Methods, devices, and storage media for improving the reliability of differential RTK solutions in satellite systems

Abstract

This application provides a method, apparatus, and storage medium for improving the reliability of differential RTK solution for satellite systems. The method includes: outputting positioning coordinates, ambiguity fixation status, and residual vectors through an RTK solver; calculating horizontal and vertical protection levels based on the residual vectors, a preset error source model, and risk coefficients using an integrity monitoring module; triggering feedback control commands when the protection level exceeds a risk threshold to drive integer ambiguity recalculation and adaptive adjustment of observation processing; enabling real-time self-checking and closed-loop correction of positioning results under risk constraints; and outputting reliable positioning coordinates when the updated protection level meets the threshold conditions. This solves the problem of large deviations in positioning results and a lack of real-time reliability constraints caused by erroneous fixation or abnormal observations in complex environments.

Description

Technical Field

[0001] This application relates to the field of satellite positioning technology, specifically to a method, apparatus, and storage medium for improving the reliability of differential RTK solution for satellite systems. Background Technology

[0002] Global Navigation Satellite System (GNSS) positioning technology, due to its wide coverage and all-weather operation, is widely used in surveying, drones, autonomous driving, intelligent transportation, and engineering machinery. To meet the demand for high-precision positioning, differential real-time dynamic (RTK) technology achieves centimeter-level positioning accuracy through differential observations between a base station and a rover, utilizing carrier phase observations, and is widely used in real-time navigation and control scenarios. However, in practical applications, the reliability of RTK solutions is easily affected by various factors. On the one hand, obstructed environments such as urban canyons, under bridges, and under trees can reduce the number of visible satellites, worsen satellite geometry, and enhance signal multipath effects, leading to increased observation noise and abnormal residuals. On the other hand, enhanced ionospheric activity, tropospheric changes, residual satellite orbit clock errors, and receiver hardware errors can also cause incomplete differential corrections, resulting in accumulated model errors. These factors can reduce the success rate of ambiguity fixation and even lead to false fixations, causing sudden jumps or large deviations in the positioning solution. In high-reliability applications, the situation where a seemingly fixed solution is actually significantly deviated due to false fixations is particularly dangerous.

[0003] In existing technologies, RTK systems typically use methods such as ratio checks, residual threshold screening, and DOP limits to control the quality of the solution results. However, these methods often focus on local checks within the solver and are difficult to provide a safe boundary for positioning errors under unified risk constraints. Some systems introduce integrity monitoring and protection level calculation to evaluate the reliability of the positioning output, but these are often independent of the RTK solution process and are mainly used for post-event alarms or result marking. They lack real-time feedback control over the RTK solution process and cannot proactively trigger ambiguity recalculation, observation processing strategy adjustment, or output degradation when the protection level exceeds the limit. As a result, problems such as unreliable positioning results, untimely alarms, or discontinuous positioning output may still occur in complex environments. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and storage medium for improving the reliability of differential RTK solution for satellite systems.

[0005] To achieve the above objectives, the first aspect of this application provides a method for improving the reliability of differential RTK solution in a satellite system. The method is used in a satellite navigation and positioning system comprising a base station and a rover station, and includes:

[0006] Acquire GNSS observation data from the base station and rover;

[0007] The GNSS observation data is solved using an RTK solver to obtain the positioning coordinates, ambiguity fixation status, and residual vector corresponding to the positioning coordinates for the current epoch.

[0008] The residual vector and ambiguity fixed state are input into the integrity monitoring module. Based on the residual vector, the preset error source model and risk coefficient, the integrity monitoring module calculates the horizontal protection level and vertical protection level of the current epoch.

[0009] When the horizontal protection level is greater than the horizontal risk threshold and / or the vertical protection level is greater than the vertical risk threshold, the integrity monitoring module outputs a feedback control command to the RTK solver to trigger integer ambiguity re-resolution.

[0010] After the RTK solver responds to the feedback control command and solves the adaptively adjusted observation processing results again, it updates the positioning coordinates, residual vector, horizontal protection level, and vertical protection level.

