A partial ambiguity fixing method suitable for multi-frequency multi-system RTK

Abstract

A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK includes: S1: Temporal correlation subset screening. When the RTK of the previous epoch is a fixed solution, the set of fixed double-difference ambiguities of the previous epoch is obtained as the first candidate set, and matched with all double-difference ambiguities of the current epoch. The number of unmatched non-correlated satellites is recorded as the first quantity; S2: When the first quantity reaches a first threshold, the satellite with the largest continuous signal lock value is selected as the first candidate satellite to form the first expanded subset. Based on the index position of each double-difference ambiguity in the subset, the corresponding ambiguity floating-point value and its covariance matrix sub-block are extracted, and the ambiguity fixing solution equation is constructed for fixing solution calculation; S3: If there is no fixed solution in the previous epoch, a multi-index fusion iterative screening process is initiated to remove low-quality double-difference ambiguities from the candidate set; S4: Joint removal by multi-dimensional quality assessment; S5: Unified invocation of ambiguity fixing solution calculation; S6: Status information maintenance and update.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning technology. Specifically, it relates to a method for fixing partial ambiguity in RTK (Real-Time Kinematic Differential Positioning), and more specifically, it relates to a method for fixing partial ambiguity in multi-frequency, multi-system RTK. Background Technology

[0002] In the field of RTK high-precision positioning, integer ambiguity fixation is crucial for achieving centimeter-level positioning. Traditional full ambiguity fixation methods require fixing the ambiguity parameters of all visible satellites, achieving ideal positioning results under favorable observation conditions and short baselines. However, in complex environments such as forests, mountains, and urban canyons, satellite signals are easily obstructed and interfered with, making full ambiguity fixation difficult to pass the RATIO test, leading to fixation failure or low fixation rate, severely impacting the usability and reliability of positioning. Therefore, it is necessary to overcome the shortcomings of some existing ambiguity fixation methods and improve the ambiguity fixation rate in practical applications.

[0003] In non-open environments, satellite signals are easily obstructed, leading to a decrease in RTK fixation rate for vehicle-mounted GNSS receivers. To improve fixation rates in such scenarios, existing technologies typically employ a partial ambiguity fixation strategy. This involves discarding low-quality ambiguities based on accuracy metrics such as satellite lock duration, elevation angle, or ambiguity variance when fixation fails for an entire cycle, and retaining a high-quality subset for re-fixation. However, in partially obstructed scenarios such as tree-lined roads or urban canyons, relying solely on these accuracy metrics has significant limitations: due to the complexity of the obstruction environment, newly added ambiguities may exhibit excellent instantaneous accuracy at the current epoch, while ambiguities fixed in the previous epoch may experience relative deterioration in accuracy due to brief fluctuations. If the existing strategy is followed, it is highly susceptible to the phenomenon of "incorrectly discarding fixed ambiguities and mistakenly retaining newly added ambiguities," resulting in the failure to reconstruct a subset of ambiguities that could have been fixed.

[0004] The basic idea behind existing partial ambiguity resolution (MIRP) methods is to select an optimal subset from the full ambiguity set for ambiguity resolution. Based on different selection strategies, these methods can be divided into two categories. The first category is based on observation quality, primarily using factors such as satellite elevation angle and signal-to-noise ratio (SNR) to directly eliminate satellites with poor observation conditions. This method is simple to implement, but elevation angle and SNR only indirectly reflect observation quality, and the fixed threshold elimination lacks a comprehensive consideration of the correlation between errors between satellites, easily leading to information redundancy or excessive elimination of effective satellites. The second category is based on the statistical characteristics of floating-point ambiguity solutions, using the variance-covariance matrix to select the optimal subset. This method has stronger theoretical completeness, but it relies entirely on the statistical information of floating-point solutions, which are susceptible to pseudorange errors and atmospheric residual errors. When observation errors are large, the information is easily distorted, resulting in a selection result that is not truly optimal.

[0005] While the two methods described above can eliminate low-quality satellites, their improvement in ambiguity resolution is limited in practice. Since the factors affecting ambiguity resolution (AR) are not fully reflected in the aforementioned accuracy metrics, it is difficult to maintain the stability of the fixed ambiguity parameter subset when using these metrics for ambiguity subset selection, especially in complex scenarios.

[0006] According to existing traditional methods, either based on the quality index of observations or based on the statistical characteristics of floating-point solutions, the ambiguity subset selection is difficult to coordinate the error correlation between satellites, and the statistical information is easily distorted when the observation error is large. Both methods are difficult to maintain the stability of a fixed ambiguity parameter subset.

[0007] Furthermore, existing technologies typically filter ambiguities based on instantaneous accuracy indicators such as satellite lock value, elevation angle, or ambiguity variance when RTK fixation fails. In our research, we discovered a typical "degradation reversal" phenomenon in partially obscured scenarios such as tree-lined areas and urban canyons: a set of ambiguities successfully fixed in one epoch may fail to be fixed entirely in the next epoch due to the introduction of new ambiguities. In this case, directly removing the new ambiguities could achieve partial fixation. However, existing filtering strategies based on instantaneous accuracy often mistakenly remove fixed ambiguities due to occasional degradation of their instantaneous accuracy, while retaining new ambiguities with seemingly good instantaneous accuracy, ultimately leading to complete failure of partial fixation.

