Partial ambiguity resolving method and device, medium and equipment

Abstract

The invention discloses a partial ambiguity resolving method and device, a medium and equipment, and relates to the technical field of satellite navigation and positioning. Based on the concept that in the partial ambiguity resolving process, after multiple-dimensional ambiguity corresponding parameters are removed, the more accurate the whole-cycle ambiguity resolving based on the remaining ambiguity related parameters is, the more stable the resolving of partial parameters corresponding to partial-dimensional ambiguity subsets is, the invention proposes that in each round of partial ambiguity resolving process, the more accurate the whole-cycle ambiguity resolving is, and the more accurate the whole-cycle ambiguity resolving is, the more stable the resolving of partial parameters corresponding to partial-dimensional ambiguity subsets is. According to the current ambiguity related parameters and the integer ambiguity, and different parameter parts corresponding to ambiguity subsets of different dimensions in the current ambiguity related parameters and the integer ambiguity, respectively solving baseline fixed solutions for multiple times to obtain multiple groups of baseline fixed solutions; the difference between the maximum value and the minimum value in the multiple groups of baseline fixed solutions is compared with the preset threshold value, the stability of the integer ambiguity solution is judged, and the accuracy and the reliability of the partial ambiguity solution are improved by utilizing the statistical characteristics of the ambiguity integer solution.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a partial ambiguity resolution method, device, medium and equipment. Background Art

[0002] With the widespread adoption of the Global Navigation Satellite System (GNSS) in real-time, high-precision positioning applications such as autonomous driving and precision agriculture, fast and accurate ambiguity fixation has become a key component in improving positioning performance. Traditional ambiguity resolution methods often fail in complex observation environments due to observations from low-angle satellites or multipath interference. Partial Ambiguity Resolution (PAR) effectively improves the fix success rate by eliminating unreliable ambiguity subsets.

[0003] In existing technologies, partial ambiguity resolution often uses a dual-discrimination ambiguity resolution strategy, which typically employs a bootstrapping success rate threshold and a ratio test value for joint determination. This approach offers some improvement over a single-test strategy.

[0004] However, in practical applications, the dual discrimination dimension in the current partial ambiguity resolution process is still relatively simple, and the statistical characteristics of the ambiguity integer solution itself are not fully utilized. The error fixation rate is high in complex environments (such as urban canyons), which seriously limits its accuracy in dynamic scenes, and the reliability of the obtained ambiguity fixation solution is low. Summary of the Invention

[0005] Based on this, it is necessary to provide a partial ambiguity resolution method, device, medium and equipment to address the above technical problems.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a partial ambiguity resolution method, comprising:

[0008] Determining an ambiguity dimension based on data received by the receiver, and obtaining ambiguity-related parameters under the ambiguity dimension; the ambiguity-related parameters include a baseline floating-point solution, an ambiguity floating-point solution, a variance-covariance matrix between a baseline component floating-point solution and an ambiguity floating-point solution, and a variance-covariance matrix of the ambiguity floating-point solution;

[0009] De-correlation of ambiguity-related parameters is performed, and the order of the ambiguity conditional variance of each dimension is optimized to reduce the correlation between the ambiguities of each dimension. Bootstrapping success rate test is performed on the current ambiguity conditional variance of each dimension. If it passes, the integer ambiguity is solved and the ratio test is performed.

[0010] If the Ratio test passes, the baseline fixed solution is solved multiple times according to the current ambiguity-related parameters and the integer ambiguity, and the different parameter parts corresponding to the different dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity, to obtain multiple groups of baseline fixed solutions;

[0011] If the difference between the maximum and minimum values in the multiple sets of baseline fixed solutions is less than or equal to the preset threshold, the subset test is passed and the integer ambiguity parameters are fixed correctly. The baseline fixed solution obtained by solving the current ambiguity-related parameters and the integer ambiguity is output.

[0012] If the Bootstrapping success rate test, Ratio test, or subset test fails, the first-dimension ambiguity conditional variance and the parameter part of the corresponding dimension in the ambiguity-related parameters are eliminated according to the order of the ambiguity conditional variance of each dimension, and the ambiguity dimension is reduced. The Bootstrapping success rate test, integer ambiguity solution, Ratio test, and subset test are performed again until the ambiguity dimension is less than or equal to the preset dimension, and the ambiguity floating-point solution is output.