[0011] If the updated horizontal protection level is less than or equal to the horizontal risk threshold and the updated vertical protection level is less than or equal to the vertical risk threshold, output the updated positioning coordinates for the current epoch.

[0012] In this embodiment of the application, the method further includes:

[0013] If, after executing the feedback control command a preset number of times, the updated horizontal protection level is still greater than the horizontal risk threshold and / or the updated vertical protection level is still greater than the vertical risk threshold, the integrity monitoring module outputs an alarm message and instructs the RTK solver to refuse to output the positioning coordinates of the current epoch and / or output a downgraded positioning result.

[0014] In this embodiment of the application, the method further includes:

[0015] When the horizontal protection level is less than or equal to the horizontal risk threshold and / or the vertical protection level is less than or equal to the vertical risk threshold, the integrity monitoring module will output the integrity judgment result along with the positioning coordinates, and use the horizontal protection level, vertical protection level, and ambiguity fixed state as the credibility indicator of the positioning coordinates.

[0016] The integrity assessment result is determined based on a comparison between the horizontal protection level and the horizontal risk threshold and / or a comparison between the vertical protection level and the vertical risk threshold.

[0017] In this embodiment of the application, the method further includes:

[0018] When the integrity monitoring module generates a feedback control command, it acquires the anomaly source identification information. The anomaly source identification information includes at least the suspicious satellite identification and / or the suspicious observation type identification. The RTK solver performs weight reduction or elimination on the corresponding satellite observations based on the anomaly source identification information before solving.

[0019] In this embodiment of the application, the formula for the integrity monitoring module to calculate the horizontal protection level and / or the vertical protection level is as follows:

[0020]

[0021]

[0022] in, For horizontal protection level, For vertical protection level, For risk coefficient, The standard deviation of the horizontal position error. The absolute value of the projection factor in the fault direction. This represents the standard deviation of the vertical position error.

[0023] The projection factor in the fault direction is determined by the satellite's geometric matrix.

[0024] In this embodiment of the application, the horizontal risk threshold and / or the vertical risk threshold are determined according to the preset integrity risk target and confidence level, and the risk threshold is adaptively adjusted as the ambiguity is fixed.

[0025] Among them, the risk threshold when the ambiguity is fixed as completely fixed is less than the risk threshold when the ambiguity is fixed as unfixed or partially fixed.

[0026] In this embodiment of the application, the method further includes:

[0027] After the RTK solver re-solves the floating-point ambiguity and its covariance in response to the feedback control command, integer least squares search is used to fix the integer ambiguity.

[0028] In this embodiment of the application, the method further includes:

[0029] Integer least squares search is implemented using the LAMBDA method, and the ambiguity fixation is determined by ratio test and / or residual consistency test.

[0030] If the inspection fails, the RTK solver outputs an unfixed state or a partially fixed state and continues to perform integrity monitoring.

[0031] The second aspect of this application provides a device for improving the reliability of differential RTK solution in a satellite system, comprising:

[0032] The memory is configured to store instructions;

[0033] The processor is configured to retrieve instructions from memory and, when executing instructions, implement a method to improve the reliability of differential RTK solutions for satellite systems.

[0034] A third aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform a method for improving the reliability of differential RTK solutions for satellite systems.

[0035] The RTK solver outputs positioning coordinates, ambiguity fixation status, and residual vectors. The integrity monitoring module calculates the horizontal and vertical protection levels based on the residual vectors, a preset error source model, and risk coefficients. When the protection level exceeds the risk threshold, a feedback control command is triggered to drive integer ambiguity recalculation and adaptive adjustment of observation processing. This enables the positioning results to achieve real-time self-checking and closed-loop correction under risk constraints. As a result, reliable positioning coordinates are output when the updated protection level meets the threshold conditions. This solves the problem that differential RTK in complex environments is prone to misfixation or abnormal observations, resulting in large deviations in positioning results and a lack of real-time reliability constraints. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0037] Figure 1 The schematic diagram illustrates a process flow of a satellite system differential RTK solution reliability improvement method according to an embodiment of this application;

[0038] Figure 2 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0040] Figure 1 A schematic flowchart illustrating a satellite system differential RTK solution reliability improvement method according to an embodiment of this application is shown. Figure 1As shown in one embodiment of this application, a method for improving the reliability of differential RTK solution for a satellite system is provided, including the following steps:

[0041] Step 102: Obtain GNSS observation data from the base station and rover station;

[0042] Step 104: Solve the GNSS observation data using the RTK solver to obtain the positioning coordinates, ambiguity fixation status, and residual vector corresponding to the positioning coordinates for the current epoch.