[0008] Patent application number "202510235835.3" entitled "A Partial Ambiguity Resolution Method Applicable to Complex Dynamic Environments" discloses the construction of a single-epoch carrier phase double-difference observation equation. It calculates the weight factor corresponding to each satellite using standardized residuals and the IGG3 method, and substitutes the weight factor into the double-difference factor matrix to obtain the corrected observation value. The updated observation equation is solved using a weighted iterative least squares method. Partial ambiguity is fixed in the corrected floating-point ambiguity solution to obtain a fixed integer ambiguity solution. A ratio-based posterior consistency traversal test is performed. During the traversal search, the number of satellites participating in the solution is monitored; if the number of satellites is less than a threshold, the floating-point solution is output. This is an "optimization method within the current epoch," and its construction idea, selection criteria, subset construction logic, and dynamic supplementation mechanism differ from this invention. Summary of the Invention

[0009] To overcome the above-mentioned defects, the object of the present invention is:

[0010] This paper proposes a partial ambiguity fixing method based on time-domain prior information. This method breaks the limitation of relying solely on instantaneous accuracy indicators in the traditional method. It prioritizes the inheritance of ambiguities that were successfully fixed in the previous epoch as time-domain prior information to ensure the stability of the ambiguity subset between epochs. At the same time, it introduces a dynamic supplementation mechanism to avoid abandoning high-quality new satellites due to over-reliance on prior information.

[0011] The present invention also aims to:

[0012] This paper presents a partial ambiguity fixation method based on temporal prior information. This method breaks through the limitation of relying solely on instantaneous accuracy indicators in traditional methods. It prioritizes the inheritance of ambiguities that were successfully fixed in the previous epoch as temporal prior information to ensure the stability of the ambiguity subset across epochs. While introducing a dynamic supplementation mechanism, it also constructs a hierarchical backoff mechanism. When the temporal prior strategy fails, the accuracy filtering strategy is activated as a fallback, thereby comprehensively and robustly improving the RTK fixation success rate in complex occlusion scenarios.

[0013] That is, this invention proposes a method for partially fixing ambiguity by combining prior information in the time domain with features of the observation domain and the solution domain. It utilizes the ambiguity-fixed solution information from the previous epoch to filter the optimal subset, and further filters it by combining the quality of the observations and the statistical characteristics of the floating-point solutions. The aim is to accurately retain a high-precision ambiguity subset and dynamically incorporate the optimal newly added ambiguity parameters, thereby solving the "false rejection" problem that existing partial ambiguity fixing techniques easily lead to in partially occluded scenarios. Specifically: According to this invention:

[0014] Prioritize inheriting the fuzzy subset that utilizes the fixed fuzzy solution from the previous epoch and maintain the stability of this fuzzy subset. Match it with all double-difference fuzzies in the current epoch. Successfully matched double-difference fuzzies are marked as temporally correlated subsets and used as initial fixed subsets to maintain the stability of the fuzzy subsets across epochs.

[0015] To prevent the abandonment of ambiguities due to the gradual increase of new ambiguities in subsequent epochs, this invention employs a dynamic supplementation mechanism. New ambiguities are only considered for inclusion when they reach five. The satellite with the largest continuous signal lock value is selected from the non-correlated satellites, and its corresponding double-difference ambiguity is added to the initial fixed subset for fixation. If fixation fails, it is removed, and the initial fixed subset is restored for recalculation. This avoids interference from new ambiguities on the stable subset and solves the problem of fixation failure caused by an unstable ambiguity subset.

[0016] Key points of this invention:

[0017] Temporal prior inheritance mechanism: Abandoning the idea of ​​relying solely on the precision of the current epoch, it prioritizes inheriting the ambiguity that has been fixed in the previous epoch, ensuring the continuity and stability of the ambiguity subset in the temporal sequence;

[0018] Dynamic supplementation mechanism: Based on the inherited prior set, the newly added high-quality fuzziness is dynamically evaluated and incorporated to prevent subset solidification;

[0019] Layered fallback mechanism: When the prior strategy for the current time domain cannot achieve fixation, the existing accuracy screening strategy is used as a fallback. Through the above settings, this invention fundamentally overcomes the deceptive nature of instantaneous accuracy indicators in partially occluded scenarios, significantly improving the fixation rate and reliability of vehicle-mounted RTK in complex environments.

[0020] The technical solution of this invention is as follows:

[0021] A partial ambiguity fixation method applicable to multi-frequency, multi-system RTK is a partial ambiguity fixation method based on the fusion of time-domain prior information and multiple indicators, including the following steps:

[0022] S1: Temporal correlation subset filtering

[0023] When the RTK of the previous epoch is a fixed solution, obtain the set of double-difference ambiguities that were fixed in the previous epoch, and denote it as the first candidate set.

[0024] Iterate through all double-difference ambiguities in the current epoch, matching each one with elements in the first candidate set. For each successfully matched double-difference ambiguity, mark its available state as valid and update the corresponding satellite's fixed state flag to a pending fixed state.

[0025] For double-difference ambiguities that fail to match elements in the first candidate set, they are temporarily excluded from the initial fixed subset. At the same time, the number of non-associated satellites that do not match the fixed solution of the previous epoch, i.e., the double-difference ambiguity elements in the first candidate set, in the current epoch is counted. That is, the number of newly appearing or unused satellites in the previous ambiguity fixing is recorded as the first number.

[0026] S2: Dynamic replenishment mechanism

[0027] After completing the temporal correlation filtering of S1, the first quantity is judged. When the first quantity reaches the preset first threshold, the dynamic replenishment mechanism is activated as follows:

[0028] First, from all uncorrelated satellites, the satellite with the largest continuous signal lock value is selected as the first candidate satellite. The double-difference ambiguity corresponding to the first candidate satellite is added to the initial fixed subset to form the first expanded subset.

