Ambiguity threshold adaptive adjustment method based on aperture estimation

Abstract

The invention relates to the technical field of navigation and positioning, in particular to an ambiguity threshold adaptive adjustment method based on aperture estimation, which comprises the following steps: constructing a carrier phase observation equation, and calculating an ambiguity floating point solution; integer estimation is carried out on the floating point solution of the ambiguity, and candidate integer solutions are generated; based on an aperture estimation theory, adaptively adjusting a ratio test dynamic threshold; performing ratio test on the candidate integer solution to confirm an ambiguity fixed result; and substituting the determined integer ambiguity parameter back into the positioning model, correcting a positioning solution, and outputting a high-precision positioning result. The threshold value is adjusted by controlling the ambiguity fixing failure rate, and ambiguity error fixing is effectively reduced. According to the change of the satellite number and the model intensity, the ratio threshold is dynamically adjusted, and the reliability of an ambiguity fixing result in high-precision positioning is ensured. The method can significantly reduce the positioning deviation, improves the positioning precision, and solves a problem that an existing ambiguity detection method is lower in positioning precision.

Description

Technical Field

[0001] The present invention relates to the field of navigation and positioning technology, and in particular to a method for adaptively adjusting an ambiguity threshold based on aperture estimation. Background Art

[0002] In the Global Navigation Satellite System (GNSS), carrier phase positioning technology is a key method for achieving high-precision positioning. However, there is an integer unknown in the carrier phase measurement process, namely the integer ambiguity. Accurately and quickly determining the integer ambiguity is a necessary condition for achieving GNSS precise positioning. The resolution of integer ambiguity is usually divided into four steps: first, the floating-point solution of the ambiguity and other parameters is calculated through the adjustment model; second, the floating-point solution of the ambiguity is estimated as an integer to obtain a candidate integer solution; then, the candidate integer solution is confirmed; finally, the confirmed integer solution is used as the true value of the ambiguity for back substitution to correct the positioning solution. Due to various errors in the carrier phase observation process, the candidate integer solution may be incorrect, so the ambiguity verification step is crucial.

[0003] Traditional ambiguity testing methods primarily determine whether the optimal solution passes the test by evaluating the difference between the optimal and suboptimal candidate solutions. Common methods include the F-ratio test, the R-ratio test, the W-ratio test, and the difference test. Among these, the ratio test is one of the most commonly used methods. It compares the quadratic residual ratio of the optimal and suboptimal solutions with a given threshold to determine the correctness of the optimal solution.

[0004] However, the ratio test method is not a test for the correctness of the integer least squares solution, and the fixed-threshold ratio test method carries the risk of incorrectly fixing the ambiguity. Although integer aperture estimation theory proposes a threshold determination method based on a fixed failure rate, which can control the failure rate by controlling the aperture size, this threshold still does not directly reflect the accuracy of the positioning solution. Therefore, existing ambiguity test methods still have room for improvement in ensuring the accuracy of ambiguity fixation and positioning precision. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for adaptively adjusting the ambiguity threshold based on aperture estimation, aiming to solve the problem of low positioning accuracy of existing ambiguity detection methods.

[0006] To achieve the above object, the present invention provides a method for adaptively adjusting a fuzzy threshold based on aperture estimation, comprising the following steps:

[0007] Obtain carrier phase observation data;

[0008] Construct the carrier phase observation equation and calculate the floating-point solution of ambiguity;

[0009] Perform integer estimation on the floating-point solution of the ambiguity to generate candidate integer solutions;

[0010] Based on aperture estimation theory, the ratio test dynamic threshold is adaptively adjusted;

[0011] Perform ratio tests on candidate integer solutions to confirm the ambiguity fixation results;

[0012] The confirmed integer ambiguity parameters are substituted back into the positioning model to correct the positioning solution and output a high-precision positioning result.

[0013] Among them, in "obtaining carrier phase observation data", the carrier phase observation data includes carrier phase observation value, satellite position information, and receiver position information.

[0014] Among them, in "constructing the carrier phase observation equation and calculating the ambiguity floating-point solution", the ambiguity floating-point least squares method is used to adjust the carrier phase observation equation to obtain the solution.

[0015] Among them, in "constructing the carrier phase observation equation and calculating the ambiguity floating-point solution", the ambiguity floating-point least squares method is used to adjust the carrier phase observation equation to obtain the solution.

[0016] Among them, in "Adaptive adjustment of ratio test dynamic threshold based on aperture estimation theory", the ratio threshold is dynamically adjusted by controlling the ambiguity fixation failure rate to ensure that the threshold can be adjusted in real time when the number of satellites and model strength change.