[0043] Step 106: Input the residual vector and ambiguity fixed state into the integrity monitoring module. The integrity monitoring module calculates the horizontal protection level and vertical protection level of the current epoch based on the residual vector, the preset error source model and risk coefficient.

[0044] Step 108: When the horizontal protection level is greater than the horizontal risk threshold and / or the vertical protection level is greater than the vertical risk threshold, the integrity monitoring module outputs a feedback control command to the RTK solver to trigger integer ambiguity recalculation.

[0045] Step 110: After the RTK solver responds to the feedback control command and solves the adaptively adjusted observation processing results again, update the positioning coordinates, residual vector, horizontal protection level, and vertical protection level.

[0046] Step 112: If the updated horizontal protection level is less than or equal to the horizontal risk threshold and the updated vertical protection level is less than or equal to the vertical risk threshold, output the updated positioning coordinates of the current epoch.

[0047] In one embodiment, the satellite navigation and positioning system includes a base station and a rover station. The base station and rover station each collect their own GNSS observation data, including pseudorange observations and carrier phase observations. The system inputs the GNSS observation data from the base station and rover station into an RTK solver. The RTK solver solves for the current epoch based on differential observations, obtaining the positioning coordinates for the current epoch, and simultaneously providing the ambiguity fixation state and the residual vector corresponding to the positioning coordinates. The ambiguity fixation state reflects whether the solution result for the current epoch is in an unfixed, partially fixed, or completely fixed state, while the residual vector reflects the fitting error between the observation values ​​and the solution model. After obtaining the positioning coordinates, ambiguity fixation state, and residual vector, the system inputs the residual vector and ambiguity fixation state into an integrity monitoring module. The integrity monitoring module calculates the horizontal and vertical protection levels for the current epoch based on the residual vector, a preset error source model, and a risk coefficient. The error source model describes the contribution of error sources such as orbital clock error residuals, ionospheric and tropospheric delay residuals, multipath errors, and receiver noise to observation uncertainty. The risk coefficient reflects the degree of conservatism under preset false alarm rate and false miss rate constraints. By combining the error source model with the statistical characteristics of the residual vector, the integrity monitoring module can obtain the protection level in the horizontal and vertical directions for the current epoch, which characterizes the safety boundary of the positioning result under given risk constraints. After calculating the horizontal and vertical protection levels, the integrity monitoring module compares the horizontal protection level with the horizontal risk threshold and the vertical protection level with the vertical risk threshold. When the horizontal protection level is greater than the horizontal risk threshold and / or the vertical protection level is greater than the vertical risk threshold, it indicates that the positioning result of the current epoch may exceed the risk constraints. The integrity monitoring module outputs a feedback control command to the RTK solver to trigger integer ambiguity recalculation. Feedback control commands drive the RTK solver to adaptively adjust the observation processing strategy for the current epoch. This includes downweighting or removing suspicious satellite observations, reselecting reference satellites, resetting ambiguity states and re-estimating floating-point ambiguities, and then performing integer ambiguity fixing. This prevents positioning errors caused by incorrect fixing or abnormal observations from being amplified in subsequent outputs. After responding to the feedback control commands, the RTK solver recalculates the current epoch based on the adaptively adjusted observation processing results, obtaining updated positioning coordinates and an updated residual vector. The updated residual vector is then sent back to the integrity monitoring module. The integrity monitoring module recalculates the updated horizontal and vertical protection levels accordingly. If the updated horizontal protection level is less than or equal to the horizontal risk threshold and the updated vertical protection level is less than or equal to the vertical risk threshold, the updated positioning coordinates for the current epoch are output as the final positioning result.Through the aforementioned closed-loop feedback mechanism driven by protection level, real-time self-checking and adaptive correction of the positioning results are achieved. This avoids situations where the solution appears normal but the actual deviation is large in complex environments, thus improving the reliability and availability of differential RTK in dynamic scenarios. If the horizontal protection level is less than or equal to the horizontal risk threshold and / or the vertical protection level is less than or equal to the vertical risk threshold, the current epoch positioning coordinates are output as the final positioning result.