[0029] Based on the index position of each double-difference ambiguity in the state vector within the first expanded subset, the corresponding ambiguity floating-point value and its covariance matrix sub-block are extracted from the floating-point solution. A unified ambiguity fixed-solution module (LAMBDA) is then used to construct the ambiguity fixed-solution equation for fixed-solution.

[0030] Double-difference ambiguity (AMB):

[0031] , in, The coefficient matrix is ​​represented as: , Simultaneously, the covariance matrix corresponding to the double-difference ambiguity parameters is obtained.

[0032] ,

[0033] Double difference ambiguity and the corresponding covariance matrix Input into the LAMBDA algorithm; S3: Multi-indicator fusion and iterative screening

[0034] If there is no fixed solution in the previous epoch, or if the initial fixed subset fails to pass the verification due to the failure to fix S1 to S2, the multi-index fusion iterative screening process is started, that is, the joint elimination of multi-dimensional quality assessment. This process gradually eliminates low-quality double-difference ambiguities in the candidate set through multiple iterations until the fixation is successful or the iteration termination condition is met.

[0035] In each iteration, the identification and removal of the three types of low-quality ambiguity will be carried out simultaneously.

[0036] S4: Unified Invocation of Ambiguity Fixed Solution

[0037] In all the steps described above, all operations involving fixed ambiguity resolution are completed using a unified fixed ambiguity solver module (LAMBDA) to construct the aforementioned solution equations.

[0038] The input parameters for the unified ambiguity fixation module include: a list of double-difference ambiguities to be fixed, a floating-point ambiguity solution vector, the covariance matrix of the floating-point solution, and relevant auxiliary information.

[0039] Finally, if the fixation is successful, the fixed solution is substituted back into the positioning solution equation to obtain a high-precision fixed solution. The position parameters and other parameters to be estimated are then updated, and the fixed solution positioning result is output.

[0040] If the fixation fails, exit the fixation solution calculation module and RTK outputs a floating-point solution;

[0041] S5: Status Information Maintenance and Update

[0042] After each epoch is processed, the fixed solution information of the previous epoch is maintained and updated.

[0043] According to the present invention,

[0044] In S1, each double-difference ambiguity element in the first candidate set contains at least a reference star identifier, a non-reference star identifier, a frequency type, and index information in the Kalman filter state vector.

[0045] According to the present invention,

[0046] The number of non-associated satellites matched by the double-difference ambiguity elements in the first candidate set, which is the fixed solution of the previous epoch, is denoted as the first quantity, which is the number of newly appearing satellites or satellites not used in the previous ambiguity fixing.

[0047] According to the present invention: In S1, the first threshold is set based on the empirical results of multiple tests. The first threshold is set in the range of 5 to 15 based on multiple tests of the comprehensive sports car scenario. If there are many occluded scenes in the sports car route, the first threshold can be increased to 15.

[0048] According to the present invention, the first threshold is set to 10.

[0049] According to the present invention, in complex environments with multiple frequencies and multiple systems, it is necessary to construct ambiguity vectors and covariance matrices of corresponding dimensions.

[0050] According to the present invention,

[0051] For the first type of low-quality ambiguity, by traversing all available double-difference ambiguities in the candidate set and recording the continuous signal locking values ​​of the non-reference satellite, the double-difference ambiguity corresponding to the minimum value is selected as the object to be eliminated. If multiple minimum values ​​exist, further secondary screening can be performed by combining other dimensions, or all ambiguities can be eliminated.

[0052] For the second type of low-quality ambiguity, by traversing all available double-difference ambiguities in the candidate set, recording the variance value of each double-difference ambiguity, and selecting the double-difference ambiguity corresponding to the maximum value as the object to be eliminated.

[0053] For the third type of low-quality ambiguity, this strategy only takes effect when there is no fixed solution in the previous epoch. By traversing all available double-difference ambiguities in the candidate set, it checks whether the elevation angle of the non-reference satellite is lower than the preset cutoff elevation angle, and eliminates all double-difference ambiguities that meet the condition. In the initial stage where there is no fixed solution, this strategy prioritizes ensuring the basic geometric configuration quality of the subset by eliminating satellites with low elevation angles.

[0054] According to the present invention,

[0055] The preset cutoff elevation angle can be flexibly set according to the application scenario, and it is preferred to set it to the current cutoff elevation angle plus the preset step size.

[0056] According to the present invention, a fixed angle value is added: such as +5° or +10°, preferably 5° each time.

[0057] According to the present invention, the high-precision fixed solution is at the centimeter level.

[0058] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0059] In S1: The matching conditions are: same reference star, same non-reference star, and same frequency type. Double-difference ambiguities that meet the matching conditions are marked as a temporal-domain correlated subset, and their index positions in the state vector are recorded as initial fixed subsets.

[0060] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0061] If multiple satellites have the same lock-on consecutive values, and all of them are the maximum values, then the variances of the double-difference ambiguities corresponding to these satellites are further compared, and the satellite with the smallest variance is selected as the first candidate satellite.

[0062] This comparison strategy takes into account both signal continuity and parameter estimation accuracy.

[0063] Satellites with higher signal continuity have a lower probability of cycle slips, reflecting higher accuracy of the original observations; while the smallest variance reflects the internal consistency accuracy of the ambiguity parameters. These two indicators can be used to identify reliable satellites as candidates.

[0064] According to the present invention, in S2, the first threshold is set to 5 satellites.

[0065] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0066] In S2, the validity of the fixed solution result is verified. Verification methods include, but are not limited to, the RATIO test and the residual test. If the first expanded subset passes the verification, the fixed result is output, thus completing the ambiguity fixation for the current epoch.

[0067] If the first expanded subset fails verification, the double-difference ambiguity corresponding to the first candidate satellite is removed from the first expanded subset, and the system is restored to the initial fixed subset.

