] Furthermore, the method of the present invention further includes: The traditional BIE estimation assumes that the data follows a Gaussian distribution. However, in complex environments and with low-cost receivers, GNSS observations usually exhibit an obvious heavy-tail effect, resulting in an excessive quadratic form of ambiguity residuals and an unreasonable weight distribution among candidate solutions, seriously affecting the reliability of the positioning result. The present invention introduces the Laplace distribution to construct the BIE weight model: Assume that the GNSS observations follow the Laplace distribution, and its probability density function can be expressed as: (10) Where and respectively represent the GNSS observations and their variance matrix, represents the determinant operator, represents the exponential function operator, and respectively represent the coefficient matrices of the ambiguity parameter and the baseline parameter ; represents the scale factor of the Laplace distribution; Then the BIE estimators of the ambiguity parameter and the baseline parameter can be expressed as: (11) (12) Where (13) Where the weight factor satisfies , represents the n-dimensional integer space.

[0071] Different from the Gaussian distribution, the Laplace distribution takes the square root of the quadratic form of the residuals and divides by the scale factor, and effectively prevents the wrong estimation of the ambiguity by evenly distributing the weights among the candidate solutions.

[0072] The overall process of the method of the present invention is as shown in Figure 2As shown, the technical solution is summarized as follows: 1. To address the problem of difficult ambiguity fixation in complex environments, an algorithm framework for adaptive ambiguity processing of ILS and BIE estimation is constructed; 2. To improve the reliability of BIE estimation, a triple screening strategy for the observation value range and state domain is proposed to select the optimal ambiguity subset for BIE estimation; 3. To address the problems of low search efficiency and difficult determination of the number of BIE candidate solutions, the SEVB ambiguity search algorithm is improved by introducing weight decay constraints, realizing the dynamic search and determination of BIE candidate solutions; 4. The Laplace distribution is introduced to construct a BIE weight model, solving the problem of unreasonable Gaussian weight allocation caused by significant non-modeling errors in observation values; 5. Based on the BIE solution variance under the Laplace distribution, the BIE fixed solution is verified, further enhancing the reliability of ambiguity fixation.

[0073] First, the present invention provides a method for screening BIE ambiguity subsets. By using a triple screening strategy for the observation value range and state domain to select the optimal ambiguity subset for BIE estimation, the reliability of BIE estimation is significantly improved: The first screening strategy is the double-difference carrier phase inter-frequency difference (PIFD) test. After the ILS estimation fails and switches to BIE estimation, the root mean square (RMS) error of the PIFD corresponding to all ambiguity subsets is calculated. If the error exceeds the set threshold, the subset is excluded to ensure that only reliable ambiguity subsets are retained. After screening in the observation value range, there may still be multiple qualified ambiguity subsets, and further refinement of the subsets is required through state domain screening. The second screening strategy is the fixed solution consistency test. The occurrence frequency of the fixed solution within each ambiguity subset is counted, the fixed solution with the highest occurrence frequency is identified, and the ambiguity subsets inconsistent with this fixed solution are excluded to ensure the high reliability of the fixed solution. The third screening strategy is the candidate solution consistency test, which directly evaluates the difference between the optimal and sub-optimal candidate solutions by calculating the Euclidean distance between them. If the difference is significant, it is considered that the fixed solution of this ambiguity subset is unreliable and it is excluded. Through the triple screening strategy of the above-mentioned observation value range, state domain, and candidate solution consistency test, if no candidate solution passes the test, the floating-point solution is selected to avoid the risk of incorrect BIE estimation; if multiple candidate solutions pass the screening, the ambiguity subset with the optimal geometric configuration is preferentially selected for BIE estimation to enhance the model strength and thus improve the estimation accuracy. Through the method of the present invention, incorrect ambiguity subsets can be effectively excluded, and the reliability of ambiguity resolution can be improved.

