Ambiguity transmission method, system and equipment based on quality control and medium

Abstract

The invention discloses an ambiguity transmission method, system and device based on quality control and a medium, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: obtaining floating point ambiguity; the decimal part of the floating point ambiguity is checked, and when the decimal part is larger than a set threshold value, fixing is not carried out; otherwise, carrying out ambiguity fixation on the floating point ambiguity, carrying out Ratio test on the obtained candidate fixed solution, and if the ambiguity is smaller than a set threshold value, keeping the floating point solution by the epoch; otherwise, performing quality control on the fixed ambiguity; checking the fixed ambiguity after the quality is fixed, if the number of the fixed ambiguity is smaller than a set threshold value, keeping a floating point solution for the epoch, otherwise, establishing a pseudo observation equation according to the fixed ambiguity to carry out filtering solution, updating the ambiguity parameter of the current epoch, and then inheriting to the epoch of a subsequent continuous arc segment without cycle slip; the method aims at solving the problem of decimeter-level positioning errors caused by transmission of wrong fixed ambiguity in a traditional FH mode.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation and positioning, and particularly relates to a method, system, device and medium for ambiguity transfer based on quality control. Background Technique

[0002] Precise Point Positioning (PPP) is a high-precision positioning technology based on the Global Navigation Satellite System (GNSS). By using precise satellite orbits and clock offset products, PPP eliminates most of the error sources in traditional single-point positioning (such as satellite orbit errors, clock offset errors, etc.), thereby achieving high-precision positioning. Traditional PPP uses a floating-point solution, which has the disadvantage of slow convergence speed and limits the application range of PPP. Compared with the floating-point solution of ambiguities, the fixed solution of PPP ambiguities can significantly shorten the convergence time and improve the positioning accuracy to a certain extent. The key lies in whether the integer ambiguities can be fixed quickly and reliably. Only when the ambiguities are correctly fixed, the phase observations become absolute observables similar to pseudorange, but the ranging accuracy is up to centimeter level or even millimeter level.

[0003] Therefore, ambiguity resolution (AR) has become a breakthrough point to accelerate the convergence speed of PPP, and many domestic and foreign scholars have conducted in-depth research on it. First, ignoring the integer characteristics of ambiguities, the PPP floating-point solution is executed. Then, the integer ambiguity resolution is divided into two steps: ambiguity calculation and verification. The Least-Squares Ambiguity Decorrelation Adjustment (LAMBDA) method is the most commonly used ambiguity calculation method and can be used to search for integer candidate solutions. Ambiguity verification methods mainly include fixed threshold ratio-test, bootstrapping success rate test, ratio-test with fixed failure rate, etc.; due to the simplicity and effectiveness of the fixed threshold ratio-test, most scholars and software mainly use the fixed threshold ratio-test for verification. After passing the ambiguity verification, the fixed solution update formula or the way of integer ambiguity back substitution calculation can be used to obtain the fixed solutions of parameters of interest such as position.

[0004] For the widely used real-time high-precision PPP-AR solution, how to use and inherit the ambiguity fixed solution information of the current epoch is also one of the key steps, which has a significant impact on the fixation rate and accuracy of the fixed solution. In the commonly used Fixand Hold (FH) mode, integer ambiguities can be used to impose strict constraints on the floating-point ambiguity estimation. The fixed ambiguities will be maintained and inherited in subsequent continuous arc segments without cycle slips to improve the continuity and reliability of positioning. This method is mostly used in application scenarios with high requirements for positioning accuracy and stability, such as high-precision measurement and precise navigation. According to the research of Takasu and Yasuda in 2010, in this mode, the E / N / U direction errors are 1.1 cm, 1.9 cm, and 3.5 cm respectively. The fixation rate is increased to 99.3%. This mode is effective for improving the initialization performance and increasing the ambiguity rate. However, in the FH ambiguity fixation mode, there is a situation where the subsequent positioning results diverge due to incorrect ambiguity fixation in a certain epoch, resulting in poor positioning accuracy. Therefore, some scholars designed a dual-parallel processing line, including an "updating" processing line and an FH processing line. The "updating" processing line is responsible for updating the copy of the filtering state using all successfully fixed ambiguities. Its main purpose is to output navigation and positioning parameters in each cycle, while the FH processing line aims to directly update the filtering state with the "correctly" fixed ambiguities and transfer this tight constraint to the next cycle.

[0005] As can be seen from the above, the existing FH method cannot fully distinguish incorrect fixed ambiguities, and the incorrectly fixed ambiguities will lead to positioning deviations of decimeters or even larger. The solution result is even worse than the floating-point solution of ambiguities, thus reducing the positioning accuracy of the satellite system. Summary of the Invention

[0006] Aiming at the situation that the existing technology cannot fully distinguish incorrectly fixed ambiguities, and the incorrectly fixed ambiguities will lead to positioning deviations of decimeters or even larger, the present invention proposes a method, system, device and medium for ambiguity transfer based on quality control. By implementing a quality control strategy, low-precision fixed ambiguities are effectively screened out to ensure that only high-quality ambiguities are transferred to the next epoch, thus greatly improving the problems existing in the prior art.

[0007] A method for ambiguity transfer based on quality control includes the following steps:

[0008] Receive satellite system observation data, construct a precise point positioning (PPP) observation model, solve the observation model to obtain floating-point ambiguities, and perform ambiguity fixation on the floating-point ambiguities to obtain fixed ambiguities;

[0009] Check the cut-off elevation angle of the satellite corresponding to the fixed ambiguity. When the cut-off elevation angle is less than the set threshold, mark the fixed ambiguity of this satellite as untrusted; otherwise, check the signal-to-noise ratio of the frequencies of the L1 and L2 carriers of this satellite. When the signal-to-noise ratio of the frequencies of the L1 and L2 carriers is lower than the set threshold, mark the fixed ambiguity of this satellite as untrusted; otherwise, filter the fixed ambiguity corresponding to the satellite marked as downweighted during the solution of the observation model; check the phase residual during the filtered satellite's filter solution. When the satellite phase residual is larger than the set threshold, exclude this fixed ambiguity; otherwise, judge whether the number of consecutive fixed epochs of this satellite is greater than the set threshold. If the number of consecutive fixed epochs is less than the set threshold, mark the fixed ambiguity of this satellite as untrusted; after excluding all the fixed ambiguities marked as untrusted, obtain the remaining fixed ambiguities;

[0010] Establish a pseudo-observation equation based on the remaining fixed ambiguities to update the ambiguity parameters of the current epoch and inherit them to the epochs of the subsequent continuous arcs without cycle slips in this satellite system.