[0068] Subsequently, the ambiguity fixation was performed again using the initial fixed subset.

[0069] If the initial fixed subset passes validation, the fixed result is output;

[0070] If the verification fails, exit the current path and proceed to the subsequent supplementary screening process.

[0071] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0072] In S3, the multi-indicator fusion and iterative screening process includes the following sub-steps:

[0073] S3.1: Construct a candidate set of all available double-difference ambiguities in the current epoch, and mark all double-difference ambiguities in the candidate set as available;

[0074] S 3.2: Set the maximum number of iterations to 4.

[0075] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0076] Perform the following operations in each iteration:

[0077] (1) Count the number of available double-difference ambiguities in the current candidate set, and denote it as the second number.

[0078] If the second number is less than or equal to the preset minimum number of fixed satellites, the iteration terminates and the fixed solution calculation module exits, and RTK outputs a floating-point solution.

[0079] The number of available double-difference ambiguities in the current candidate set, i.e., the minimum number of fixed satellites for the second quantity, is preferably set to 4 to ensure basic redundancy in the positioning solution.

[0080] (2) Traverse the candidate set, identify all available double-difference ambiguities, remove them from the candidate set, and update the candidate set.

[0081] (3) Construct a fixed subset of ambiguities using the updated candidate set. Based on the index position of each double-difference ambiguity in the state vector within the subset, extract the corresponding floating-point value of the ambiguity and its covariance matrix sub-block. Call the ambiguity fixed solution module to perform fixed solution.

[0082] (4) Verify the validity of the fixed solution result. If the verification is successful, output the fixed result and terminate the iteration process.

[0083] If the verification fails, proceed to the next iteration and repeat the above operation.

[0084] According to the present invention, operation (2) involves: traversing the candidate set and checking each possible combination of integer ambiguities (i.e., candidate solutions). Identifying available double-difference ambiguities: for each candidate, determining whether it meets fixed conditions, such as:

[0085] Ratio value > threshold (usually ≥2);

[0086] The sum of squared residuals is relatively small;

[0087] It conforms to prior geometric constraints (such as elevation and coordinate range);

[0088] Within the error band (such as the EL / EW constraint in the DUFCOM method).

[0089] Eliminate confirmed ambiguities: Once a candidate is confirmed as "available" (i.e. successfully fixed), it is removed from the candidate set to avoid duplicate processing.

[0090] Update the candidate set: retain unconfirmed candidates and continue to participate in subsequent searches or optimizations (such as iterating LAMBDA, introducing new observations, etc.).

[0091] According to the above (4) of the present invention, the iteration can be 1-4 times, with a maximum of 4 iterations. Too many iterations will affect the computing power, and too few iterations may result in insufficient elimination and still cannot be fixed.

[0092] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0093] Based on operation (2): three types of low-quality ambiguity were identified as follows:

[0094] Category 1: Double-difference ambiguity with the smallest signal lock continuity value for the non-reference satellite. A smaller signal lock continuity value indicates poorer continuity of the satellite signal in the time domain, and lower reliability of its ambiguity parameters. Category 2: Double-difference ambiguity with the largest variance. The variance of double-difference ambiguity reflects the estimation accuracy of the floating-point ambiguity solution; a larger variance indicates lower estimation accuracy and a correspondingly lower success rate in fixing the ambiguity. Category 3: Double-difference ambiguity where the elevation angle of the non-reference satellite is lower than the preset cutoff elevation angle.

[0095] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0096] The preset cutoff height angle can be flexibly set according to the application scenario. It is preferred to set it to the current cutoff height angle plus a preset step size, that is, to add a fixed angle value.

[0097] According to the present invention, a fixed angle value is added: such as +5° or +10°, preferably 5° each time.

[0098] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0099] In S3, a joint elimination strategy based on multi-dimensional quality assessment is implemented, specifically:

[0100] For the first type of low-quality ambiguity, by traversing all available double-difference ambiguities in the candidate set and recording the continuous signal locking values ​​of the non-reference satellite, the double-difference ambiguity corresponding to the minimum value is selected as the object to be eliminated. If multiple minimum values ​​exist, further secondary screening can be performed by combining other dimensions, or all ambiguities can be eliminated.

[0101] For the second type of low-quality ambiguity, by traversing all available double-difference ambiguities in the candidate set, recording the variance value of each double-difference ambiguity, and selecting the double-difference ambiguity corresponding to the maximum value as the object to be eliminated.

[0102] For the third type of low-quality ambiguity, this strategy only takes effect when there is no fixed solution in the previous epoch. By traversing all available double-difference ambiguities in the candidate set, it checks whether the elevation angle of the non-reference satellite is lower than the preset cutoff elevation angle, and eliminates all double-difference ambiguities that meet the condition. In the initial stage where there is no fixed solution, this strategy prioritizes ensuring the basic geometric configuration quality of the subset by eliminating satellites with low elevation angles.

[0103] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0104] In S4, the internal processing flow of the ambiguity fixed solution module is as follows:

[0105] First, based on the input index list, the corresponding fuzzy floating-point subset is extracted from the complete floating-point solution vector, and the corresponding covariance submatrix is ​​extracted from the complete covariance matrix.

[0106] Secondly, the LAMBDA method, namely Least-squares Ambiguity Decorrelation Adjustment, is used to correctly fix the integer ambiguities (usually decimals) of the floating-point solutions in carrier phase observations to integers, thereby achieving centimeter-level or even millimeter-level positioning accuracy.

[0107] The extracted floating-point ambiguity subset is subjected to decorrelation processing and integer search to obtain integer ambiguity candidate solutions.