[0017] Among them, in "performing ratio test on candidate integer solutions and confirming the ambiguity fixing result", the dynamic ratio threshold method is used to test the candidate integer solutions to determine the difference between the optimal solution and the suboptimal solution; if the candidate integer solution passes the test, proceed to the next step; otherwise, the ambiguity search and test are performed again.

[0018] Among them, in "substituting the confirmed integer ambiguity parameters back into the positioning model, correcting the positioning solution, and outputting high-precision positioning results", the confirmed integer ambiguity parameters are transformed back into the original space through inverse transformation, and then back-substituted into the positioning model, the positioning solution is corrected, and finally a high-precision positioning result is obtained.

[0019] The present invention proposes an adaptive ambiguity threshold adjustment method based on aperture estimation, comprising the following steps: acquiring carrier phase observation data; constructing a carrier phase observation equation to calculate a floating-point solution to the ambiguity; performing integer estimation on the floating-point solution to generate candidate integer solutions; adaptively adjusting a dynamic threshold for ratio verification based on aperture estimation theory; performing a ratio verification on the candidate integer solutions to confirm the ambiguity fixation result; back-substituting the confirmed integer ambiguity parameters into the positioning model to correct the positioning solution and output a high-precision positioning result. The present invention adjusts the threshold by controlling the ambiguity fixation failure rate, effectively reducing the occurrence of ambiguity fixation errors. The method dynamically adjusts the ratio threshold based on changes in the number of satellites and model strength, ensuring the reliability of ambiguity fixation results in high-precision positioning. Experimental results demonstrate that compared with traditional fixed-threshold methods, the present invention significantly reduces positioning deviation and improves positioning accuracy, demonstrating improved robustness, particularly when the number of satellites is small or the model strength is low. This overcomes the low positioning accuracy problem of existing ambiguity verification methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 It is a flow chart of a method for adaptively adjusting blur threshold based on aperture estimation of the present invention;

[0022] Figure 2 is a schematic diagram of aperture estimation of the present invention;

[0023] Figure 3 It is a schematic diagram of the two-dimensional z-domain ambiguity fixation of the present invention;

[0024] Figures 4 to 6 Schematic diagram of the relationship between the ambiguity fixation failure rate, the Ratio threshold, and the fixation rate under different numbers of satellites of the present invention;

[0025] Figures 7 and 8 2 is a schematic diagram comparing positioning results of a fixed Ratio threshold and a dynamic Ratio threshold according to the present invention;

[0026] Figures 9 and 10 3 is a schematic diagram comparing positioning deviations of a fixed Ratio threshold and a dynamic Ratio threshold according to the present invention. DETAILED DESCRIPTION

[0027] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0028] See also Figures 1 to 10 The present invention provides a method for adaptively adjusting a fuzzy threshold based on aperture estimation, comprising the following steps:

[0029] S1 obtains carrier phase observation data;

[0030] The carrier phase observation data includes carrier phase observation values, satellite position information, and receiver position information.

[0031] Specifically, a GNSS receiver receives carrier phase observation data from multiple satellites. This data includes carrier phase observations, satellite position information, and receiver position information. The received observation data undergoes preprocessing, including cycle slip detection and repair, and multipath mitigation, to ensure data integrity and reliability. This preprocessed data is then stored in a computing system, providing the foundation for subsequent ambiguity resolution.

[0032] S2 constructs the carrier phase observation equation and calculates the floating-point solution of the ambiguity;

[0033] The ambiguity floating point least square method is used to adjust the carrier phase observation equation to obtain the result.

[0034] Specifically, according to the carrier phase observation equation, the following mathematical model is constructed:

[0035] in, is the carrier phase observation value, N is the ambiguity parameter vector, b is the baseline vector, A and B are the corresponding coefficient matrices, V is the observation residual vector, is the variance-covariance matrix of the observations. According to the least squares theory:

[0036]

[0037] S3 performs integer estimation on the floating-point solution of the ambiguity and generates a candidate integer solution;

[0038] The ambiguity floating point least square method is used to adjust the carrier phase observation equation to obtain the result.

[0039] Specifically, in the above case, the unknown parameters N and b are estimated. Without adding ambiguity constraints, the floating-point solution of the ambiguity without integer characteristics can be obtained first. The integer ambiguity N0 can be searched through the least squares theory, which can be converted to:

[0040]

[0041] Among them, N is the floating-point solution of ambiguity without integer characteristics, Q N is the variance-covariance matrix of the floating-point ambiguity. Due to the large correlation between the ambiguity parameters, the variance-covariance matrix and the ambiguity parameters are transformed into the new space through integer Gaussian transformation:

[0042]

[0043] in, are the floating point ambiguity, integer ambiguity and variance-covariance matrix in the new space respectively. At this time, the search condition for ambiguity in the new space becomes:

[0044]

[0045] S4 is based on aperture estimation theory and adaptively adjusts the ratio test dynamic threshold;

[0046] By controlling the ambiguity fixation failure rate and dynamically adjusting the ratio threshold, we ensure that the threshold can be adjusted in real time when the number of satellites and model strength change.