[0013] Optionally, solving the baseline fixed solution multiple times based on the current ambiguity-related parameters and the integer ambiguity, and different parameter parts corresponding to different-dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity specifically includes:

[0014] According to the current ambiguity-related parameters and the integer ambiguity, the baseline fixed solution is solved;

[0015] According to the current ambiguity dimension, the current ambiguity-related parameters and the parameter parts corresponding to the different-dimensional ambiguities in the integer ambiguity are sequentially eliminated to obtain the different parameter parts corresponding to the different-dimensional ambiguity subsets;

[0016] According to different parameter parts corresponding to different dimensional ambiguity subsets, the baseline fixed solution is solved multiple times.

[0017] Optionally, if the difference between the maximum value and the minimum value in the multiple groups of baseline fixed solutions is less than or equal to a preset threshold, then determining that the subset test is passed specifically includes:

[0018] The maximum and minimum values of multiple sets of baseline fixed solutions on the three coordinate components are extracted respectively. If the difference between the maximum and minimum values on the three coordinate components is less than or equal to the preset threshold, the subset test is judged to have passed.

[0019] Optionally, the bootstrapping success rate test on the current ambiguity conditional variance of each dimension specifically includes:

[0020] The Bootstrapping success rate test is performed on the current ambiguity conditional variance of each dimension using the following formula:

[0021]

[0022] Determine whether the Bootstrapping success rate is greater than 0.995. If so, the Bootstrapping success rate test is determined to have passed. If not, the Bootstrapping success rate test is determined to have failed.

[0023] Among them, P s is the Bootstrapping success rate, n is the fuzziness dimension, Φ(·) is the cumulative distribution function of the standard normal distribution, is the ambiguity conditional variance obtained after Cholesky decomposition and integer transformation, Indicates continuous multiplication.

[0024] Optionally, resolving integer ambiguities and performing ratio testing specifically includes:

[0025] Based on the ambiguity correlation parameters after down-correlation, the integer ambiguity is solved through an oscillating search strategy;

[0026] The Ratio test value is determined by the following formula based on the optimal solution of the quadratic form of the integer ambiguity obtained by solving the integer ambiguity and the suboptimal solution of the quadratic form of the integer ambiguity:

[0027]

[0028] Determine whether the Ratio test value is greater than a preset threshold. If so, the Ratio test is determined to have passed; if not, the Ratio test is determined to have failed.

[0029] Among them, Ratio is the Ratio test value, It means solving the corresponding quadratic form, is the floating point solution of ambiguity, a sec is the suboptimal solution of the quadratic form of the integer ambiguity, a min is the optimal solution of the quadratic form of the integer ambiguity, is the variance-covariance matrix of the floating-point solution to the ambiguity.

[0030] The present invention provides a partial ambiguity resolution device, comprising:

[0031] an acquisition module, configured to determine an ambiguity dimension based on data received by the receiver and acquire ambiguity-related parameters under the ambiguity dimension; the ambiguity-related parameters include a baseline floating-point solution, an ambiguity floating-point solution, a variance-covariance matrix between a baseline component floating-point solution and an ambiguity floating-point solution, and a variance-covariance matrix of the ambiguity floating-point solution;

[0032] The dimensionality reduction test module is used to reduce the correlation of ambiguity-related parameters and optimize the order of the ambiguity conditional variances in each dimension to reduce the correlation between the ambiguities in each dimension. The bootstrapping success rate test is performed on the current ambiguity conditional variances in each dimension. If the success rate is passed, the integer ambiguity is solved and a ratio test is performed.