[0108] Then, the candidate solutions are subjected to a RATIO test. The ratio of the sum of squared residuals of the suboptimal solution and the optimal solution is calculated and compared with a preset RATIO threshold. The Ratio threshold ranges from 1.5 to 3, and preferably, the threshold is set to 3.

[0109] If the ratio is greater than the threshold, the optimal solution is accepted as the fixed solution; otherwise, the fixing is deemed to have failed, the fixed solution calculation module is exited, and RTK outputs the floating-point solution.

[0110] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0111] In S5, if a fixed solution is successfully obtained in the current epoch, the double-difference ambiguity information of the current epoch is saved to the first candidate set for use in the temporal correlation filtering of the next epoch.

[0112] If a fixed solution is not obtained in the current epoch, the fixed solution information of the previous epoch is retained unchanged, or it is cleared according to the preset strategy, and the process enters process S3: multi-index fusion iterative screening.

[0113] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0114] While saving the successfully fixed double-difference ambiguity information for the next epoch screening, the satellite lock continuity value is updated:

[0115] For satellites successfully tracked at the current epoch, the lock continuity value is increased by 1.

[0116] For satellites that have lost their lock, their lock continuity value is reset to zero.

[0117] If the current epoch exists, the satellite's continuity value is incremented by one; if the current epoch does not exist (i.e., the satellite is out of lock), it is reset to zero. This continuity value check can be performed when selecting the second quantity, or during multi-indicator fusion and iterative filtering.

[0118] According to the present invention, "locked continuity" refers to whether the satellite receiver maintains a locked state on the satellite signal during the tracking process (such as the continuity of observations of carrier phase, pseudorange, etc.) or, in high-precision positioning (such as PPP, RTK), improves the signal broadcasting strategy or atomic clock stability at the satellite end through software upgrades to enhance the tracking continuity and reliability of the ground receiver; or it refers to the ground control system dynamically adjusting the satellite health status, orbital parameters, clock bias model, etc., to maintain or improve the "locked" continuity at the user end.

[0119] A partial ambiguity fixing method applicable to multi-frequency, multi-system RTK according to the present invention:

[0120] The variance information of each double-difference ambiguity is maintained in real time. After the Kalman filter is updated, the variance values ​​of each ambiguity parameter are extracted from the covariance matrix of the state vector and stored in the corresponding double-difference ambiguity data structure for use in subsequent filtering steps.

[0121] Through the coordinated operation of the above six steps, this invention achieves a hierarchical filtering strategy for partially fixed ambiguity: when prior temporal information is available, historical fixed solutions are preferentially used to guide subset construction, maintaining the continuity and stability of positioning; when temporal information is insufficient or invalid, an iterative elimination strategy based on multi-index fusion is used to quickly converge to the optimal subset. According to this invention, high-precision ambiguity subsets can be accurately retained, and optimal new ambiguity parameters can be added to the subset based on accuracy indicators, thereby effectively improving the ambiguity fixation rate and positioning reliability in complex environments. Attached Figure Description

[0122] Figure 1 This is a flowchart summarizing the process of this invention.

[0123] Figure 2 This is a detailed flowchart of the present invention.

[0124] Figure 3a The RTK trajectory diagram for test route 1 without the inclusion of a partial ambiguity fixing strategy (green trajectories indicate fixed solutions).

[0125] Figure 3b The RTK trajectory diagram of test route one, which incorporates a screening strategy with added accuracy metrics (green trajectory indicates a fixed solution).

[0126] Figure 3c The RTK trajectory diagram for test route one incorporating the strategy of this invention (green trajectories represent fixed solutions).

[0127] Figure 4a The RTK trajectory diagram for test route 2 without the inclusion of a partial ambiguity fixing strategy (green trajectories indicate fixed solutions).

[0128] Figure 4b The RTK trajectory diagram of test route two for the inclusion of accuracy index screening strategy (green trajectory indicates fixed solution).

[0129] Figure 4c The RTK trajectory diagram for test route two incorporating the strategy of this invention (green trajectories indicate fixed solutions).

[0130] from Figures 3a-3c As can be seen, after adding the screening strategy based on accuracy indicators, the RTK fixation rate (the number of green trajectories) has been significantly improved, with the overall fixation rate increasing from 74.8% to 82.9%. After adopting the strategy of this invention, the overall fixation rate has been further improved to 84.0%.

[0131] from Figures 4a-4c As can be seen, after adding the screening strategy based on accuracy indicators, the RTK fixation rate (the number of green trajectories) has improved, with the overall fixation rate increasing from 62.3% to 69.1%. After adopting the strategy of this invention, the overall fixation rate has further increased to 69.8%. Detailed Implementation

[0132] Example

[0133] In this example, the positioning algorithm is first programmed into the GNSS receiver board. The receiver is installed inside the test vehicle and connected to the in-vehicle computer via a serial port. Real-time observation data is stored on the computer. Simultaneously, differential service provided by China Mobile is used as the base station data, transmitted wirelessly to the in-vehicle computer and then serially to the GNSS receiver. Upon receiving the base station data, the receiver can perform RTK positioning. A satellite signal receiving antenna is installed on the roof of the test vehicle and connected to the GNSS receiver board. It is important to note that a "mushroom-shaped" antenna with a choke coil must be used, and it must be securely fixed to the vehicle roof using magnets and tape to ensure that the antenna does not move. Two test routes are conducted as required. Each test route includes a combination of scenarios such as open roads, tree-lined roads, under viaducts, and high-rise residential areas, with a total route length exceeding 20 kilometers.