[0048] In one embodiment, after the integrity monitoring module triggers a feedback control command based on the comparison results of the horizontal protection level and the horizontal risk threshold, and the vertical protection level and the vertical risk threshold, the system does not repeat the recalculation process indefinitely. Instead, it sets a preset number of iterations as an upper limit to constrain the number of times the closed-loop feedback is executed. The preset number of iterations can be set according to the real-time requirements of the application scenario. For example, in vehicle navigation or robot positioning scenarios, to avoid the failure of upper-level control due to a long period of no positioning results, the preset number of iterations can be set to 2, 3, or 5 times. After each execution of the feedback control command, the RTK solver recalculates based on the adaptively adjusted observation processing results. The integrity monitoring module updates the horizontal and vertical protection levels accordingly and compares them with the corresponding risk thresholds again. If, after multiple executions of the feedback control command within the preset number of iterations, the updated horizontal protection level is still greater than the horizontal risk threshold and / or the updated vertical protection level is still greater than the vertical risk threshold, it indicates that the GNSS observation quality under the current environment continues to fail to meet the risk constraints, and continued repeated ambiguity recalculation is unlikely to restore the system to a usable state in a short time. In this scenario, the integrity monitoring module outputs an alarm to alert the upper-layer service or security modules that the current positioning result is unreliable or unavailable. It also instructs the RTK solver to refuse to output the positioning coordinates for the current epoch, thus preventing potentially inaccurate positioning results from being provided to downstream systems. As an alternative, the integrity monitoring module can also instruct the RTK solver to output a degraded positioning result. This degraded result can be an unfixed floating-point solution, a partially fixed solution, or a low-precision solution with conservative weighting, used to maintain system continuity while ensuring safety boundaries. For example, in autonomous driving or industrial vehicle applications, the degraded positioning result can be used as input for fused positioning, working in conjunction with the inertial measurement unit output to reduce the risk of positioning interruption when GNSS is unavailable. By setting a preset number of consecutive exit strategies combined with alarm and output rejection mechanisms, closed-loop feedback can be prevented from falling into an invalid cycle, while simultaneously improving the overall system security and engineering availability in complex environments.

[0049] In one embodiment, after calculating the horizontal and vertical protection levels at each epoch, the integrity monitoring module further generates an integrity determination result based on the comparison results of the horizontal protection level and the horizontal risk threshold, and / or the vertical protection level and the vertical risk threshold. The integrity determination result indicates whether the current epoch's positioning coordinates meet risk constraints. The integrity determination result can be represented in binary form as available or unavailable, or in a multi-level form as pass, alarm, or downgrade status, allowing upper-layer services to adopt different strategies according to different statuses. When the horizontal protection level is less than or equal to the horizontal risk threshold and / or the vertical protection level is less than or equal to the vertical risk threshold, the integrity monitoring module outputs the integrity determination result along with the positioning coordinates, and simultaneously outputs the horizontal protection level, vertical protection level, and ambiguity fixation status as a credibility identifier for the positioning coordinates. The credibility identifier is used to add interpretable quality information to the positioning coordinates, enabling downstream applications to directly determine the risk level of the positioning result without parsing the internal calculation details. For example, horizontal and vertical protection levels can serve as quantitative indicators of the positioning error safety boundary. Ambiguity fixation status characterizes whether the positioning result is unfixed, partially fixed, or completely fixed at the carrier phase integer ambiguity level, thus reflecting the accuracy level and risk of misfixation. In one implementation, the credibility identifier output by the integrity monitoring module can be directly used by the upper-layer fusion positioning or security control module. The upper-layer module reads the integrity determination result while receiving the positioning coordinates. When the integrity determination result indicates that the positioning result meets the risk constraints, the positioning coordinates are allowed to enter the control link; when the integrity determination result indicates that the positioning result is risky, the module can choose to reject the positioning coordinates of that epoch, trigger multi-source fusion weight adjustment, or switch to another positioning mode. By uniformly carrying integrity determination results and credibility identifiers such as protection level and ambiguity fixation status at the output, the availability and controllability of positioning information in engineering applications can be improved while ensuring the continuity of positioning output, avoiding downstream systems relying solely on positioning coordinate values ​​and ignoring their reliability differences.