[0033] The subset verification module is used to solve the baseline fixed solution multiple times according to the current ambiguity-related parameters and the integer ambiguity, and the different parameter parts corresponding to the different dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity if the ratio test passes, to obtain multiple groups of baseline fixed solutions;

[0034] A subset verification module is configured to determine that the subset verification has passed and the integer ambiguity parameters have been correctly fixed if the difference between the maximum and minimum values in the multiple sets of baseline fixed solutions is less than or equal to a preset threshold, and output the baseline fixed solution obtained based on the current ambiguity-related parameters and the integer ambiguity resolution;

[0035] The partial iteration module is used to remove the first-dimension ambiguity conditional variance and the parameter part of the corresponding dimension in the ambiguity-related parameters according to the order of the ambiguity conditional variance of each dimension if the Bootstrapping success rate test, Ratio test or subset test fails, and the ambiguity dimension is automatically reduced. The Bootstrapping success rate test, integer ambiguity solution, Ratio test and subset test are then performed again until the ambiguity dimension is less than or equal to the preset dimension, and the ambiguity floating-point solution is output.

[0036] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned partial ambiguity resolution method is implemented.

[0037] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned partial ambiguity resolution method when executing the program.

[0038] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0039] The present invention is based on the concept that after eliminating the corresponding parameters of several dimensional ambiguities in the partial ambiguity resolution process, the more accurate the integer ambiguity resolution based on the remaining ambiguity-related parameters is, the more stable the resolution of partial parameters corresponding to the ambiguity subsets of its partial dimensions should be. It proposes that in each round of partial ambiguity resolution, baseline fixed solutions are solved multiple times according to the current ambiguity-related parameters and the integer ambiguity, and the current ambiguity-related parameters and different parameter parts corresponding to different dimensional ambiguity subsets in the integer ambiguity, to obtain multiple groups of baseline fixed solutions. The stability of the integer ambiguity resolution is judged by comparing the difference between the maximum and minimum values in the multiple groups of baseline fixed solutions with a preset threshold, so as to supplement the Bootstrapping success rate test and the Ratio test, effectively utilize the statistical characteristics of the ambiguity integer solution itself, and improve the accuracy and reliability of the partial ambiguity resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0041] Figure 1 A schematic flow chart of a partial ambiguity resolution method provided by the present invention;

[0042] Figure 2 A schematic diagram of a specific implementation process of partial ambiguity resolution provided by the present invention;

[0043] Figure 3 A schematic diagram of a partial ambiguity resolution device provided by the present invention;

[0044] Figure 4 A schematic diagram of a computer device for implementing a partial ambiguity resolution method provided by the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Currently, most existing technologies employ dual-discrimination ambiguity resolution strategies, such as a combined bootstrapping success rate test and ratio test. While these methods offer some improvement over single-test strategies, they still suffer from the following drawbacks in practical applications: The discriminant dimension is relatively single, and the statistical properties of the integer ambiguity solutions themselves are not fully utilized. However, existing dual-discriminant methods suffer from high false fixation rates in complex environments such as urban canyons, severely limiting their accuracy in dynamic scenes. In summary, the current problem is that the single discriminant dimension leads to low reliability of ambiguity fixation solutions.

[0047] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] Figure 1 The following is a flow chart of a partial ambiguity resolution method according to the present invention, which specifically includes the following steps:

[0049] S101: Determine an ambiguity dimension based on data received by a receiver, and obtain ambiguity-related parameters under the ambiguity dimension; the ambiguity-related parameters include a baseline floating-point solution, an ambiguity floating-point solution, a variance-covariance matrix between a baseline component floating-point solution and an ambiguity floating-point solution, and a variance-covariance matrix of the ambiguity floating-point solution.

[0050] S102: De-correlate the ambiguity-related parameters and optimize the order of the ambiguity conditional variances in each dimension to reduce the correlation between the ambiguities in each dimension. Perform a bootstrapping success rate test on the current ambiguity conditional variances in each dimension. If the test passes, resolve the integer ambiguity and perform a ratio test.

[0051] S103: If the ratio test passes, the baseline fixed solution is solved multiple times according to the current ambiguity-related parameters and the integer ambiguity, and the current ambiguity-related parameters and different parameter parts corresponding to different dimensional ambiguity subsets in the integer ambiguity, to obtain multiple groups of baseline fixed solutions.