[0134] After the test vehicle starts, the GNSS receiver board will perform RTK calculations after acquiring the base station data. First, it will use the velocity value calculated by Doppler and the RTK result calculated at the previous moment to recursively deduce the velocity and obtain the Kalman filter prediction state. Then, it will establish a double-difference observation equation for pseudorange and carrier phase observations. Taking the carrier phase observation as an example, it will first perform inter-station differential to obtain the inter-station single-difference observation equation:

[0135]

[0136] In the formula, Indicates the inter-station single-difference carrier phase. These represent the inter-station single-difference geometric distance, ambiguity, tropospheric delay, ionospheric delay, and random error, respectively. When multiple common-view satellites exist, one is selected, typically the first satellite encountered, as the reference satellite (ref). This yields two single-difference observation equations, as shown below:

[0137]

[0138] At this point, by subtracting the reference star ref from the single-difference observation equation of the non-reference star s, the double-difference observation equation is obtained. In this case, the ionospheric and tropospheric delays are very small and can be ignored.

[0139]

[0140] In the formula, Indicates the phase of a double-difference carrier. Let X represent the double-difference geometric distance and random error, respectively. Then, extended Kalman filtering is used for observation updates. Note that the double-difference ambiguity should be expressed as the difference between the two inter-station single-difference ambiguities for parameter estimation. That is, the ambiguity parameters in the state matrix X are the inter-station single-difference ambiguities, not the double-difference ambiguities.

[0141] After the observation update, the RTK floating-point solution is obtained. At this point, the double-difference ambiguity AMB can be obtained using the coefficient matrix H and the state matrix X of the ambiguity parameters.

[0142]

[0143] in, The coefficient matrix is ​​represented as:

[0144]

[0145] Simultaneously, the covariance matrix corresponding to the double-difference ambiguity parameters can be obtained.

[0146]

[0147] Double difference ambiguity and the corresponding covariance matrix The input is fed into the LAMBDA algorithm for ambiguity fixing and RATIO testing. If the RATIO test fails, the partial ambiguity fixing module is entered.

[0148] First, determine if the Flag flag of the previous epoch RTK is equal to 4 (fixed solution). If so, obtain the fixed double-difference ambiguity set PreDDAMB from the previous epoch and match it with all double-difference ambiguities DDAMB in the current epoch. The matching conditions are that the reference star Ref, non-reference star NonRef, and frequency type Freq parameters are all the same. Find common ambiguities (that is, find these ambiguities that exist in both the previous and current epochs, because compared to the previous epoch, some satellites may have lost lock and some satellites may have been added, so even between the previous and current epochs, satellite ambiguities may increase or decrease). Successful matches are marked as the initial fixed subset DDAMBSlt, and the number of unmatched non-associated satellites is counted.

[0149] When the number of uncorrelated satellites reaches 5, the dynamic replenishment mechanism is activated:

[0150] Select the satellite with the largest continuous signal lock value from the non-correlated satellites. If multiple satellites exist, select the one with the smallest variance and add it to the initial fixed subset to form the first expanded subset. Extract the corresponding ambiguity floating-point value and covariance sub-block for fixed solution. If the RATIO test passes, output the fixed result; if it fails, remove the satellite, restore the initial fixed subset and recalculate. If it still fails, proceed to the next step.

[0151] When there is no fixed solution in the previous epoch or the aforementioned fixed solution fails, multi-index fusion iterative screening is initiated. At this time, the maximum number of iterations needs to be set to 4. Too many iterations will severely impact computing power, while too few iterations may result in poor performance.

[0152] In each iteration, if the number of usable ambiguities in the candidate set is less than or equal to 4, the iteration terminates and returns a failure message. The candidate set is then traversed, and three types of low-quality ambiguities are identified and eliminated: those with the smallest continuous signal lock value, those with the largest variance in double-difference ambiguities, and those whose elevation angle was lower than a preset cutoff elevation angle when no fixed solution was available in the previous epoch. After elimination, a fixed solution calculation and RATIO test are performed using the updated subset. If successful, the result is output and the iteration terminates; otherwise, the next iteration continues.

[0153] All ambiguity fixation calculations uniformly call the ambiguity fixation solution module, employing the LAMBDA method for integer search and RATIO verification. After each epoch, if fixation is successful, the successfully fixed double-difference ambiguity information is saved for the next epoch's filtering; simultaneously, the satellite lock continuity value is updated, incrementing by 1 for successfully tracked satellites and resetting to zero for lost-lock satellites, and the variance values ​​of each ambiguity are extracted from the Kalman filter covariance matrix and stored for subsequent filtering.

[0154] from Figures 3a-3c As can be seen, after adding the screening strategy based on accuracy indicators, the RTK fixation rate (the number of green trajectories) has been significantly improved, with the overall fixation rate increasing from 74.8% to 82.9%. After adopting the strategy of this invention, the overall fixation rate has been further improved to 84.0%.

[0155] from Figures 4a-4c As can be seen, after adding the screening strategy based on accuracy indicators, the RTK fixation rate (the number of green trajectories) has improved, with the overall fixation rate increasing from 62.3% to 69.1%. After adopting the strategy of this invention, the overall fixation rate has further increased to 69.8%.

[0156] The beneficial effects of this invention are mainly reflected in the following aspects:

[0157] 1. Significantly improves ambiguity fixation rate in complex environments.

[0158] By introducing the fixed solution of the previous epoch as prior information in the time domain, and using historical high-precision ambiguity to guide the construction of the current epoch subset, this method effectively solves the problem of fixation failure caused by poor observation quality and statistical information distortion in traditional methods in scenarios with severe signal obstruction, such as urban canyons, forests, and mountainous areas.