[0050] In one embodiment, when the integrity monitoring module determines that the horizontal protection level is greater than the horizontal risk threshold and / or the vertical protection level is greater than the vertical risk threshold and generates a feedback control command, it also simultaneously generates anomaly source identification information. The anomaly source identification information is used to indicate potential sources of anomalies causing the protection level to exceed the threshold. This information includes at least a suspicious satellite identifier and / or a suspicious observation type identifier. The suspicious satellite identifier indicates that one or more satellites have anomaly risks at the current epoch, while the suspicious observation type identifier indicates that the anomaly mainly originates from carrier phase observations, pseudorange observations, or observations at specific frequencies. In practice, the integrity monitoring module can generate anomaly source identification information based on the distribution characteristics of residual vectors across different satellites and observation types. For example, when the residual amplitude corresponding to a satellite is consistently large, or when the satellite's contribution to the horizontal or vertical protection level significantly increases, the satellite is marked as a suspicious satellite; when the carrier phase residual shows a sudden change at a certain frequency or fails the consistency check, the carrier phase observation at that frequency is marked as a suspicious observation type; when the pseudorange residual shows a systematic shift within a short period, the pseudorange observation is marked as a suspicious observation type. Anomaly source identification information can be output in the form of satellite PRN number, constellation identifier, observation type code, frequency point identifier, etc., so that the RTK solver can locate specific subsets of observations. After receiving the anomaly source identification information, the RTK solver performs weight reduction or removal of the marked satellite observations before solving. Weight reduction can be achieved by increasing the noise variance of the corresponding observation, reducing the observation weight, or increasing the suppression coefficient of robust estimation. Removal can be achieved by removing the corresponding observation from the current epoch solution equation. By using anomaly source identification information to drive observation weight reduction or removal, the RTK solver can prioritize the handling of observation sources that may cause misfixation or positioning errors during resolving, reducing the computational overhead of indiscriminate resolving and improving the convergence speed and correction effectiveness of resolving. This mechanism forms a tighter two-way collaborative closed loop between integrity monitoring and RTK solving. Integrity monitoring not only triggers resolving but also indicates observations that need to be prioritized, thereby improving the reliability and availability of positioning in occlusion, multipath, or interference scenarios.

[0051] In one embodiment, when calculating the protection level based on the residual vector, error source model, and risk coefficient, the integrity monitoring module can use a simplified protection level calculation formula to obtain the horizontal and vertical protection levels. The integrity monitoring module calculates the horizontal protection level (HPL) and the vertical protection level (VPL) respectively, where the horizontal protection level (HPL) characterizes the safety boundary in the horizontal direction under a given risk constraint, and the vertical protection level (VPL) characterizes the safety boundary in the vertical direction under a given risk constraint. Specifically, the integrity monitoring module can calculate the protection level according to the following formula:

[0052]

[0053]

[0054] in, The risk coefficient reflects the amplification factor under the constraints of preset false alarm rate and false alarm rate; This is the standard deviation of the horizontal position error, used to quantify the uncertainty of the current position estimate in the horizontal direction; This is the standard deviation of the vertical position error, used to quantify the uncertainty of the current position estimate in the vertical direction; It is the absolute value of the projection factor in the fault direction, used to reflect the amplification effect of potential faults or observation biases projected onto the location solution along the most unfavorable direction. and The covariance matrix of the location solution can be obtained. After completing the RTK solution, the system obtains the covariance matrix of the location parameters. The integrity monitoring module converts the horizontal and vertical components of the covariance matrix into standard deviations, which are then used as the standard deviations. and Among them, the standard deviation of the horizontal position error The standard deviation of the vertical position error can be obtained by combining the variances of the east and north components. It can be obtained from the variance of the axial component. In this way, and It can be matched with the satellite geometry, observation weights, and residual levels of the current epoch, thereby reflecting the actual uncertainty of the positioning results at the current epoch.