[0074] Secondly, the present invention improves the SEVB (Schnorr-Euchner and Viterbo-Biglieri) ambiguity search algorithm. By introducing a weight decay constraint, it realizes the dynamic search and determination of BIE candidate solutions, significantly improving the efficiency of ambiguity resolution. Specifically, the present invention controls the weight of candidate solutions by defining a weight decay factor to ensure that the influence of the excluded candidate solutions on the BIE estimation result can be ignored, thereby limiting the search space of integer candidate solutions to a finite set of integers. The present invention provides search strategies for two scenarios: The first scenario is to directly perform BIE estimation. Similar to the SEVB algorithm, at the initial stage, the search space size is set to infinity, and rounding is performed successively from the nth level to the 1st level to obtain the first integer candidate solution. Whenever a new candidate solution is found, it is necessary to check whether its residual quadratic form is the smallest. If the condition is satisfied, the search space is re-shrunk. This process continues until all qualified candidate solutions are found. The second scenario is to first perform ILS estimation and then perform BIE estimation. The optimal integer candidate solution obtained from the ILS estimation will be used as the starting point for the BIE candidate solution search, and the search space size is also determined accordingly and remains unchanged throughout the search process. All integer candidate solutions contained therein will be included in the BIE estimation. Through the method of the present invention, the candidate solutions required for BIE estimation can be dynamically searched and determined, effectively reducing the computational complexity while ensuring the estimation accuracy.

[0075] Then, the present invention uses a BIE weight model based on the Laplace distribution, significantly improving the reliability of BIE estimation. Specifically, the present invention uses the Laplace distribution to replace the traditional Gaussian distribution to model GNSS observation noise and constructs a BIE weight formula based on the Laplace distribution. Different from the Gaussian distribution, the Laplace distribution can reasonably distribute the weights among candidate solutions by taking the square root of the ambiguity residual quadratic form and dividing by the scale factor, thus effectively solving the problem of unreasonable weight distribution in Gaussian distribution BIE and improving the stability of ambiguity resolution.

[0076] Finally, the present invention uses a reliability checking method based on the variance of the BIE solution to further improve the reliability of BIE estimation. Specifically, based on the variance of the BIE estimator under the Laplace distribution and using the trace of the variance matrix as an index for accuracy evaluation. Only when the accuracy of the BIE solution exceeds the floating-point solution will it be accepted as the output to ensure the reliability of the ambiguity-fixed solution.

[0077] According to the method described in the above embodiments, this embodiment will be further described from the perspective of an ambiguity adaptive resolution system based on ILS and BIE estimation. The ambiguity adaptive resolution system based on ILS and BIE estimation can be specifically implemented as an independent entity, or integrated in an electronic device, which can be a terminal, a server, or other devices. Among them, the terminal can include a tablet computer, a laptop computer, a personal computer (PC), a microprocessing box, or other devices, etc.

[0078] Please refer to Figure 4 , Figure 4 which specifically describes the ambiguity adaptive resolution system provided by the embodiments of the present application, applied to an electronic device. The ambiguity adaptive resolution system based on ILS and BIE estimation may include: An acquisition module, configured to acquire GNSS raw observations; A processing module, configured to construct a pseudo-range and carrier-phase double-difference observation equation based on the GNSS raw observations, and calculate a floating-point solution of the ambiguity parameter using an extended Kalman filter; A resolution module, configured to construct an ambiguity subset based on the floating-point solution of the ambiguity parameter, perform ambiguity fixing on the ambiguity subset through the ILS algorithm to obtain an ILS fixed solution, perform ambiguity fixing verification on the ILS fixed solution. If the verification is passed, the ILS fixed solution is used as the final solution. If the verification is not passed, ambiguity fixing is performed through the BIE algorithm to obtain a BIE fixed solution; The process of obtaining a BIE fixed solution through the BIE algorithm includes: Performing multiple screenings on the ambiguity subset based on the observation value domain and the state domain to obtain an optimal ambiguity subset; Using an improved SEVB algorithm based on weight decay constraint to search the optimal ambiguity subset to obtain a BIE multi-ambiguity candidate solution; Obtaining a BIE fixed solution based on the BIE multi-ambiguity candidate solution, and performing reliability verification on the BIE fixed solution to obtain a final fixed solution.

[0079] In specific implementation, each of the above modules and / or units can be implemented as an independent entity, or can be combined arbitrarily to be implemented as the same or several entities. The specific implementation of each of the above modules and / or units can refer to the foregoing method embodiments, and the specific beneficial effects that can be achieved can also be referred to the beneficial effects in the foregoing method embodiments, which will not be elaborated here.