[0011] Furthermore, receiving the observation data of the satellite system, constructing a precise point positioning PPP observation model, solving the observation model to obtain the floating-point ambiguity, and performing ambiguity fixing on the floating-point ambiguity to obtain the fixed ambiguity, specifically including the following steps:

[0012] Receive the observation data of the satellite system and construct a precise point positioning PPP observation model;

[0013] Solve the observation model using the Kalman filtering method to obtain the floating-point ambiguity;

[0014] Check the decimal part of the floating-point ambiguity. When the decimal is greater than or less than the set threshold, no ambiguity fixing is performed; otherwise, fix the floating-point ambiguity through the least squares ambiguity decorrelation adjustment method LAMBDA to obtain the fixed ambiguity.

[0015] Furthermore, the receiving of the observation data of the satellite system and the construction of the precise point positioning PPP observation model are expressed as:

[0016]

[0017] Among them, is the geometric distance between the satellite and the receiver, in meters; c is the speed of light; dt r and dt s are the receiver and satellite clock errors respectively, in seconds; is the tropospheric delay, in meters; λ IF represents the carrier wavelength of the ionosphere-free observation combination; B r,IF and They are the fractional carrier phase biases UPD at the receiver side and the satellite side respectively; is the integer ambiguity; b r,IF is the code pseudorange hardware delay between the receiver antenna and the signal correlator; is the code pseudorange hardware delay between the signal transmitter on the satellite side and the satellite antenna; represents the pseudorange measurement error; represents the carrier phase measurement error; and represent the pseudorange observation value and the carrier phase observation value respectively.

[0018] Furthermore, the floating-point ambiguity is fixed by the least-squares ambiguity decorrelation adjustment method LAMBDA, and the generated fixed ambiguity is specifically expressed as:

[0019]

[0020] where, represents the IF combined floating-point ambiguity; D nl represents the variance of the NL ambiguity; represents the fixed wide-lane WL ambiguity; D IF is the non-differenced IF combined floating-point ambiguity covariance matrix; f 1 and f 2 represent the frequencies of the L1 and L2 carriers respectively.

[0021] Furthermore, the pseudo-observation equation y is established according to the remaining fixed ambiguities, expressed as:

[0022] y = Hx + v v ∼ N(0, R s )

[0023] where,

[0024]

[0025] R s = diag(δ s 2 , δ s 2 , …)

[0026] In the formula, H represents the coefficient matrix of the pseudo-observation equation; x represents the fixed satellite pair ambiguity vector, x 1 represents the reference satellite ambiguity, x n represents the nth fixed satellite ambiguity; v represents the observation noise vector, δ s 2 represents that the observation noise follows a normal distribution with a mean of 0 and a variance-covariance matrix of R s , R sIt represents the variance of the ambiguity error for maintaining the s satellite system.

[0027] The present invention also includes an ambiguity transfer system based on quality control, comprising:

[0028] A fixed ambiguity acquisition module, configured to receive satellite system observation data, construct a precise point positioning (PPP) observation model, solve the observation model to obtain floating ambiguities, and perform ambiguity fixing on the floating ambiguities to obtain fixed ambiguities;

[0029] A quality control module, configured to check the cut-off elevation angle of the satellite corresponding to the fixed ambiguity. When the cut-off elevation angle is less than the set threshold, the fixed ambiguity of this satellite is marked as untrustworthy; otherwise, check the signal-to-noise ratio of the frequencies of the L1 and L2 carriers of this satellite. When the signal-to-noise ratio of the frequencies of the L1 and L2 carriers is lower than the set threshold, the fixed ambiguity of this satellite is marked as untrustworthy; otherwise, filter the fixed ambiguities corresponding to the satellites marked for downweighting during the solution of the observation model; check the phase residuals during the filtered satellite filtering solution. When the phase residuals of this satellite are larger than the set threshold, this fixed ambiguity is excluded; otherwise, determine whether the number of consecutive fixed epochs of this satellite is greater than the set threshold. If the number of consecutive fixed epochs is less than the set threshold, the fixed ambiguity of this satellite is marked as untrustworthy; after excluding all the fixed ambiguities marked as untrustworthy, the remaining fixed ambiguities are obtained;

[0030] A transfer module, configured to establish a pseudo-observation equation based on the remaining fixed ambiguities to update the ambiguity parameters of the current epoch and inherit them to the epochs of the subsequent continuous arc segments of this satellite system without cycle slips.

[0031] The present invention also includes a computer device for ambiguity transfer based on quality control, comprising: a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, the steps of the ambiguity transfer method based on quality control are implemented.

[0032] The present invention also includes a readable storage medium storing a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, they are used to execute the steps of the ambiguity transfer method based on quality control.

[0033] The present invention provides an ambiguity transfer method based on quality control, having the following beneficial effects:

[0034] In the present invention, considering that signals from satellites at low elevation angles are usually more affected by multipath effects and have higher noise, the cut-off elevation angle of the satellite corresponding to the fixed ambiguity is tested. At the same time, considering that satellite measurements with low signal-to-noise ratios may be interfered with or have a serious noise level, resulting in low-precision fixed ambiguities, the signal-to-noise ratios of the frequencies of the satellite L1 and L2 carriers are tested. Since the accuracy of the PPP floating solution has a significant impact on the performance of the fixed ambiguity, reliable measurements are given priority in subset selection. Considering that both the carrier-phase multipath effect and undetected cycle slips will be reflected in the posterior phase residuals, if the fixed ambiguity has a large posterior phase residual, it will be excluded. This method aims to solve the problem of decimeter-level positioning errors caused by the transmission of incorrect fixed ambiguities in the traditional FH mode. By implementing a more rigorous preprocessing strategy, low-precision fixed ambiguities are effectively screened out, thereby avoiding their contamination of the ambiguity maintenance process and improving the positioning accuracy of the satellite system. Description of the Drawings

[0035] Figure 1 It is a flowchart of the ambiguity fixing method in an embodiment of the present invention;

[0036] Figure 2 It is a flowchart of the FH ambiguity transmission based on quality control in an embodiment of the present invention;

[0037] Figure 3 It is a distribution map of user stations in an embodiment of the present invention;