[0055] In one embodiment, the fault direction projection factor W is determined by the satellite geometry matrix. The satellite geometry matrix describes the spatial geometric relationship between the satellite and the receiver and participates in the construction of the linearization model for positioning calculation. The integrity monitoring module can calculate the mapping relationship of observation errors in the position domain based on the satellite geometry matrix, and further determine the projection amplification factor of the potential fault acting on the horizontal or vertical position error along the most unfavorable direction, obtaining W and taking its absolute value. Used for protection level calculation. By introducing |W|, the protection level is not only related to the position standard deviation, but also reflects the amplification effect of satellite geometry in the fault propagation process. This allows the protection level to be increased accordingly when the geometric conditions are poor or certain fault directions are more sensitive, thereby triggering integrity alarms or closed-loop recalculation more promptly.

[0056] In one embodiment, horizontal and vertical risk thresholds are used to constrain the horizontal and vertical protection levels, respectively. The risk thresholds represent the maximum acceptable range of positioning error safety boundaries for the business scenario. The risk thresholds are not fixed but determined based on preset integrity risk targets and confidence levels. Integrity risk targets constrain the system's allowed false alarm and false negative rates, while confidence levels reflect the statistical coverage requirements of the protection levels. By setting integrity risk targets and confidence levels during the system design phase, horizontal and vertical risk thresholds consistent with the target security requirements can be obtained, thus providing a unified risk basis for integrity determination and alarm strategies. The risk thresholds adaptively adjust with the ambiguity fixation state. The ambiguity fixation state reflects the credibility of the current epoch RTK solution at the integer ambiguity level; different fixation states correspond to different error patterns and risk characteristics. A fully fixed state typically corresponds to higher positioning accuracy, but misfixation can lead to sudden and significant deviations in the positioning results; unfixed or partially fixed states typically have lower positioning accuracy but are less prone to sudden errors caused by misfixation. Based on the above differences, different risk threshold strategies can be adopted for different ambiguity fixation states to maintain a balance between safety margin and availability in integrity determination under different solution states. Specifically, when the ambiguity fixation state is completely fixed, the risk threshold is set lower than the risk threshold when the ambiguity fixation state is unfixed or partially fixed, so as to increase the constraint strength on high-precision output and promptly detect and suppress potential large deviation risks caused by misfixation. When the ambiguity fixation state is unfixed or partially fixed, the risk threshold is set relatively loosely to avoid frequent alarm triggering or repeated re-resolution under naturally lower accuracy states, which would lead to discontinuous positioning output.

[0057] In one embodiment, after the integrity monitoring module outputs a feedback control command triggering integer ambiguity recalculation, the RTK solver adaptively adjusts the observation processing strategy for the current epoch and re-executes the RTK solution. During the recalculation process, the RTK solver first estimates the ambiguity without integer constraints, obtaining floating-point ambiguity and its covariance. Floating-point ambiguity represents the real-valued estimate of integer ambiguity in carrier phase observations, and covariance characterizes the uncertainty of the floating-point ambiguity estimate and the correlation between ambiguity components. By first obtaining the floating-point ambiguity and its covariance, the RTK solver can provide the necessary statistical input for subsequent integer fixing, ensuring that the integer search considers both the goodness of fit of candidate integers and the correlation between ambiguities. After obtaining the floating-point ambiguity and its covariance, the RTK solver uses integer least squares search to fix the integer ambiguity. The goal of integer least squares search is to find the integer ambiguity solution that minimizes the cost function across all integer candidate sets, making the fixed ambiguity as close as possible to the floating-point ambiguity in a covariance-weighted sense. Integer least squares search for integer ambiguity fixation eliminates integer uncertainties in carrier phase observations, thereby improving the positioning solution from an unfixed floating-point solution to a fixed or partially fixed solution, enhancing positioning accuracy and usability. Integer least squares search can work in conjunction with an integrity monitoring closed loop. Since feedback control commands typically trigger risk states where the protection level exceeds a threshold, the re-solved floating-point ambiguities and their covariance can be re-established under adaptively adjusted observation conditions. In this case, the integer search input is more reliable, reducing the probability of false fixation. After fixation, the RTK solver outputs the updated ambiguity fixation state and residual vector. The integrity monitoring module then recalculates the protection level and determines whether the risk threshold is met, thus forming a protection level-driven ambiguity recalculation and refixation process, improving the reliability and stability of differential RTK solutions in complex environments.