[0047] Specifically, based on aperture estimation theory, the dynamic threshold for the ratio test is adaptively adjusted. The Ratio value in real-time positioning should not be fixed. The Ratio threshold in this patented invention changes in real time as the number of satellites and model strength change. When there are many available satellites and the model strength is very high, the Ratio threshold should be well below 3.0. When there are few satellites and the model strength is low, the Ratio threshold is increased to prevent false positives.

[0048] S5 performs a ratio test on the candidate integer solutions to confirm the ambiguity fixation result;

[0049] The dynamic ratio threshold method is used to test the candidate integer solutions and determine the difference between the optimal solution and the suboptimal solution. If the candidate integer solution passes the test, the next step is entered; otherwise, the fuzzy search and test are repeated.

[0050] Specifically, a ratio test is performed on the candidate integer solutions to confirm the ambiguity fixation result. The classic ambiguity acceptance test method is the ratio test method, such as F-Ratio or R-Ratio. The R-Ratio test is similar to the F-Ratio test and uses the ratio of the residual quadratic form of the optimal solution and the suboptimal solution for testing, which is expressed as:

[0051]

[0052] Among them, N, N sec ,N min are the floating-point solution of ambiguity without integer characteristics, the suboptimal solution of ambiguity, and the optimal solution, respectively. c is the aperture pull-in region parameter value, which is usually fixed to 2 or 3. After z-transformation, the normalized region of the ratio aperture estimation is:

[0053]

[0054] by For example, the integration area can be transformed into:

[0055]

[0056] Use Matlab to plot the inequality group as follows Figure 2 As shown, the green area is The normalized region is the intersection of the inequality groups, and the colored ellipse represents the case where the inequalities take equal signs.

[0057] Floating-point solution and integer solutions There is a many-to-one mapping relationship between them. Since the floating-point ambiguity covers the entire number domain, it also covers the entire Z domain after the Z transformation, and the regular domain of the ambiguity has Z-translation invariance.

[0058] Taking two-dimensional Nmin=0 as an example, its complete regular domain is as follows Figure 3 As shown, the regular domain is divided into the following areas:

[0059] Red areas indicate that the blur is correctly fixed

[0060] Black areas indicate ambiguity false rejections

[0061] White areas indicate correct rejection of ambiguity

[0062] Yellow area; indicates incorrect fixation of ambiguity (ambiguity is fixed normally during ambiguity resolution, but the fixed result is not the actual value of the ambiguity)

[0063] The four results correspond to the success rate P S , failure rate P f , correct rejection rate P fa , wrongly reject P cr :

[0064]

[0065] Among them, f N (x) is a floating point solution The probability density function of :

[0066]

[0067] The ambiguity fixation probability is:

[0068] P fix =P s -P f

[0069] The ambiguity fixation failure rate is defined as:

[0070]

[0071] S6 substitutes the confirmed integer ambiguity parameters back into the positioning model, corrects the positioning solution, and outputs a high-precision positioning result.

[0072] The confirmed integer ambiguity parameters are transformed back into the original space through inverse transformation and back-substituted into the positioning model to correct the positioning solution and finally obtain a high-precision positioning result.

[0073] Specifically, the correct ambiguity parameters found can be transformed back to the original space through inverse transformation:

[0074]

[0075] The positioning solution is then back-substituted into the positioning model to correct the positioning solution, ultimately achieving a high-precision positioning result. This step corrects the positioning solution by back-substituting the confirmed integer ambiguity parameters into the positioning model, ensuring high positioning accuracy. The receiver's high-precision position coordinates are calculated based on the corrected positioning solution.

[0076] Example:

[0077] Example 1: To analyze and verify the effectiveness of the proposed algorithm, we selected measured data for experimental verification. A single-frequency 906B-M receiver was used for data acquisition, collecting 5231 epochs of data. The distance between the base station and the rover was 2 meters, and the data were collected on the rooftop of the library at the Jinjiling Campus of Guilin University of Electronic Technology.

[0078] For a low-cost dual-frequency dual-mode receiver, when the ambiguity fixation failure rate (FFRT) is 0.1%, the model strength of the least squares method is 0.005, 0.035, and 0.085 respectively. The new ratio threshold and FFRT are compared under different satellite numbers. The experimental results are shown in the figure. Figures 4 to 6 shown.