[0038] Figure 4 It is a histogram of the statistical values of positioning errors in the east, north, and up directions in the static mode in an embodiment of the present invention;

[0039] Figure 5 It is a cumulative distribution map of positioning errors in the static mode in an embodiment of the present invention;

[0040] Figure 6 It is a coordinate sequence diagram of the KRGG station in the east, north, and up directions on the 75th day of the year 2024 in the simulated dynamic mode in an embodiment of the present invention;

[0041] Figure 7 It is a histogram of the statistical values of positioning in the east, north, and up directions in the simulated dynamic mode in an embodiment of the present invention;

[0042] Figure 8 It is a cumulative distribution map of positioning errors in the simulated dynamic mode in an embodiment of the present invention. Detailed Embodiment

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0044] The present invention proposes a partial ambiguity retention strategy based on quality control. This strategy aims to solve the problem of decimeter-level positioning errors caused by the transmission of incorrect fixed ambiguities in the traditional FH mode, and at the same time make up for the defect that the existing method for selecting the ambiguity subset to be retained does not fully consider various factors affecting the ambiguity accuracy. By cleverly integrating the idea of PAR subset selection into the FH mode and implementing a more rigorous preprocessing strategy, low-precision fixed ambiguities are effectively screened out, thus avoiding their contamination of the ambiguity retention process. On this basis, the correctly identified fixed ambiguities are used as pseudo-observations and participate in the filtering process of the corresponding floating-point solutions, ensuring that only high-quality ambiguities are transmitted to the next epoch. It not only realizes the selection scheme of the partial ambiguity subset to be retained, but also verifies its actual application effect in the PPP-AR technology. Specifically, it includes the following steps:

[0045] S1. Receive GNSS observation data and construct a precise point positioning (PPP) observation model; adopt the ionosphere-free combination (IF) model as the observation method. Since the two carriers used by GPS satellites are both in the L band of microwaves, they are respectively called the L1 carrier and the L2 carrier, and the L1 and L2 carriers are used for IF combination of GPS. The IF combination is one of the most commonly used observation combination models in PPP. This model can eliminate the first-order ionospheric delay in the pseudorange and carrier phase measurements through a dual-frequency linear combination. The pseudorange and phase observation equations of the ionosphere-free combination can be expressed as:

[0046]

[0047] Here is the geometric distance between the satellite and the receiver, with the unit of meter; c is the speed of light; dt r and dt s are the receiver and satellite clock errors respectively, with the unit of second; is the tropospheric delay, with the unit of meter; λIF represents the carrier wavelength of the ionosphere-free observation combination; B r,IF and are the carrier phase fractional biases (UPD) at the receiver end and the satellite end respectively. Only with a stable and reliable UPD product can subsequent PPP ambiguity fixing be achieved; is the integer ambiguity; b r,IF is the code pseudorange hardware delay between the receiver antenna and the signal correlator; is the code pseudorange hardware delay between the satellite signal transmitter and the satellite antenna; represents the pseudorange measurement error; represents the carrier phase measurement error, and represent the carrier phase observation value and the pseudorange observation value respectively.

[0048] In practice, since the precise satellite clock error correction is estimated using the ionosphere-free combination observations, when using precise products, the satellite-side IF combination pseudorange hardware delay is eliminated by the satellite clock error product; since the pseudorange observation value provides the absolute reference for the receiver clock error, the receiver clock error correction c·dt r absorbs the ionosphere-free combination pseudorange hardware delay b at the receiver side r,IF . In the ambiguity float solution PPP data processing, since the phase delay is linearly related to the ambiguity parameter, the ambiguity parameter absorbs the phase delay.

[0049] After performing partial error corrections using precise products, Equation (1) can be expressed as:

[0050]

[0051] In the equation, and represent the reparameterized receiver clock error and the ambiguity parameter respectively:

[0052]

[0053]

[0054] It can be seen from Equation (2) that the ambiguity term in the ionosphere-free linear combination observations is not an integer. Therefore, generally, the ambiguity is estimated as a real unknown parameter. To obtain an effective PPP fixed solution, after eliminating the initial phase and hardware delay, the ionosphere-free ambiguity parameter can be decomposed into the form of wide-lane (WL) and narrow-lane (NL) ambiguity combinations. Thus, the PPP ambiguity integer solution problem is transformed into the problem of finding the wide-lane and narrow-lane ambiguity integer solutions. Once the wide-lane and narrow-lane ambiguities of the carrier phase are correctly fixed, they are recombined as known quantities to form the IF combination ambiguity, and the obtained ambiguity is the fixed value of the IF combination ambiguity parameter.

[0055] S2. Use the Kalman filtering method to solve the observation model to obtain the float ambiguity.

[0056] S3. After the float ambiguity corrected for the fractional bias restores the integer-week characteristic, check the fractional part of the ambiguity that has restored the integer-week characteristic. If the fractional part is greater than the set threshold, do not perform fixing; otherwise, fix the ambiguity.

[0057] S4. The method of gradually fixing the wide-lane (WL) and narrow-lane (NL) ambiguities is adopted to achieve the ionosphere-free ambiguity fixing in PPP. Among them, the wide-lane ambiguity is directly fixed by the rounding method, and the narrow-lane ambiguity is fixed by the Least-squares Ambiguity Decorrelation Adjustment (LAMBDA) method.

[0058] PPP ambiguity fixing; PPP-AR ambiguity fixing is a key step in high-precision positioning, which involves correcting the real-valued ambiguities that have lost their integer properties to restore their integer properties. When the real-valued ambiguities converge to a certain accuracy, the ambiguity fixing can be carried out to obtain high-precision positioning results. The flowchart of ambiguity fixing is as Figure 1 shown.

[0059] Since the ambiguities of the ionosphere-free combination method in PPP do not have integer properties, PPP ambiguity fixing is usually decomposed into sequentially attempting to fix the wide-lane and narrow-lane ambiguities. The ambiguities of the IF combination can be expressed in terms of the wide-lane ambiguity and the narrow-lane ambiguity as:

[0060]

[0061] where N wl and λ wl are the wide-lane ambiguity and its wavelength respectively; N 1 and λ nl are the narrow-lane ambiguity and its wavelength respectively. When the user fixes the wide-lane, narrow-lane, and ionosphere-free (IF) combination ambiguities, inter-satellite single-difference processing is required to eliminate the influence of the UPD at the receiver end.