[0052] S104: If the difference between the maximum and minimum values in the multiple sets of baseline fixed solutions is less than or equal to the preset threshold, it is determined that the subset test has passed and the integer ambiguity parameters are fixed correctly. The baseline fixed solution obtained based on the current ambiguity-related parameters and the integer ambiguity solution is output.

[0053] S105: If the Bootstrapping success rate test, the Ratio test, or the subset test fails, the first-dimension ambiguity conditional variance and the parameter part of the corresponding dimension in the ambiguity-related parameters are eliminated according to the order of the ambiguity conditional variances of each dimension, and the ambiguity dimension is automatically reduced. The Bootstrapping success rate test, the integer ambiguity solution, the Ratio test, and the subset test are performed again until the ambiguity dimension is less than or equal to the preset dimension, and the ambiguity floating-point solution is output.

[0054] For the sake of convenience, the following description will only be based on the server as the execution subject. The server mentioned in the present invention can be a server set up on a business platform, or a device such as a desktop computer or a laptop computer that can execute the solution of the present invention.

[0055] Figure 2 This is a schematic diagram of a specific implementation flow of partial ambiguity resolution in the present invention. Figure 2 The following example is used for illustration. In one or more embodiments of the present invention, it is assumed that the ambiguity dimension n is determined based on the data received by the receiver. The ambiguity dimension is generally related to the number of satellites involved in the solution and the dimension of the observation equation. Ambiguity reduction, bootstrapping success rate testing, integer ambiguity resolution, and ratio testing all have relatively mature implementation technologies, and this invention only briefly introduces them.

[0056] You can also get the baseline floating point solution obtained in the previous process of positioning solution Ambiguity float solution Variance-covariance matrix between the baseline component float solution and the ambiguity float solution Variance-covariance matrix of the floating-point solution to the ambiguity Let the ambiguity-related parameter elimination index i = 1, and store the result matrix b of the sub-baseline fixed solution all Is an empty set.

[0057] The upper triangular Cholesky decomposition of the variance-covariance matrix of the ambiguity float solution is performed by the following formula (L T DL decomposition):

[0058] in, is the variance-covariance matrix of the floating-point solution to the ambiguity, L is the unit lower triangular matrix and the lower triangular elements are l j,k , D is the conditional variance and the diagonal elements are d j The diagonal matrix of .

[0059] It should be noted that: j,k and d j The calculation formula is:

[0060]

[0061] Where a j,j is the variance value of the j-th row and j-th column of the ambiguity floating-point solution variance-covariance matrix.

[0062] Then, LAMBDA can be used to perform integer transformation, reduce the correlation of ambiguity-related parameters, and optimize the order of the ambiguity conditional variances in each dimension to reduce the correlation between the ambiguities in each dimension. This can be done using the following formula:

[0063]

[0064] in, are the lower triangular elements after integer transformation, The first equation in this mathematical expression is the Gaussian integer transform, and the second equation is the order of the conditional variance of the ambiguity in each dimension.

[0065] In the Gaussian integer transform, when the lower triangular element l j,k When the absolute value of (j>k) is greater than 0.5, the corresponding integer transformation matrix Z j,k :

[0066]

[0067] Among them, I n is an n-dimensional unit matrix, [·] int Represents the rounding operation, e i and e j are n-dimensional coordinate vectors respectively.

[0068] When || l j,k When ||>0.5, Gaussian elimination can be performed according to the following formula:

[0069]

[0070] Where μ=[l j,k ] int , this formula can only guarantee the The absolute value is not greater than 0.5. If you want the values of other elements in the column vector to also satisfy the absolute value not greater than 0.5, you need to perform integer Gaussian transform in sequence according to the above formula.

[0071] In the conditional variance ranking, when It is necessary to calculate the adjacent conditional variance (d j-1 ,d j ) to exchange, and its exchange matrix is P j-1,j :

[0072] in, I j-1 , In-j-1 are the unit matrices of j-1 and nj-1 dimensions respectively.