[0058] In one embodiment, when fixing integer ambiguities using integer least squares search, the RTK solver uses the LAMBDA method to achieve integer least squares solution. The LAMBDA method reduces the correlation between ambiguity parameters by decorrelating the floating-point ambiguities and their covariance, thereby narrowing the integer search space and improving the efficiency of integer candidate search. After obtaining integer candidate solutions, the RTK solver performs reliability assessment on the candidate solutions to avoid incorrect fixing that could lead to significant deviations in the positioning results. Reliability assessment includes ratio testing and / or residual consistency testing. Ratio testing compares the cost function differences between the optimal and second-best integer candidate solutions. When the difference reaches a preset threshold, the integer solution is considered to have sufficient discriminative power, and ambiguity fixing is considered successful. Residual consistency testing checks whether the statistical characteristics of the fixed residuals meet preset consistency conditions, such as whether the residuals increase significantly after fixing or whether the residual distribution shows abnormal shifts. When residual consistency meets preset conditions, the fixing result is considered reliable. By employing a combination of ratio testing and residual consistency testing, the fixation quality can be constrained simultaneously from two perspectives: integer candidate discrimination and observation fit consistency, thus reducing the risk of misfixation. When the ratio test and / or residual consistency test fail, the RTK solver does not output a fully fixed state, but rather an unfixed or partially fixed state, and continues to perform integrity monitoring. The unfixed state corresponds to maintaining the floating-point solution output, while the partially fixed state corresponds to fixing only some reliable ambiguities and maintaining floating-point estimates for the remaining ambiguities. By proactively downgrading when the test fails, the system avoids using unreliable integer fixation results for positioning output. Simultaneously, the updated residual vector and ambiguity fixation state are input into the integrity monitoring module, which recalculates the protection level and determines whether further feedback control commands need to be triggered, thereby maintaining the continuity and stability of the positioning output while ensuring safety constraints.

[0059] In one embodiment, this application further includes an integer ambiguity resolution process to improve the reliability of differential RTK resolution even after satellite signal loss. When solving GNSS observation data, the RTK solver handles the resolution and fixation of integer ambiguities in carrier phase observations. For integer jumps that may be caused by satellite signal loss, the RTK solver, after resolving the floating-point ambiguities and their covariance, uses integer least squares search to fix the integer ambiguities. The integer least squares search is implemented using the LAMBDA algorithm to quickly search for combinations of integer ambiguities in the candidate integer set; when the number of visible satellites meets preset conditions, the success rate of ambiguity fixation is improved. The RTK solver can also use multi-frequency observations for fusion resolution to accelerate the ambiguity fixation process. Multi-frequency observations include three-frequency observations. Taking the BeiDou system as an example, the RTK solver can perform combined resolution based on three-frequency observations (B1I, B2a, and B3I), thereby shortening the ambiguity fixation time and achieving integer ambiguity fixation faster than dual-frequency resolution. By using the integer ambiguity resolution method described above, the RTK solver can still complete ambiguity fixation more efficiently when the signal is lost or the observation conditions change, thus improving the stability and availability of differential RTK resolution.