[0062] Usually, the wide-lane phase minus narrow-lane pseudorange (MW) combination is used to fix the wide-lane ambiguity. Since the MW combination is affected by observation noise and multipath effects, it needs to be smoothed over multiple epochs before fixing the wide-lane ambiguity:

[0063]

[0064] where represents the MW combination observation value; and represent the floating-point single-difference WL ambiguity and the single-difference WL ambiguity with integer properties respectively. It can be seen from the above formula that to obtain the integer WL ambiguity, the UPD at the receiver end and the satellite end need to be known, B r,wl and The UPD at the receiver end is eliminated through inter-satellite single-differencing, and the UPD at the satellite end is corrected by analyzing the wide-lane ambiguity products broadcast by the central station. Since the WL ambiguity has a longer wavelength and is less affected by measurement noise and observation errors, high precision can be achieved after smoothing over several epochs. Therefore, the WL ambiguity is fixed directly using the rounding method.

[0065] When the fixed WL ambiguity is obtained, the NL ambiguity with integer characteristics and its variance are obtained by combining the ionosphere-free combined floating ambiguity:

[0066]

[0067] In the formula, represents the IF combined floating ambiguity; represents the fixed WL ambiguity; f 1 and f 2 represent the frequencies of the L1 and L2 carriers, which are 1575.42 MHz and 1227.60 MHz respectively; D IF is the ionosphere-free floating ambiguity covariance matrix for undifferenced observations. After phase bias correction, the NL ambiguity restores its integer characteristics.

[0068] Since the single-differenced NL ambiguity in PPP has strong correlation, the fixing of the NL ambiguity is searched and fixed by using the least squares ambiguity decorrelation adjustment LAMBDA method. After obtaining the fixed wide-lane and narrow-lane ambiguities, substituting them into Equation (7), the fixed IF combined ambiguity can be obtained. The functional model of PPP-AR can be simplified as:

[0069] E(y) = Aa + Bb + ε, D(y) = Q y (11)

[0070] In the formula, E(·) and D(·) represent expectation and variance respectively; y is the carrier phase observation value vector; a is the integer ambiguity parameter vector; b is the coordinates and other parameters independent of the ambiguity; A and B are the corresponding coefficient matrices; ε is the observation value residual vector; Q y is the variance-covariance matrix of the observation values.

[0071] According to the least squares theory, the unknowns a and b are estimated under the condition of

[0072]

[0073] The least squares theory when some unknowns are restricted to integers is called the integer least squares theory. In the integer least squares estimation algorithm, Equation (12) cannot be directly solved, so it needs to be solved in three steps:

[0074] ①Ignore the integer parameters; use the traditional least squares estimation method to solve the position parameters and the floating-point solution of the ambiguity parameter and the corresponding covariance matrix:

[0075]

[0076] Then, the NL ambiguity with restored integer-week characteristics is obtained according to the method in step S2.

[0077] ② Considering the integer characteristics of the ambiguity parameter, the LAMBDA algorithm proposed by Professor Teunissen is used to fix the NL ambiguity; the LAMBDA algorithm adopted for fixing the NL ambiguity includes two parts. First, ambiguity de-correlation processing, that is, integer Z-transform, is performed, and then integer ambiguity search is carried out.

[0078] The NL ambiguity after phase deviation correction undergoes integer Z-transform to obtain a new ambiguity vector:

[0079]

[0080] In the formula, and represent the original ambiguity and the variance-covariance matrix of the original ambiguity; and represent the de-correlated ambiguity vector and the ambiguity variance-covariance matrix; Z is an invertible integer transformation matrix.

[0081] Search for integer candidate solutions for the de-correlated floating-point solution of the ambiguity and its variance-covariance matrix to obtain the optimal fixed solution of the ambiguity parameter and its variance Therefore, based on step one, the optimal estimation criterion is considered:

[0082]

[0083] In the formula, is the integer vector solution of.

[0084] Finally, the newly searched integer ambiguity in the new space is obtained as the real ambiguity in the original space through inverse transformation.

[0085] Based on the fixed solution of the ambiguity parameter and its variance-covariance matrix update the position parameter to obtain the fixed solution of the position parameter and its variance-covariance matrix

[0086]

[0087] In the formula, and respectively represent the floating-point solution of the position parameter and its variance-covariance matrix; represents the floating-point solution of the ambiguity.

[0088] ③ Substitute the integer ambiguity obtained by the search into the normal equation to re-solve the fixed solution.

[0089] S5. After the search by the LAMBDA method, multiple groups of candidate NL fixed solutions can be obtained, and the Ratio test is performed on the candidate NL fixed ambiguities.

[0090] The most commonly used Ratio test is a "data-driven" type of index for the reliability of ambiguity fixing, which refers to the ratio of the sum of the squared errors of the sub-optimal and optimal integer ambiguities. The larger the ratio, the stronger the reliability of the optimal integer candidate value of the ambiguity. The specific formula is as follows:

[0091]

[0092] In the formula, and are the floating-point ambiguity vector and variance with the characteristics of integer ambiguity; and respectively represent the candidate vectors of the optimal and sub-optimal integer solutions of the ambiguity; k is a threshold, a constant given according to experience, and usually takes 2 or 3.

[0093] S6. Perform quality control on the obtained fixed ambiguity. If the fixed ambiguity passes all quality controls, it is considered that the fixed ambiguity is correctly fixed; otherwise, it is marked as an unreliable ambiguity.

[0094] In PPP-AR, effectively using the fixed ambiguity is the core of improving the fixing performance and positioning accuracy. However, the traditional FH ambiguity fixing mode has the risk of maintaining incorrect fixed ambiguities. The traditional FH fixing mode uses the fixed solutions of all ambiguities at this epoch as measurements, the floating-point solution of the calculated ambiguity as the state, performs Kalman filtering, and the fused ambiguity as the state of the current ambiguity. Use the fixed ambiguities corresponding to the fixed satellites to form the pseudo-observation equation y:

[0095] y = Hx + v v ∼ N(0, R s ) (18)

[0096] Here:

[0097]

[0098] R s = diag(δ s 2 , δ s 2 , …) (21)

[0099] In the formula, H represents the coefficient matrix of the pseudo-observation equation; x represents the ambiguity vector of the fixed satellites, and x 1 represents the reference satellite ambiguity, and x n represents the ambiguity of the nth fixed satellite; v represents the observation noise vector, and δ s 2 indicates that the observation noise follows a normal distribution with a mean of 0 and a variance-covariance matrix of R s and R s represents the ambiguity error variance of the s satellite system for retention.