[0080] In addition, an embodiment of the present application further provides an electronic device, which may be a device such as a computer or a tablet computer. The electronic device can implement the steps in any embodiment of the ambiguity adaptive resolution method based on ILS and BIE estimation provided in the embodiments of the present application. Therefore, the beneficial effects achievable by any of the ambiguity adaptive resolution methods based on ILS and BIE estimation provided in the embodiments of the present invention can be achieved. For details, refer to the previous embodiments and will not be elaborated herein.

[0081] Figure 5 The specific structural block diagram of the electronic device provided in the embodiment of the present invention is shown. The electronic device can be used to implement the ambiguity adaptive resolution method based on ILS and BIE estimation provided in the above embodiments. The electronic device 500 can be a device such as a terminal or a server. Among them, the terminal may include a tablet computer, a laptop computer, a personal computer (PC), a micro processing box, or other devices, etc.

[0082] The RF circuit 510 is used to receive and transmit electromagnetic waves, realizing the mutual conversion between electromagnetic waves and electrical signals, so as to communicate with a communication network or other devices. The RF circuit 510 may include various existing circuit components for performing these functions. For example, an antenna, a radio frequency transceiver, a digital signal processor, an encryption / decryption chip, a subscriber identity module (SIM) card, a memory, and so on. The RF circuit 510 can communicate with various networks such as the Internet, an enterprise intranet, a wireless network or communicate with other devices through a wireless network. The above-mentioned wireless network may include a cellular phone network, a wireless local area network or a metropolitan area network. The above-mentioned wireless network can use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and / or IEEE 802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, and may even include those protocols that have not been developed yet.

[0083] The memory 520 can be used to store software programs and modules, such as the corresponding program instructions / modules in the above embodiments. The processor 580 executes various functional applications and data processing by running the software programs and modules stored in the memory 520, that is, realizes functions such as taking pictures with the front camera, processing the captured images, and switching the display colors of the display content on the display screen. The memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 520 may further include a memory remotely disposed relative to the processor 580, and these remote memories can be connected to the electronic device 500 through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0084] The input unit 530 can be used to receive input digital or character information, and generate keyboards and mice related to user settings and function controls. The display unit 540 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces, and these graphical user interfaces can be composed of graphics, text, icons, videos, and any combination thereof. The display unit 540 may include a display panel 541. Optionally, the display panel 541 can be configured in the form of an LCD (Liquid Crystal Display) or an OLED (Organic Light-Emitting Diode).

[0085] The electronic device 500 can help the user receive requests, send information, etc. through the transmission module 570 (such as a Wi-Fi module), and it provides the user with wireless broadband Internet access. Although the transmission module 570 is illustrated, it can be understood that it does not belong to the essential components of the electronic device 500 and can be omitted entirely within the scope of not changing the essence of the invention as needed.

[0086] The processor 580 is the control center of the electronic device 500, connects various parts of the entire mobile phone through various interfaces and lines, executes various functions of the electronic device 500 and processes data by running or executing the software programs and / or modules stored in the memory 520, and calling the data stored in the memory 520, so as to monitor the electronic device as a whole. Optionally, the processor 580 may include one or more processing cores; in some embodiments, the processor 580 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above modem processor may not be integrated into the processor 580.

[0087] The electronic device 500 further includes a power supply 590 (such as a battery) for powering each component. In some embodiments, the power supply can be logically connected to the processor 580 through a power management system, so as to manage functions such as charging, discharging, and power consumption management through the power management system. The power supply 590 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0088] Specifically, in this embodiment, the display unit of the electronic device is a touch screen or a display. The mobile terminal further includes a memory and one or more programs, where one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations: Obtain GNSS raw observations; Based on the GNSS raw observations, construct a pseudo-range and carrier-phase double-difference observation equation, and use the extended Kalman filter to calculate the floating-point solution of the ambiguity parameter; Based on the floating-point solution of the ambiguity parameter, construct an ambiguity subset, perform ambiguity fixing on the ambiguity subset through the ILS algorithm to obtain the ILS fixed solution, and perform ambiguity fixing verification on the ILS fixed solution. If the verification is passed, the ILS fixed solution is used as the final solution. If the verification is not passed, perform ambiguity fixing through the BIE algorithm to obtain the BIE fixed solution; The process of obtaining the BIE fixed solution by performing ambiguity fixing through the BIE algorithm includes: Perform multiple screenings on the ambiguity subset based on the observation value domain and the state domain to obtain the optimal ambiguity subset; Use an improved SEVB algorithm based on weight decay constraints to search for the optimal ambiguity subset to obtain the BIE multi-ambiguity candidate solution; Based on the BIE multi-ambiguity candidate solution, obtain the BIE fixed solution, and perform reliability verification on the BIE fixed solution to obtain the final fixed solution.