[0073] After that, the Bootstrapping success rate test can be performed. The server can determine the Bootstrapping success rate of the current ambiguity conditional variance in each dimension by the following formula:

[0074]

[0075] Among them, P s is the Bootstrapping success rate, n is the fuzziness dimension, Φ(·) is the cumulative distribution function of the standard normal distribution, is the ambiguity conditional variance obtained after Cholesky decomposition and integer transformation, Indicates continuous multiplication.

[0076] Then determine whether the Bootstrapping success rate is greater than a success rate threshold (such as 0.995). If so, determine that the Bootstrapping success rate test has passed. If not, determine that the Bootstrapping success rate test has failed. The success rate threshold can be set as needed, and the present invention does not limit this.

[0077] If the Bootstrapping success rate test fails, you can first determine whether the fuzzy dimension is greater than the preset dimension (such as 3). If so, you can press Sequentially remove the parameter part of the corresponding dimension in the current first-dimension ambiguity conditional variance and ambiguity-related parameters, then set the ambiguity dimension n'=n-1, n' is the ambiguity dimension after self-decrement update, and return to the Bootstrapping success rate test until it passes and executes the subsequent process. If the ambiguity dimension is less than or equal to the preset dimension, the process is terminated and the ambiguity floating-point solution is directly output.

[0078] If the Bootstrapping success rate test is passed, the integer ambiguity can be further solved. There are many relatively mature technologies for the specific solution method, which will not be described in detail in the present invention.

[0079] For example, in one or more embodiments of the present invention, an oscillating search strategy (SEVB) may be used to resolve the integer ambiguity a based on the ambiguity correlation parameters after decorrelation. Then, a Ratio test value may be determined using the following formula based on the optimal solution of the quadratic form of the integer ambiguity and the suboptimal solution of the quadratic form of the integer ambiguity obtained by resolving the integer ambiguity:

[0080]

[0081] Among them, Ratio is the Ratio test value, It means solving the corresponding quadratic form, is the floating point solution of ambiguity, a sec is the suboptimal solution of the quadratic form of the integer ambiguity, a min is the optimal solution of the quadratic form of the integer ambiguity, is the variance-covariance matrix of the floating-point solution to the ambiguity.

[0082] Then, determine whether the Ratio test value is greater than or equal to a preset threshold value c: Ratio ≥ c. If so, the Ratio test is considered to have passed; otherwise, the Ratio test is considered to have failed. The preset threshold value c is an empirical threshold value and can typically be set to values such as 1.5 / 2.0 / 2.5 / 3.0, and the present invention is not limited to this.

[0083] If the Ratio test fails, you can first determine whether the fuzzy dimension is greater than the preset dimension (such as 3). If so, you can press Eliminate the parameter part of the corresponding dimension in the current first-dimension ambiguity conditional variance and ambiguity-related parameters in sequence, then set the ambiguity dimension n'=n-1, n' is the ambiguity dimension after self-decrement update, and return to the Bootstrapping success rate test. If the ambiguity dimension is less than or equal to the preset dimension, terminate the process and directly output the ambiguity floating-point solution

[0084] If the Ratio test passes, the baseline fixed solution is solved according to the current ambiguity related parameters and the integer ambiguity by the following formula: And store it in b all .

[0085] Then, according to the current ambiguity dimension, the current ambiguity-related parameters and the parameter parts corresponding to the ambiguities of different dimensions in the integer ambiguity can be sequentially eliminated to obtain different parameter parts corresponding to the ambiguity subsets of different dimensions. For example, in one or more embodiments of the present invention, the initialization can be performed according to the following relationship:

[0086]

[0087] Then, according to the elimination index i of the fuzziness related parameters, the corresponding i rows or corresponding i columns of the sub-subset related parameters after initialization are eliminated, and the related parameters of the sub-subset are reconstructed;

[0088] Since the above-mentioned determination of different parameter parts corresponding to different dimensional ambiguity subsets is carried out in the form of ambiguity sub-subsets of each dimension, therefore, in each iteration, for the current ambiguity dimension n, the current ambiguity-related parameters and integer ambiguities will correspond to n different sub-subset combinations, and the sub-subset parameter part constructed at this time is the corresponding i-th sub-subset parameter part.