[0100] In the FH mode, the integer ambiguity is used to constrain the floating-point ambiguity, which improves the fixing accuracy of the ambiguity. However, there is a situation where the floating-point ambiguity is constrained by the wrongly fixed ambiguity. In this mode, once the wrong fixed ambiguity is retained, it will result in a worse effect than when no floating-point ambiguity constraint is performed, causing a positioning accuracy of decimeters or even meters.

[0101] To optimize this method, the present invention proposes an FH mode based on strict quality control. Compared with the traditional mode, this mode implements a more strict preprocessing strategy for the retained ambiguity and selects a partial subset of the fixed ambiguity to impose constraints. The thresholds involved in this strategy are all set based on experience. Specifically, it includes:

[0102] (1) Elevation angle check. Generally speaking, the lower the satellite elevation angle, the more easily its observation value is affected by the multipath effect. Therefore, in the present invention, satellites with an elevation angle lower than 10° are excluded.

[0103] Considering that the signals from low-elevation satellites are usually affected by a higher multipath effect and have a large noise, the cut-off elevation angle of the satellite corresponding to the fixed ambiguity is checked. If the cut-off elevation angle is less than 10°, the fixed ambiguity of this satellite does not participate in the constraint of the floating-point ambiguity; otherwise, the judgment of the next quality control index is carried out.

[0104] (2) Signal-to-noise ratio check. If the SNR value of the satellite signal is too low, it may mean that the signal is severely interfered or attenuated, which will affect the accurate determination of the ambiguity. Therefore, for a satellite, if the signal-to-noise ratios of the first and second frequencies are lower than a given value (the empirical value in the present invention is 35 dB-Hz), they will not participate in the ambiguity retention.

[0105] Considering that satellite measurements with low signal-to-noise ratio may be subject to interference or severe noise levels, resulting in low-precision fixed ambiguities, the signal-to-noise ratios of the first and second frequencies of the satellites passing the (1) test are therefore examined. If the signal-to-noise ratio at the first or second frequency is below 35 dB-Hz, the corresponding fixed ambiguity will not be used as a constraint for subsequent epochs. Otherwise, the judgment of the next quality control index is carried out.

[0106] (3) Float solution accuracy. The accuracy of the float solution has an important impact on the fixing of narrow-lane ambiguities. Therefore, satellite observations with higher weights and stronger reliability are preferentially adopted, and the satellites with down-weighted float solutions are screened to ensure data quality.

[0107] Since the accuracy of the PPP float solution has a significant impact on the performance of the fixed ambiguity, high-reliability measurements are preferentially considered during subset selection. In the PPP float solution calculation in step one, GEO satellites and satellites with large pre-fit residuals are down-weighted and marked. If it is detected that the satellite is marked, the corresponding fixed ambiguity of the satellite is filtered out. Otherwise, the judgment of the next quality control index is carried out.

[0108] (4) Posterior carrier phase residuals. Considering that factors such as multipath effects and ionospheric delays may increase the phase residuals of the float solution, thereby affecting the positioning accuracy, satellites with large phase residuals are also filtered.

[0109] Both the carrier phase multipath effect and undetected cycle slips will be reflected in the posterior phase residuals. Therefore, if the fixed ambiguity has large posterior phase residuals, it is excluded. Otherwise, the last step of quality control is carried out.

[0110] (5) Number of consecutive fixed epochs. During the positioning convergence process, there will occasionally be a situation where the fractional part of the ambiguity happens to be close to 0 and is fixed. The reliability of such a fixation is not high. Therefore, only satellites with the number of consecutive fixed epochs exceeding the threshold (4 epochs in the present invention) retain their fixed ambiguities, aiming to avoid transmitting unreliable ambiguities fixed during the convergence process.

[0111] To ensure that the ambiguity accuracies of most satellites have converged, only satellites that meet all the above quality control criteria are marked as fixed satellites. In the present invention, only when the number of fixed satellites is not less than 4 is it allowed to maintain the fixed ambiguity. Otherwise, the fixed ambiguity is marked as untrustworthy.

[0112] S7. Exclude all fixed ambiguities marked as untrustworthy. Check the number of correctly fixed ambiguities. If the number of correctly fixed ambiguities is less than the set threshold, retain the PPP floating solution for this epoch and do not use the fixed ambiguities to constrain the parameters to be estimated. Otherwise, perform filtering calculations based on the correctly fixed ambiguities and inherit the ambiguity integer solution into the parameter vector to be estimated.

[0113] S8. Use some of the fixed ambiguities obtained through quality control as pseudo-observation equations to update the parameters to be estimated. At the same time, update the ambiguity parameters for the current epoch using the fixed ambiguity solution and then inherit them to the epochs of subsequent continuous arcs without cycle slips.

[0114] Experimental verification and result analysis:

[0115] To verify and evaluate the proposed partial ambiguity retention strategy based on quality control, the experiment is divided into two modes: static and simulated dynamic. Both experiments describe the dataset, processing method, and evaluation metrics (ambiguity fixing performance and positioning accuracy). Among them, the analysis of ambiguity fixing performance is carried out from two dimensions: epoch fixing rate and correct fixing rate. The epoch fixing rate is the ratio of the number of epochs with fixed solutions to the total number of epochs; the correct fixing rate refers to the ratio of the number of epochs with fixed solutions where the three-dimensional positioning error is less than 1 dm to the total number of epochs with fixed solutions. In the static and simulated dynamic modes, three sets of controls are set up for the experiment respectively. The specific experimental design scheme is shown in Table 1.

[0116] Table 1 Experimental design scheme

[0117]

[0118] Experimental data and processing strategy: The data selected for this invention is the observation data of 50 MGEX stations with approximately uniform distribution globally from day of year (DOY) 70 to 79 in 2024, with a sampling interval of 30 s, and the data is used for calculation. The distribution of user stations is as Figure 3 shown. Precise orbit and clock products are the post-processed multi-system precise orbit and clock products provided by the Center for Orbit Determination in Europe (CODE). The server uses 100 globally uniformly distributed MGEX stations to self-estimate the post-processed UPD products to achieve rapid ambiguity fixing. The client uses forward Kalman filtering to simulate real-time processing of the IGS MGEX station tracking dataset based on the IF combination. The 24-hour static post-processed PPP is used as the reference true value of the station coordinates.