[0079] The monitoring point coordinates use the ENU coordinate system, the initial ENU is (-470.3961, -1135.2127, 71.0545), the solution mode is Kinematic, the solution frequency is dual-frequency L1, the satellite elevation angle cutoff angle is 15°, the ambiguity fixation mode is Fix and Hold, the satellite signal-to-noise ratio cutoff is 35dBHz, and the cycle slip recovery strategy and delay strategy are used. Only the Ratio threshold is changed, and a fixed Ratio threshold of 3 is used for comparison with the dynamic Ratio threshold. The solution results are as follows: Figures 7 and 8 shown.

[0080] Depend on Figures 7 and 8 From the positioning results, there is no obvious difference in positioning accuracy, and the ambiguity fixation is shown in Table 1.

[0081]

[0082] Table 1

[0083] As shown in Table 1, the ambiguity fixation rate using the dynamic ratio is slightly reduced. To verify the accuracy of the ambiguity fixation, the deviation of the fixed ratio and the dynamic ratio fixed solution is plotted. The positioning deviation of the fixed ratio threshold and the dynamic ratio threshold is compared. Figures 9 and 10 shown.

[0084] In summary, the present invention relates to an adaptive adjustment method for ambiguity threshold based on aperture estimation, which aims to solve the problem of reduced positioning accuracy caused by integer ambiguity fixation errors in the global satellite navigation system (GNSS). Traditional ambiguity detection methods, such as ratio detection methods, usually use fixed thresholds, which are prone to introduce ambiguity fixation errors, affecting positioning accuracy.

[0085] Based on integer aperture estimation theory, an adaptive ratio dynamic threshold determination method is proposed. This method adjusts the threshold by controlling the ambiguity fixation failure rate, effectively reducing the occurrence of ambiguity fix errors. This method dynamically adjusts the ratio threshold based on changes in the number of satellites and model strength, ensuring the reliability of ambiguity fixation results in high-precision positioning. Experimental results show that compared with traditional fixed threshold methods, this method can significantly reduce positioning deviation and improve positioning accuracy, especially in situations with a small number of satellites or low model strength, demonstrating better robustness. This method is suitable for the field of GNSS high-precision navigation and positioning and has broad application prospects.

[0086] The above disclosure is merely a preferred embodiment of the method for adaptively adjusting the blur threshold based on aperture estimation of the present invention. It is certainly not intended to limit the scope of the present invention. A person skilled in the art will understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for adaptively adjusting blur threshold based on aperture estimation, characterized in that: The following steps are involved: Obtain carrier phase observation data; Construct the carrier phase observation equation and calculate the floating-point solution of ambiguity; Perform integer estimation on the floating-point solution of the ambiguity to generate candidate integer solutions; Based on aperture estimation theory, the ratio test dynamic threshold is adaptively adjusted; Perform ratio tests on candidate integer solutions to confirm the ambiguity fixation results; The confirmed integer ambiguity parameters are substituted back into the positioning model to correct the positioning solution and output a high-precision positioning result.

2. The method for adaptively adjusting blur threshold based on aperture estimation according to claim 1, wherein: In "obtaining carrier phase observation data", the carrier phase observation data includes carrier phase observation values, satellite position information, and receiver position information.

3. The method for adaptively adjusting blur threshold based on aperture estimation according to claim 1, wherein: In "Constructing a carrier phase observation equation and calculating an ambiguity floating-point solution", the ambiguity floating-point least squares method is used to adjust the carrier phase observation equation to obtain the solution.

4. The method for adaptively adjusting blur threshold based on aperture estimation according to claim 1, wherein: In "Performing Integer Estimation of Floating-Point Solutions of Ambiguities to Generate Candidate Integer Solutions," integer estimation is performed using integer least squares theory to search for candidate integer solutions of the ambiguities.

5. The method for adaptively adjusting blur threshold based on aperture estimation according to claim 1, wherein: In the paper "Adaptive adjustment of dynamic threshold of ratio test based on aperture estimation theory", the ratio threshold is dynamically adjusted by controlling the ambiguity fixation failure rate, ensuring that the threshold can be adjusted in real time when the number of satellites and model strength change.

6. The method for adaptively adjusting blur threshold based on aperture estimation according to claim 1, wherein: In "Ratio testing of candidate integer solutions to confirm ambiguity fixation," a dynamic ratio threshold method is used to test candidate integer solutions and determine the difference between the optimal solution and the suboptimal solution. If the candidate integer solution passes the test, proceed to the next step; Otherwise, redo the ambiguity search and check.

7. In the method for adaptively adjusting ambiguity thresholds based on aperture estimation according to claim 1, in the step of "back-substituting the confirmed integer ambiguity parameters into the positioning model, correcting the positioning solution, and outputting a high-precision positioning result," the confirmed integer ambiguity parameters are transformed back into the original space through an inverse transformation, and then back-substituted into the positioning model to correct the positioning solution, ultimately obtaining a high-precision positioning result.

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