[0089] Then, the baseline fixed solution can be solved multiple times according to the different parameter parts corresponding to the different dimensional ambiguity subsets: And store it in b all .

[0090] The solution is repeated multiple times until i equals n. If i is not equal to n, i is incremented and the traversal continues. Finally, the maximum and minimum values of the three coordinate components of the multiple baseline fixed solutions are extracted. If the difference between the maximum and minimum values of the three coordinate components is less than or equal to the preset threshold, the subset test is determined to have passed, and the baseline fixed solution b obtained based on the current ambiguity-related parameters and the integer ambiguity solution is output.

[0091] Specifically, b all The maximum value b of the three coordinate components max and minimum value b min , and then perform the integer ambiguity test in the ambiguity integer domain:

[0092] b max -b min ≤τ

[0093] Here, τ is the threshold set for the integer ambiguity check in the ambiguity integer domain, which can usually be selected in the range of [0.008m, 0.02m]. Other values can also be selected according to actual needs, and the present invention does not impose any limitation on this.

[0094] If the subset test fails, you can first determine whether the fuzzy dimension is greater than the preset dimension (such as 3). If so, you can press Sequentially remove the parameter part of the corresponding dimension in the current first-dimension ambiguity conditional variance and ambiguity-related parameters, and then set the ambiguity dimension n'=n-1, n' is the ambiguity dimension after self-decrement update, and at the same time reset the elimination index i of the ambiguity-related parameters to 1, and return to the Bootstrapping success rate test. If the ambiguity dimension is less than or equal to the preset dimension, the process is terminated and the ambiguity floating-point solution is directly output.

[0095] Of course, the above-mentioned multiple calculations of the baseline fixed solution using sub-subsets are only one feasible implementation method proposed by the present invention. Specifically, the present invention does not impose any restrictions on how to eliminate several dimensional ambiguities from the full set of different dimensional ambiguities to obtain different dimensional ambiguity subsets, and this can be determined as needed.

[0096] based on Figure 1The partial ambiguity resolution method shown in the present invention is based on the concept that after eliminating the corresponding parameters of several dimensional ambiguities in the partial ambiguity resolution process, the more accurate the integer ambiguity resolution based on the remaining ambiguity-related parameters, the more stable the resolution of partial parameters corresponding to the ambiguity subsets of some dimensions should be. It proposes that in each round of partial ambiguity resolution, baseline fixed solutions are solved multiple times based on the current ambiguity-related parameters and the integer ambiguity, and on the current ambiguity-related parameters and different parameter parts corresponding to different dimensional ambiguity subsets in the integer ambiguity, to obtain multiple groups of baseline fixed solutions. The stability of the integer ambiguity resolution is judged by comparing the difference between the maximum and minimum values in the multiple groups of baseline fixed solutions with a preset threshold. This method complements the Bootstrapping success rate test and the Ratio test, effectively utilizes the statistical properties of the ambiguity integer solution itself, and improves the accuracy and reliability of partial ambiguity resolution.

[0097] In summary, the present invention provides a partial ambiguity resolution method based on consistency checking, which effectively improves the success rate of ambiguity fixation and ensures the reliability of ambiguity resolution results. Compared to the dual-discrimination partial ambiguity resolution strategy, this new solution eliminates the problem of a single discriminant dimension when the satellite observation environment degrades, thereby improving the reliability of the integer ambiguity fixation solution.

[0098] When applying the partial ambiguity resolution method provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0099] The above is a partial ambiguity resolution method provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding partial ambiguity resolution device, such as Figure 3 shown.