[0119] To weaken the potential impact of UPD at the receiver end on the observed values, the satellite with the largest elevation angle is selected as the reference benchmark to perform the inter-satellite single-difference calculation. When the ambiguity is fixed, the satellite cutoff elevation angle is set to 15°; the fixed error variance of GPS is set to 0.03 cycles. In addition, the PPP-AR solution strategies for different ambiguity fixing modes are the same without special settings. During the data processing in the static mode, the present invention cuts each observation file into segments of 3 hours, with a total of 8 observation arcs within a day, and the experiment contains a total of 4000 observation arcs. For the detailed data solution strategy, see Table 2.

[0120] Table 2 Solution Processing Strategy

[0121]

[0122] Analysis and Comparison of 3h Static PPP Results: Table 3 lists the statistical results of the epoch fixing rate and the correct fixing rate in the static mode. It can be clearly seen from Table 3 that in the static mode, the ambiguity fixing rates of Experimental Group II and Experimental Group III both exceed 90%. However, the ambiguity fixing rate is not sufficient to comprehensively reflect the reliability of the fixed solution. Therefore, the correct ambiguity fixing rate is further analyzed and compared. The correct fixing rate of Experimental Group III is significantly higher than that of Experimental Group II, reaching 98.81%. This result clearly reveals that Experimental Group III has higher accuracy and reliability when fixing the ambiguity. This shows that the fixed performance of the research algorithm in the static mode is stable and shows certain superiority compared with the correct fixing rate of the traditional method.

[0123] Table 3 Statistical Results of the Epoch Fixing Rate and the Correct Fixing Rate of Two Groups of Experiments in the Static Mode

[0124]

[0125] Figure 4 and Figure 5(a), (b), and (c) respectively show the histograms of the positioning error statistics and the cumulative distribution function (CDF) graphs of the positioning errors of the floating-point solutions of each arc segment, the fixed solutions under the traditional ambiguity-fixed algorithm, and the fixed solutions of the partial ambiguity-fixed algorithm proposed in the present invention in the East, North, and Up directions in the static mode. It can be observed from the figure that the root mean square (RMS) of the positioning deviation of the fixed solution in Experiment III compared with the floating-point solution achieved significant decreases of approximately 50%, 17%, and 5% in the East, North, and Up directions, respectively. In addition, the proportions of the absolute values of the deviations less than 2 cm in the three directions in Experiment III were as high as 94.8%, 98.4%, and 70.5%, which fully demonstrated that the superiority of the PPP fixed solution in terms of positioning accuracy was maintained. Further, Experiment III was particularly prominent in terms of the positioning deviation at the 95% quantile, and its value was the smallest among the three groups of experiments, clearly indicating that the method of the present invention could ensure a smaller positioning error in the vast majority of cases, thus demonstrating a higher positioning accuracy. In Experiment III, the proportions of the positioning deviations greater than 1 dm in the East, North, and Up directions were 0.4%, 0.1%, and 0.8%, respectively. Compared with Experiment II, there was no situation where the proportion of the data with a deviation exceeding 1 dm was higher than that of the floating-point solution, which indicated that while maintaining the high accuracy of the fixed solution, the algorithm effectively reduced the risk of incorrectly fixing the ambiguity, thus demonstrating better stability and reliability. It can be seen from Figure 5 that the CDF curves of the three groups of experiments are relatively close and the differences are not obvious because in the static solution, the convergence conditions of each parameter to be estimated are good, and the positioning accuracies of each method are comparable after convergence. However, the method proposed in the present invention has the fastest rising speed at smaller error values, which means that the method has a higher probability of obtaining smaller errors in each direction, thus ensuring a higher positioning accuracy.

[0126] Comparison of the results of the simulated dynamic PPP fixed solution: To verify the positioning performance of the algorithm in different scenarios, a simulated dynamic PPP solution experiment was carried out. The coordinates of the user station were estimated every 30 s for 24 h, and other processing strategies were the same as those in the static solution mode.

[0127] Figure 6(a), (b), and (c) respectively show the coordinate sequence diagrams of the floating-point solution, the fixed solution in the Hold mode, and the fixed solution under the method proposed in the present invention in three directions at the KRGG station on the 75th day of the year 2024 in the simulated dynamic mode. It can be seen from the figure that after about 8 hours, due to incorrect ambiguity fixing, the positioning deviation in the three directions fluctuates significantly in the Hold mode. This error not only spreads rapidly but also causes the positioning in the Hold mode to start to diverge, the accuracy deteriorates sharply, and the maximum deviation in the three directions even reaches the meter level, and the RMS values are as high as 16.8 cm, 19.0 cm, and 25.2 cm respectively. In contrast, the method of the present invention avoids the problem of incorrect ambiguity transmission through relatively strict quality control screening, thus maintaining the stability and high precision of positioning. Compared with the floating-point solution, the positioning accuracy of this method in the three directions is improved by 60.7%, 23.8%, and 21.2% respectively. This preliminary result clearly shows that the method proposed in the present invention can effectively make up for the deficiencies of traditional methods in identifying incorrectly fixed ambiguities, give full play to the advantages of the fixed solution, and provide a strong guarantee for the accuracy and reliability of positioning.

[0128] Table 4 provides the statistics of the epoch fixing rate and the correct fixing rate in the simulated dynamic mode. Analyzing the data in the table, it can be found that Experiment III shows significant advantages over Experiment II in both the epoch fixing rate and the correct fixing rate, with increases of 3.59% and 4.45% respectively. This result indicates that in the simulated dynamic mode, the algorithm proposed in the present invention can provide more stable and reliable positioning results.