[0159] 2. Enhance the continuity and stability of fixed solutions.

[0160] A temporal correlation filtering mechanism is adopted to prioritize the inheritance of historically successfully fixed ambiguity information, so that the fixed solution maintains strong correlation between epochs and avoids frequent jumps in the fixed solution caused by short-term changes in the satellite environment.

[0161] 3. Multi-dimensional integrated screening overcomes the limitations of single indicators.

[0162] By integrating prior information in the time domain with features in the observation domain (signal lock continuity, elevation angle) and the solution domain (ambiguity variance), and taking into account the correlation of errors between satellites, estimation accuracy and signal quality, the selected ambiguity subset is more reasonable and stable, avoiding the one-sidedness of traditional methods that rely solely on elevation angle or floating-point solution statistical information.

[0163] 4. A tiered screening strategy that balances efficiency and reliability.

[0164] Prioritize the use of temporal-domain correlated subsets, and only initiate dynamic supplementation and multi-index iterative elimination when fixation fails. Set an upper limit on the number of iterations and a minimum number of fixed satellites (4) to control computational overhead while ensuring fixation rate and maintaining basic positioning redundancy.

[0165] 5. Status information maintenance creates positive feedback.

[0166] The fixed solution of the previous epoch, the continuous value of satellite lock, and the ambiguity variance are updated in real time, so that the subsequent epoch selection is based on the continuously optimized state information, which further improves the fixed efficiency and positioning reliability of long-term operation.

Claims

1. A partial ambiguity fixation method applicable to multi-frequency, multi-system RTK, which is a partial ambiguity fixation method based on the fusion of time-domain prior information and multiple indicators, including the following steps: S1: Temporal correlation subset filtering When the RTK of the previous epoch is a fixed solution, obtain the set of double-difference ambiguities that were fixed in the previous epoch, and denote it as the first candidate set. Iterate through all double-difference ambiguities in the current epoch, matching each one with elements in the first candidate set. For each successfully matched double-difference ambiguity, mark its available state as valid and update the corresponding satellite's fixed state flag to a pending fixed state. For double-difference ambiguities that fail to match the elements in the first candidate set, they are temporarily not included in the initial fixed subset. At the same time, the number of non-associated satellites in the current epoch that do not match the fixed solution of the previous epoch, i.e., the double-difference ambiguity elements in the first candidate set, is counted. The number of newly appearing or unused satellites in the previous ambiguity fixing is recorded as the first number. S2: Dynamic replenishment mechanism After completing the temporal correlation filtering of S1, the first quantity is judged. When the first quantity reaches the preset first threshold, the dynamic replenishment mechanism is activated as follows: First, from all uncorrelated satellites, the satellite with the largest continuous signal lock value is selected as the first candidate satellite. The double-difference ambiguity corresponding to the first candidate satellite is added to the initial fixed subset to form the first expanded subset. Based on the index position of each double-difference ambiguity in the state vector in the first expanded subset, the corresponding ambiguity floating-point value and its covariance matrix sub-block are extracted from the floating-point solution. A unified ambiguity fixed-solution module based on the LAMBDA algorithm is then used to construct the ambiguity fixed-solution equation for fixed-solution. Double-difference ambiguity (AMB): ， in, The coefficient matrix is ​​represented as: ， Simultaneously, the covariance matrix corresponding to the double-difference ambiguity parameters is obtained. , ， Double difference ambiguity and the corresponding covariance matrix Input into the LAMBDA algorithm; S3: Multi-indicator fusion and iterative screening If there is no fixed solution in the previous epoch, or if the initial fixed subset fails to pass the verification due to the failure to fix S1 to S2, the multi-index fusion iterative screening process is started, that is, the joint elimination of multi-dimensional quality assessment. This process gradually eliminates low-quality double-difference ambiguities in the candidate set through multiple iterations until the fixation is successful or the iteration termination condition is met. In each iteration, the identification and removal of the three types of low-quality ambiguity will be carried out simultaneously. S4: Unified Invocation of Ambiguity Fixed Solution In all the steps described above, all operations involving fixed ambiguity resolution are completed using a unified fixed ambiguity resolution module based on the LAMBDA algorithm, which constructs the aforementioned solution equations. The input parameters for this module include: a list of double-difference ambiguity indices to be fixed, the floating-point solution vectors of the ambiguities, the covariance matrix of the floating-point solutions, and relevant auxiliary information. Finally, if the fixation is successful, the fixed solution is substituted back into the positioning solution equation to obtain a high-precision fixed solution. The position parameters and other parameters to be estimated are then updated, and the fixed solution positioning result is output. If the fixation fails, exit the fixation solution calculation module and RTK outputs a floating-point solution; S5: Status Information Maintenance and Update After each epoch is processed, the fixed solution information of the previous epoch is maintained and updated.

2. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 1, characterized in that: In S1: The matching conditions are: the reference star is the same, the non-reference star is the same, and the frequency type is the same. The double-difference ambiguity that meets the matching conditions is marked as a temporal related subset, and its index position in the state vector is recorded as the initial fixed subset.

3. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 1, characterized in that: If multiple satellites have the same lock-on consecutive values, and all of them are the maximum values, then the variances of the double-difference ambiguities corresponding to these satellites are further compared, and the satellite with the smallest variance is selected as the first candidate satellite. This comparison strategy takes into account both signal continuity and parameter estimation accuracy.

4. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 1, characterized in that: In S2, the first threshold is set to 5 satellites.

5. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 1, characterized in that: In S2, the validity of the fixed solution result is verified. Verification methods include, but are not limited to, the RATIO test and the residual test. If the first expanded subset passes the verification, the fixed result is output, thus completing the ambiguity fixation for the current epoch. If the first expanded subset fails verification, the double-difference ambiguity corresponding to the first candidate satellite is removed from the first expanded subset, and the system is restored to the initial fixed subset. Subsequently, the ambiguity fixation was performed again using the initial fixed subset. If the initial fixed subset passes validation, the fixed result is output; If the verification fails, exit the current path and proceed to the subsequent supplementary screening process.

6. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 1, characterized in that: In S3, the multi-indicator fusion and iterative screening process includes the following sub-steps: S3.1: Construct a candidate set of all available double-difference ambiguities in the current epoch, and mark all double-difference ambiguities in the candidate set as available; S 3.2: Set the maximum number of iterations to 4.

7. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 6, characterized in that: Perform the following operations in each iteration: (1) Count the number of available double-difference ambiguities in the current candidate set, and denote it as the second number, that is, the number of available double-difference ambiguities in the current candidate set. If the second number is less than or equal to the preset minimum number of fixed satellites, the iteration terminates and the fixed solution calculation module exits, and RTK outputs a floating-point solution. The number of available double-difference ambiguities in the current candidate set, i.e., the minimum number of fixed satellites for the second quantity, is set to 4 to ensure basic redundancy in the positioning solution. (2) Traverse the candidate set, identify all available double-difference ambiguities, remove them from the candidate set, and update the candidate set. (3) Construct a fixed subset of ambiguities using the updated candidate set. Based on the index position of each double-difference ambiguity in the state vector within the subset, extract the corresponding floating-point value of the ambiguity and its covariance matrix sub-block. Call the ambiguity fixed solution module to perform fixed solution. (4) Verify the validity of the fixed solution result. If the verification is successful, output the fixed result and terminate the iteration process. If the verification fails, proceed to the next iteration and repeat the above operation.

8. The partial ambiguity fixing method applicable to multi-frequency multi-system RTK as described in claim 1, characterized in that: In S3, a joint elimination strategy based on multi-dimensional quality assessment is implemented, specifically: For the first type of low-quality ambiguity, by traversing all available double-difference ambiguities in the candidate set and recording the continuous signal locking values ​​of non-reference stars, the double-difference ambiguity corresponding to the minimum value is selected as the object to be eliminated. If multiple minimum values ​​exist, further secondary screening can be performed by combining other dimensions, or all ambiguities can be eliminated. For the second type of low-quality ambiguity, by traversing all available double-difference ambiguities in the candidate set, recording the variance value of each double-difference ambiguity, and selecting the double-difference ambiguity corresponding to the maximum value as the object to be eliminated. For the third type of low-quality ambiguity, it only takes effect when there is no fixed solution in the previous epoch. By traversing all available double-difference ambiguities in the candidate set, it checks whether the elevation angle of the non-reference star is lower than the preset cutoff elevation angle and removes all double-difference ambiguities that meet the conditions. In the initial stage when there is no fixed solution, this strategy prioritizes the quality of the basic geometric configuration of the subset by removing satellites with low elevation angles.

9. The partial ambiguity fixing method applicable to multi-frequency, multi-system RTK as described in claim 8, characterized in that: The preset cutoff height angle can be flexibly set according to the application scenario. It is preferable to set it to the current cutoff height angle plus a preset step size, that is, to add a fixed angle value, such as +5° or +10°.

10. The partial ambiguity fixing method applicable to multi-frequency multi-system RTK as described in claim 1, characterized in that: In S4, the internal processing flow of the ambiguity fixed solution module is as follows: First, based on the input index list, the corresponding fuzzy floating-point subset is extracted from the complete floating-point solution vector, and the corresponding covariance submatrix is ​​extracted from the complete covariance matrix. Secondly, the LAMBDA method, i.e., least squares ambiguity reduction adjustment, is used to correctly fix the integer ambiguity of the floating-point solution in the carrier phase observation to integers, thereby achieving centimeter-level or even millimeter-level positioning accuracy. The extracted floating-point ambiguity subset is subjected to decorrelation processing and integer search to obtain integer ambiguity candidate solutions; Then, a RATIO test is performed on the candidate solutions, calculating the ratio of the sum of squared residuals between the suboptimal and optimal solutions, and comparing it with a preset RATIO threshold. If the ratio is greater than the threshold, the optimal solution is accepted as the fixed solution; otherwise, the fixing is deemed to have failed, the fixed solution calculation module is exited, and RTK outputs the floating-point solution.

11. The partial ambiguity fixing method applicable to multi-frequency multi-system RTK as described in claim 1, characterized in that: In S5, if a fixed solution is successfully obtained in the current epoch, the double-difference ambiguity information of the current epoch is saved to the first candidate set for use in the temporal correlation filtering of the next epoch. If a fixed solution is not obtained in the current epoch, the fixed solution information of the previous epoch is retained unchanged, or it is cleared according to the preset strategy, and the process enters process S3: multi-index fusion iterative screening.

12. The partial ambiguity fixing method applicable to multi-frequency multi-system RTK as described in claim 10, characterized in that: While saving the successfully fixed double-difference ambiguity information for the next epoch screening, the satellite lock continuity value is updated: For satellites successfully tracked in the current epoch, the lock continuity value is increased by 1; For satellites that have lost their lock, their lock continuity value is reset to zero.

13. The partial ambiguity fixing method applicable to multi-frequency multi-system RTK as described in claim 1, characterized in that: The variance information of each double-difference ambiguity is maintained in real time. After the Kalman filter is updated, the variance values ​​of each ambiguity parameter are extracted from the covariance matrix of the state vector and stored in the corresponding double-difference ambiguity data structure for use in subsequent filtering steps.

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