[0100] Figure 3 A schematic diagram of a partial ambiguity resolution device provided by the present invention, comprising:

[0101] An acquisition module 201 is configured to determine an ambiguity dimension based on data received by a receiver and acquire ambiguity-related parameters under the ambiguity dimension; the ambiguity-related parameters include a baseline floating-point solution, an ambiguity floating-point solution, a variance-covariance matrix between a baseline component floating-point solution and an ambiguity floating-point solution, and a variance-covariance matrix of the ambiguity floating-point solution;

[0102] Dimensionality reduction test module 202 is used to reduce the correlation of ambiguity-related parameters and optimize the order of the ambiguity conditional variances of each dimension to reduce the correlation between the ambiguities of each dimension; perform a bootstrapping success rate test on the current ambiguity conditional variances of each dimension, and if the success rate is passed, resolve the integer ambiguity and perform a ratio test;

[0103] The subset verification module 203 is configured to, if the ratio test passes, solve the baseline fixed solution multiple times based on the current ambiguity-related parameters and the integer ambiguity, and the different parameter parts corresponding to the different-dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity, to obtain multiple sets of baseline fixed solutions;

[0104] The subset verification module 204 is configured to determine that the subset verification has passed and the integer ambiguity parameters are correctly fixed if the difference between the maximum and minimum values in the multiple sets of baseline fixed solutions is less than or equal to a preset threshold, and output the baseline fixed solution obtained based on the current ambiguity-related parameters and the integer ambiguity resolution.

[0105] The partial iteration module 205 is configured to, if the bootstrapping success rate test, ratio test, or subset test fails, remove the first-dimension ambiguity conditional variance and the parameter portion of the corresponding dimension in the ambiguity-related parameters according to the order of the ambiguity conditional variances of each dimension, and decrement the ambiguity dimension. The bootstrapping success rate test, integer ambiguity solution, ratio test, and subset test are then performed again until the ambiguity dimension is less than or equal to the preset dimension, and the ambiguity floating-point solution is output.

[0106] The specific definitions of the partial ambiguity resolution device can be found in the definitions of the partial ambiguity resolution method above and will not be repeated here. Each module in the aforementioned partial ambiguity resolution device may be implemented in whole or in part via software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0107] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 Some ambiguity resolution methods are provided.

[0108] The present invention also provides Figure 4 The structural diagram of the computer equipment shown in FIG. Figure 4As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Some ambiguity resolution methods are provided.

[0109] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0110] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A partial ambiguity resolution method, characterized in that: include: Determine an ambiguity dimension based on satellite data received by the receiver, and obtain ambiguity-related parameters under the ambiguity dimension; the ambiguity-related parameters include a baseline floating-point solution, an ambiguity floating-point solution, a variance-covariance matrix between the baseline and the ambiguity floating-point solution, and a variance-covariance matrix of the ambiguity floating-point solution; Perform LAMBDA down-correlation on the ambiguity-related parameters and optimize the order of the ambiguity conditional variances in each dimension. Perform a Bootstrapping success rate test on the current ambiguity conditional variances in each dimension. If the test passes, solve the integer ambiguity and perform a Ratio test. If the Ratio test passes, the baseline fixed solution is solved multiple times according to the current ambiguity-related parameters and the integer ambiguity, and the different parameter parts corresponding to the different dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity, to obtain multiple groups of baseline fixed solutions; If the difference between the maximum and minimum values in the multiple sets of baseline fixed solutions is less than or equal to the preset threshold, the subset test is passed and the integer ambiguity parameters are fixed correctly. The baseline fixed solution obtained by solving the current ambiguity-related parameters and the integer ambiguity is output. If the Bootstrapping success rate test, Ratio test, or subset test fails, the first-dimension ambiguity conditional variance and the parameter part of the corresponding dimension in the ambiguity-related parameters are eliminated according to the order of the ambiguity conditional variance of each dimension, and the ambiguity dimension is reduced. The Bootstrapping success rate test, integer ambiguity solution, Ratio test, and subset test are performed again until the ambiguity dimension is less than or equal to the preset dimension, and the ambiguity floating-point solution is output.

2. The method for resolving partial ambiguity according to claim 1, wherein: Solving the baseline fixed solution multiple times based on the current ambiguity-related parameters and the integer ambiguity, and different parameter parts corresponding to different dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity, specifically includes: According to the current ambiguity-related parameters and the integer ambiguity, the baseline fixed solution is solved; According to the current ambiguity dimension, the current ambiguity-related parameters and the parameter parts corresponding to the different-dimensional ambiguities in the integer ambiguity are sequentially eliminated to obtain the different parameter parts corresponding to the different-dimensional ambiguity subsets; According to different parameter parts corresponding to different dimensional ambiguity subsets, the baseline fixed solution is solved multiple times.