[0129] Table 4 Statistics of the epoch fixing rate and the correct fixing rate of two groups of experiments in the simulated dynamic mode

[0130]

[0131] Figure 7(a), (b), and (c) respectively show the histograms of error statistical data of the floating-point solution in the simulated dynamic mode, the fixed solution under the traditional algorithm of fixing ambiguity, and the fixed solution of the partial ambiguity-fixed algorithm proposed by the method of the present invention in the east, north, and up directions. Theoretically, the accuracy of the fixed solution should be higher than that of the floating-point solution. However, the RMS of the fixed solution in Experiment II is anomalously higher than that of the floating-point solution in all three directions. The proportion of the positioning error exceeding 1 dm in Experiment II is significantly higher than that in the other two groups of experiments, which reflects that the situation of incorrectly fixing the ambiguity is the most serious in this experiment. In contrast, the fixed solution in Experiment III shows the lowest error RMS, demonstrating its better positioning accuracy, specifically, the positioning accuracy in the three directions is improved by 32%, 15%, and 8% respectively. The proportions of the absolute values of the positioning deviations in the three directions in Experiment III being less than 2 cm reach 86.4%, 88.3%, and 49% respectively, which are increased by 2.8, 3.5, and 1.2 percentage points respectively compared with Experiment II. At the 95% quantile, the positioning errors in the three directions of Experiment III are significantly smaller than those of Experiment II. Especially in the east direction, the error reduction amplitude exceeds 61%. This indicates that in extreme cases, the positioning performance of the algorithm of the present invention is more superior. The proportion of the error exceeding 1 decimeter in Experiment III is the smallest, which not only further verifies the significant effect of the algorithm of the present invention in improving the positioning accuracy, but also its error distribution is more concentrated, effectively avoiding the occurrence of a large proportion of excessively large positioning errors, thus providing a more stable and accurate positioning result for users.

[0132] Figure 8 (a), (b), and (c) respectively show the CDF diagrams of the positioning errors in the east, north, and up directions in the simulated dynamic mode. In the simulated dynamic mode, the Hold mode can correctly fix most of the data ambiguities and achieve a positioning accuracy better than that of the floating-point solution. Its error is better than that of the floating-point solution within about 80% of the cumulative probability. However, the incorrect fixing of a small number of data in the Hold mode results in a large positioning deviation. Therefore, its error above a cumulative probability of 90% is higher than that of the floating-point solution. The positioning errors in each direction of Experiment III are generally small, and its CDF curve is above that of Experiment II in most error ranges. This indicates that the method proposed by the present invention can achieve smaller positioning errors with a higher probability, thus significantly improving the positioning accuracy and reliability. In contrast, in the static mode, the CDF curves of the three groups of experiments are relatively close and the differences are not obvious, but the method proposed by the present invention still shows the best positioning accuracy among the three groups of experiments. In the simulated dynamic PPP experiment, due to the complexity of environmental factors and the influence of the motion state, it is often difficult to compare the positioning accuracy with that of the static experiment. However, the algorithm proposed by the present invention can still exhibit excellent positioning performance under dynamic conditions. This discovery provides strong support for the promotion and application of this algorithm in dynamic application scenarios.

[0133] The present invention proposes a strategy for maintaining ambiguity based on quality control, aiming to optimize the process of ambiguity transfer. Compared with the traditional FH method, this strategy discriminates potential erroneously fixed ambiguities by implementing strict quality control measures, and then screens out a subset of ambiguities with high confidence. This subset is then used to constrain the floating solution of the ambiguities, thereby significantly improving the correct fixing rate of the ambiguities and effectively reducing the negative impact of erroneously fixed ambiguities on subsequent positioning results.

[0134] To verify the effectiveness of this strategy, the present invention selects data from 50 MGEX stations evenly distributed globally during DOY (Day of Year) 070 - 079 in 2024, and conducts three groups of experiments in static and simulated dynamic modes: PPP floating solution, traditional FH mode fixed solution, and fixed solution of the method of the present invention. The experimental results show that: in the static mode, the fixed solution demonstrated by the algorithm of the present invention has a significant advantage in positioning accuracy compared with the floating solution, especially in the east direction, where the RMS is reduced by 50%. Compared with the FH mode, the proportion of positioning deviations greater than 1 dm has decreased. This algorithm not only gives full play to the high-precision advantage of the fixed solution but also, to a certain extent, overcomes the contamination of erroneously fixed ambiguities to subsequent epochs. In the simulated dynamic experiment, the advantage of this algorithm is more prominent. The correct fixing rate of its ambiguities is increased from 91.0% to 95.5% compared with the traditional algorithm, and the proportion of deviations less than 1 dm can be reduced by up to 4.2 percentage points at most, and there is no situation where the positioning accuracy decreases compared with the floating solution. These experimental results strongly prove the feasibility and practicality of the algorithm of the present invention.

[0135] Based on the same inventive concept, the present invention also proposes a system for ambiguity transfer based on quality control, including:

[0136] A fixed ambiguity acquisition module, which is used to receive GNSS observation data, construct a precise point positioning PPP observation model; and use the Kalman filtering method to solve the observation model to obtain floating ambiguities; check the decimal part of the floating ambiguities. When the decimal part is greater than the set threshold, no ambiguity fixing is performed; otherwise, the floating ambiguity is fixed by the least squares ambiguity decorrelation adjustment method LAMBDA.

[0137] A quality control module is used to perform a Ratio test on the fixed ambiguities obtained through LAMBDA search. If the fixed ambiguity is less than the set threshold, the PPP float solution of this epoch is retained; otherwise, quality control is performed on this fixed ambiguity. Performing quality control on the fixed ambiguity specifically includes: checking the cut-off elevation angle of the satellite corresponding to the fixed ambiguity. When the cut-off elevation angle is less than the set threshold, the fixed ambiguity of this satellite is marked as untrustworthy; otherwise, the signal-to-noise ratios of the first frequency and the second frequency of this satellite are checked. When the signal-to-noise ratio on the first frequency or the second frequency is lower than the set threshold, the fixed ambiguity of this satellite is marked as untrustworthy; otherwise, filter the fixed ambiguities corresponding to the satellites marked as downweighted during the filtering parameter calculation; otherwise, check the phase residuals during the filtering solution of this satellite. When the residual of this satellite is larger than the set threshold, this fixed ambiguity is excluded; otherwise, determine whether the number of consecutive fixed epochs is greater than the set threshold. If the number of consecutive fixed epochs is less than the set threshold, the fixed ambiguity of this satellite is marked as untrustworthy.

[0138] A transfer module is used to check the remaining fixed ambiguities after excluding all the fixed ambiguities marked as untrustworthy. If the number of the remaining fixed ambiguities is less than the set threshold, the PPP float solution of this epoch is retained; otherwise, a pseudo-observation equation is established based on the remaining fixed ambiguities to update the ambiguity parameters of the current epoch and then inherited to the epochs of the subsequent continuous arc segments without cycle slips.

[0139] The present invention also provides a computer device for ambiguity transfer based on quality control, including: a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, the steps of the method for ambiguity transfer based on quality control are implemented.