[0089] Specifically, in implementation, the above-mentioned various modules can be implemented as independent entities, or can be combined arbitrarily to be implemented as the same or several entities. For the specific implementation of the above-mentioned various modules, reference can be made to the method embodiments described above, which will not be elaborated here.

[0090] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or by controlling related hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium in which multiple instructions are stored. The instructions can be loaded by a processor to execute the steps of any one of the embodiments of the ambiguity adaptive resolution method based on ILS and BIE estimation provided by the embodiments of the present invention.

[0091] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0092] Since the instructions stored in the storage medium can execute the steps in any one of the embodiments of the ambiguity adaptive resolution method based on ILS and BIE estimation provided by the embodiments of the present invention, the beneficial effects that can be achieved by any of the ambiguity adaptive resolution methods based on ILS and BIE estimation provided by the embodiments of the present invention can be realized. For details, see the previous embodiments and will not be elaborated here.

[0093] The above has introduced in detail an ambiguity adaptive resolution method, system, storage medium, and electronic device based on ILS and BIE estimation provided by the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. An ambiguity adaptive resolution method based on ILS and BIE estimation, characterized in that The method includes: Obtaining GNSS raw observations; Based on the GNSS raw observations, constructing a double-difference observation equation for pseudorange and carrier phase, and using the extended Kalman filter to calculate the floating-point solution of the ambiguity parameter; Based on the floating-point solution of the ambiguity parameter, constructing an ambiguity subset, using the ILS algorithm to fix the ambiguity of the ambiguity subset to obtain the ILS fixed solution, and performing an ambiguity fixing verification on the ILS fixed solution. If the verification is passed, the ILS fixed solution is used as the final solution. If the verification is not passed, the BIE algorithm is used to fix the ambiguity to obtain the BIE fixed solution; The obtaining of the BIE fixed solution by using the BIE algorithm includes: Performing multiple screenings on the ambiguity subset based on the observation value domain and the state domain to obtain the optimal ambiguity subset; Using an improved SEVB algorithm based on weight decay constraint to search for the optimal ambiguity subset to obtain the BIE multi-ambiguity candidate solution; Based on the BIE multi-ambiguity candidate solution, obtaining the BIE fixed solution, and performing a reliability verification on the BIE fixed solution to obtain the final fixed solution.

2. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 1, characterized in that, The constructing of the double-difference observation equation for pseudorange and carrier phase based on the GNSS raw observations and using the extended Kalman filter to calculate the floating-point solution of the ambiguity parameter includes: Based on the GNSS raw observations, constructing a double-difference observation equation for pseudorange and a double-difference observation equation for carrier phase to obtain double-difference observations; Constructing an initial state vector and a covariance matrix, and defining a state transition matrix and an observation matrix; Updating the observations of each epoch through the Kalman filter algorithm to obtain the floating-point solution of the ambiguity parameter.

3. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 2, wherein The constructing of the ambiguity subset based on the floating-point solution of the ambiguity parameter, using the ILS algorithm to fix the ambiguity of the ambiguity subset to obtain the ILS fixed solution, and performing an ambiguity fixing verification on the ILS fixed solution includes: Based on the floating-point solution and the covariance matrix, constructing an ambiguity rejection index; Performing an integer Gauss transformation on the floating-point solution to obtain an integer transformation matrix; Searching for the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution through the SEVB algorithm on the integer transformation matrix; Based on the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution, performing an ambiguity fixing verification. If the verification is not passed, rejecting an ambiguity according to the ambiguity rejection index, and re-executing the steps of performing an integer Gauss transformation and searching through the SEVB algorithm until the ambiguity fixing is successful or the number of remaining ambiguities does not meet the requirements.

4. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 1, characterized in that The performing of the ambiguity fixing verification on the ILS fixed solution includes: Using the Ratio test algorithm and the PIFD test algorithm to perform ambiguity fixing verification on the ILS fixed solution respectively; Among them, the using of the Ratio test algorithm to perform ambiguity fixing verification includes: Calculating the optimal ambiguity and the sub-optimal ambiguity, calculating the Ratio value based on the optimal ambiguity and the sub-optimal ambiguity. When the Ratio value is greater than the preset test threshold, the corresponding ILS fixed solution passes the verification.

5. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 1, characterized in that, Performing multiple screenings on the ambiguity subsets based on the observation value range and the state domain to obtain the optimal ambiguity subsets, including: Performing PIFD tests, fixed solution consistency tests, and candidate solution consistency tests on the ambiguity subsets respectively.

6. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 5, characterized in that Performing a PIFD test on the ambiguity subsets, including: Calculating the PIFD value and the root mean square error of the fixed solution of the ambiguity subsets, and removing the ambiguity subsets corresponding to those exceeding the PIFD threshold and the error threshold. Performing a fixed solution consistency test on the ambiguity subsets, including: Calculating the frequency of occurrence of the fixed solutions of the ambiguity subsets after the PIFD test, identifying the fixed solution with the highest frequency of occurrence, and removing the ambiguity subsets inconsistent with the fixed solution. Performing a candidate solution consistency test on the ambiguity subsets, including: Calculating the Euclidean distance between the optimal integer ambiguity candidate solution and the sub-optimal integer ambiguity candidate solution, and removing the ambiguity subsets with the Euclidean distance greater than the distance threshold.

7. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 1, wherein Using an improved SEVB algorithm based on weight decay constraints to search for the optimal ambiguity subsets to obtain BIE multi-ambiguity candidate solutions, including: Searching for integer ambiguity candidate solutions that satisfy the first condition in the optimal ambiguity subsets, where the first condition is: Among them, represents the weight factor of the $i$-th integer ambiguity candidate solution, is a constant.

8. The ambiguity adaptive resolution method based on ILS and BIE estimation according to claim 1, characterized in that The method further includes: Introducing a Laplace distribution to construct a BIE weight model: Assume GNSS observations Follow a Laplace distribution, and its probability density function can be expressed as: Among them, and represent GNSS observation values and their variance matrix respectively, represents the determinant operator, represents the exponential function operator, and represent the ambiguity parameter and the coefficient matrix of the baseline parameter respectively, represents the scale factor of the Laplace distribution; The ambiguity parameter and the baseline parameter of the BIE estimator can be expressed as: Wherein, Among them, the weight factor satisfies , denotes dimensional integer space.

9. An ambiguity adaptive resolution system based on ILS and BIE estimation, characterized in that, Including: An acquisition module for acquiring GNSS raw observations; A processing module for constructing a pseudo-range and carrier phase double-difference observation equation based on the GNSS raw observations and calculating the floating solution of the ambiguity parameters using an extended Kalman filter; A solution module for constructing ambiguity subsets based on the floating solution of the ambiguity parameters, performing ambiguity fixing on the ambiguity subsets through the ILS algorithm to obtain the ILS fixed solution, performing ambiguity fixing verification on the ILS fixed solution, if the verification is passed, taking the ILS fixed solution as the final solution, if the verification is not passed, performing ambiguity fixing through the BIE algorithm to obtain the BIE fixed solution; Performing ambiguity fixing through the BIE algorithm to obtain the BIE fixed solution, including: Performing multiple screenings on the ambiguity subsets based on the observation value range and the state domain to obtain the optimal ambiguity subsets; Using an improved SEVB algorithm based on weight decay constraints to search for the optimal ambiguity subsets to obtain BIE multi-ambiguity candidate solutions; Obtaining the BIE fixed solution based on the BIE multi-ambiguity candidate solutions, performing reliability verification on the BIE fixed solution, and obtaining the final fixed solution.

10. A computer-readable storage medium, characterized in that, Multiple instructions are stored in the computer-readable storage medium, and the instructions are suitable for being loaded by a processor to execute the ambiguity adaptive solution method based on ILS and BIE estimation according to any one of claims 1 to 8.

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