3. The partial ambiguity resolution method according to claim 1, wherein: If the difference between the maximum value and the minimum value in the multiple sets of baseline fixed solutions is less than or equal to a preset threshold, then the subset test is determined to be passed, specifically including: The maximum and minimum values of multiple sets of baseline fixed solutions on the three coordinate components are extracted respectively. If the difference between the maximum and minimum values on the three coordinate components is less than or equal to the preset threshold, the subset test is judged to have passed.

4. The method for resolving partial ambiguity according to claim 1, wherein: The bootstrapping success rate test for the current fuzzy conditional variance of each dimension specifically includes: The Bootstrapping success rate of the current ambiguity conditional variance in each dimension is determined by the following formula: Determine whether the Bootstrapping success rate is greater than 0.

995. If so, the Bootstrapping success rate test is determined to have passed. If not, the Bootstrapping success rate test is determined to have failed. Among them, P s is the Bootstrapping success rate, n is the fuzziness dimension, Φ(·) is the cumulative distribution function of the standard normal distribution, is the ambiguity conditional variance obtained after Cholesky decomposition and integer transformation, Indicates continuous multiplication.

5. The method for resolving partial ambiguity according to claim 1, wherein: The solving of the integer ambiguity and performing the ratio test specifically include: Based on the ambiguity correlation parameters after down-correlation, the integer ambiguity is solved through an oscillating search strategy; The Ratio test value is determined by the following formula based on the optimal solution of the quadratic form of the integer ambiguity obtained by solving the integer ambiguity and the suboptimal solution of the quadratic form of the integer ambiguity: Determine whether the Ratio test value is greater than a preset threshold. If so, the Ratio test is determined to have passed; if not, the Ratio test is determined to have failed. Among them, Ratio is the Ratio test value, It means solving the corresponding quadratic form, is the floating point solution of ambiguity, a sec is the suboptimal solution of the quadratic form of the integer ambiguity, a min is the optimal solution of the quadratic form of the integer ambiguity, is the variance-covariance matrix of the floating-point solution to the ambiguity.

6. A partial ambiguity resolution device, characterized in that: include: an acquisition module, configured to determine an ambiguity dimension based on data received by the receiver and acquire ambiguity-related parameters under the ambiguity dimension; the ambiguity-related parameters include a baseline floating-point solution, an ambiguity floating-point solution, a variance-covariance matrix between a baseline component floating-point solution and an ambiguity floating-point solution, and a variance-covariance matrix of the ambiguity floating-point solution; Dimensionality reduction test module, used to reduce the correlation of ambiguity related parameters and optimize the order of ambiguity conditional variance in each dimension to reduce the correlation between ambiguities in each dimension; Perform a Bootstrapping success rate test on the current ambiguity conditional variance of each dimension. If it passes, solve the integer ambiguity and perform a Ratio test. The subset verification module is used to solve the baseline fixed solution multiple times according to the current ambiguity-related parameters and the integer ambiguity, and the different parameter parts corresponding to the different dimensional ambiguity subsets in the current ambiguity-related parameters and the integer ambiguity if the ratio test passes, to obtain multiple groups of baseline fixed solutions; A subset verification module is configured to determine that the subset verification has passed and the integer ambiguity parameters have been correctly fixed if the difference between the maximum and minimum values in the multiple sets of baseline fixed solutions is less than or equal to a preset threshold, and output the baseline fixed solution obtained based on the current ambiguity-related parameters and the integer ambiguity resolution; The partial iteration module is used to remove the first-dimension ambiguity conditional variance and the parameter part of the corresponding dimension in the ambiguity-related parameters according to the order of the ambiguity conditional variance of each dimension if the Bootstrapping success rate test, Ratio test, or subset test fails, and the ambiguity dimension is automatically reduced. The Bootstrapping success rate test, integer ambiguity solution, Ratio test, and subset test are then performed again until the ambiguity dimension is less than or equal to the preset dimension, and the ambiguity floating-point solution is output.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the program.

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