[0140] The present invention also provides a readable storage medium storing a computer program. The computer program includes program instructions. When the program instructions are executed by the processor, they are used to execute the steps of the method for ambiguity transfer based on quality control.

[0141] As described above, only the specific preferred embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A fuzzy transfer method based on quality control, characterized in that: The following steps are involved: Receive satellite system observation data, build a precise point positioning PPP observation model, solve the observation model to obtain floating point ambiguity, fix the floating point ambiguity to obtain fixed ambiguity; The cut-off elevation angle of the satellite corresponding to the fixed ambiguity is checked. When the cut-off elevation angle is less than the set threshold, the fixed ambiguity of the satellite is marked as unreliable; otherwise, the signal-to-noise ratio of the frequency of the L1 and L2 carriers of the satellite is checked. When the signal-to-noise ratio of the frequency of the L1 and L2 carriers is lower than the set threshold, the fixed ambiguity of the satellite is marked as unreliable; otherwise, the fixed ambiguity corresponding to the satellite marked as downgraded during the observation model solution is filtered; the phase residual of the satellite after filtering during the filtering solution is checked. When the phase residual of the satellite is larger than the set threshold, the fixed ambiguity is excluded; otherwise, it is determined whether the number of continuous fixed epochs of the satellite is greater than the set threshold. If the number of continuous fixed epochs is less than the set threshold, the fixed ambiguity of the satellite is marked as unreliable; after excluding all fixed ambiguities marked as unreliable, the remaining fixed ambiguities are obtained; The pseudo observation equation is established according to the remaining fixed ambiguities to update the ambiguity parameters of the current epoch, and is inherited to the epochs of the subsequent continuous arc segments of the satellite system where no cycle slip occurs.

2. The ambiguity transfer method based on quality control according to claim 1, characterized in that: The receiving of satellite system observation data, building a precise point positioning PPP observation model, solving the observation model to obtain floating point ambiguity, and fixing the ambiguity of the floating point ambiguity to obtain fixed ambiguity specifically includes the following steps: Receive satellite system observation data and build a precise point positioning PPP observation model; The Kalman filter method is used to solve the observation model and obtain the floating point ambiguity; The decimal of the floating point ambiguity is checked. When the decimal is greater than or less than the set threshold, the ambiguity is not fixed; otherwise, the floating point ambiguity is fixed by the least squares ambiguity decorrelation adjustment method LAMBDA to obtain a fixed ambiguity.

3. The ambiguity transfer method based on quality control according to claim 2, characterized in that: The receiving satellite system observation data and constructing the precise point positioning PPP observation model are expressed as: in, is the geometric distance between the satellite and the receiver in meters; c is the speed of light; dt r and dt s are the receiver and satellite clock errors, respectively, in seconds; is the tropospheric delay in meters; λ IF Indicates the carrier wavelength of the ionosphere-free view combination; B r,IF and They are the carrier phase fractional deviation UPD at the receiver and satellite ends respectively; is the integer ambiguity; b r,IF is the code pseudorange hardware delay between the receiver antenna and the signal correlator; It is the code pseudo-range hardware delay between the satellite signal transmitter and the satellite antenna; represents the pseudorange measurement error; Indicates the carrier phase measurement error; and They represent pseudorange observations and carrier phase observations respectively.

4. The ambiguity transfer method based on quality control according to claim 2, characterized in that: The floating point ambiguity is fixed by the least squares ambiguity decorrelation adjustment method LAMBDA, and the generated fixed ambiguity is specifically expressed as: in, Indicates the IF combined floating point ambiguity; D nl represents the variance of NL ambiguity; represents the fixed wide lane WL ambiguity; D IF is the undifferenced IF combined floating point ambiguity covariance matrix; f1 and f2 represent the frequencies of L1 and L2 carriers, respectively.

5. The fuzzy transfer method based on quality control according to claim 1, characterized in that: The pseudo observation equation y is established according to the remaining fixed ambiguity, which is expressed as: y=Hx+vv~N(0,R s ) in, R s =diag(δ s 2 ,d s 2 ,…) Where H represents the coefficient matrix of the pseudo observation equation; x represents the fixed satellite pair ambiguity vector, x1 represents the reference satellite ambiguity, and x n represents the nth fixed satellite ambiguity; v represents the observation noise vector, δ s 2 It means that the observation noise has a mean of 0 and a variance-covariance matrix of R s The normal distribution, R s represents the variance of the held ambiguity error of the s-satellite system.

6. A quality-controlled fuzzy transfer system, characterized in that: include: The fixed ambiguity acquisition module is used to receive satellite system observation data, build a precise point positioning PPP observation model, solve the observation model to obtain floating-point ambiguity, and perform ambiguity fixation on the floating-point ambiguity to obtain fixed ambiguity; The quality control module is used to check the cut-off elevation angle of the satellite corresponding to the fixed ambiguity. When the cut-off elevation angle is less than the set threshold, the fixed ambiguity of the satellite is marked as unreliable; otherwise, the signal-to-noise ratio of the frequency of the L1 and L2 carriers of the satellite is checked. When the signal-to-noise ratio of the frequency of the L1 and L2 carriers is lower than the set threshold, the fixed ambiguity of the satellite is marked as unreliable; otherwise, the fixed ambiguity corresponding to the satellite marked as downgraded during the observation model solution is filtered; the phase residual of the satellite after filtering during the filtering solution is checked. When the phase residual of the satellite is larger than the set threshold, the fixed ambiguity is excluded; otherwise, it is determined whether the number of continuous fixed epochs of the satellite is greater than the set threshold. If the number of continuous fixed epochs is less than the set threshold, the fixed ambiguity of the satellite is marked as unreliable; after excluding all the fixed ambiguities marked as unreliable, the remaining fixed ambiguities are obtained; The transfer module is used to establish a pseudo observation equation based on the remaining fixed ambiguity to update the ambiguity parameters of the current epoch, and inherit it to the epochs of the subsequent continuous arc segments of the satellite system where no cycle slip occurs.

7. A computer device for fuzzy transfer based on quality control, characterized in that include: A memory, a processor and a computer program stored in the memory, wherein when the processor executes the computer program, the steps of the ambiguity transfer method based on quality control according to any one of claims 1 to 5 are implemented.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, they are used to execute the steps of the quality control-based ambiguity transfer method described in any one of claims 1 to 5.

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