[0060] In one embodiment, after receiving the residual vector and ambiguity fixed state output by the RTK solver, the integrity monitoring module, when calculating the protection level for the current epoch based on the residual vector, a preset error source model, and a risk coefficient, can employ a protection level enhancement calculation model oriented towards RTK characteristics to estimate the protection level. This calculation model comprehensively considers the error amplification effects caused by RTK residuals, user equivalent ranging errors, and satellite geometric factors, as well as the impact of integer ambiguity resolution uncertainty on positioning reliability. The integrity monitoring module can determine the protection level enhancement amount in the following form. :

[0061]

[0062] Where K is the risk coefficient, UERE is the user equivalent ranging error parameter corresponding to the preset error source model, and DOP is the geometric accuracy factor determined by satellite geometric relationships. The root mean square error between the carrier phase observations and the model predictions. To resolve uncertainty in integer ambiguity. It can be calculated from the residual vector output by the RTK solver, reflecting the overall level of fitting of the observations at the current epoch; The settings or updates are based on the ambiguity fixation state; when the ambiguity fixation state is completely fixed... Take the smaller value when the ambiguity is fixed in an unfixed or partially fixed state. Take a larger value to reflect the difference in ambiguity uncertainty under different solution states.

[0063] This application provides a satellite system differential RTK solution reliability improvement device, including:

[0064] The memory is configured to store instructions;

[0065] The processor is configured to retrieve instructions from memory and, when executing instructions, implement a method to improve the reliability of differential RTK solutions for satellite systems.

[0066] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described method for improving the reliability of differential RTK solution for satellite systems.

[0067] Compared with existing technologies, this application proposes a method to improve the reliability of differential RTK solution for satellite systems. The method outputs positioning coordinates, ambiguity fixation status, and residual vectors through the RTK solver. The integrity monitoring module calculates the horizontal and vertical protection levels based on the residual vectors, a preset error source model, and risk coefficients. When the protection level exceeds the risk threshold, a feedback control command is triggered to drive integer ambiguity re-resolution and adaptive adjustment of observation processing. This enables the positioning results to achieve real-time self-checking and closed-loop correction under risk constraints. As a result, reliable positioning coordinates are output when the updated protection level meets the threshold conditions. This solves the problem that differential RTK in complex environments is prone to misfixation or abnormal observations, resulting in large deviations in positioning results and a lack of real-time reliability constraints.

[0068] Figure 1 This is a flowchart illustrating a method for improving the reliability of differential RTK solutions for a satellite system in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0069] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 2As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a method for improving the reliability of differential RTK calculations for satellite systems. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0070] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0071] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0077] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0079] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for improving the reliability of differential RTK solution in a satellite system, the method being used in a satellite navigation and positioning system comprising a base station and a rover, characterized in that, The method includes: Acquire GNSS observation data from the base station and the rover; The GNSS observation data is solved using an RTK solver to obtain the positioning coordinates, ambiguity fixation status, and residual vector corresponding to the positioning coordinates for the current epoch. The residual vector and the ambiguity fixed state are input to the integrity monitoring module. Based on the residual vector, the preset error source model and the risk coefficient, the integrity monitoring module calculates the horizontal protection level and the vertical protection level of the current epoch. When the horizontal protection level is greater than the horizontal risk threshold and / or the vertical protection level is greater than the vertical risk threshold, the integrity monitoring module outputs a feedback control command to the RTK solver to trigger integer ambiguity re-resolution. After the RTK solver responds to the feedback control command and solves the adaptively adjusted observation processing results again, it updates the positioning coordinates, the residual vector, the horizontal protection level, and the vertical protection level. If the updated horizontal protection level is less than or equal to the horizontal risk threshold and the updated vertical protection level is less than or equal to the vertical risk threshold, output the updated positioning coordinates of the current epoch.

2. The method for improving the reliability of differential RTK solution for satellite systems according to claim 1, characterized in that, The method further includes: If, after executing the feedback control command a preset number of times, the updated horizontal protection level is still greater than the horizontal risk threshold and / or the updated vertical protection level is still greater than the vertical risk threshold, the integrity monitoring module outputs an alarm message and instructs the RTK solver to refuse to output the positioning coordinates of the current epoch and / or output a downgraded positioning result.

3. The method for improving the reliability of differential RTK solution for satellite systems according to claim 1, characterized in that, The method further includes: When the horizontal protection level is less than or equal to the horizontal risk threshold and / or the vertical protection level is less than or equal to the vertical risk threshold, the integrity monitoring module outputs the integrity determination result together with the positioning coordinates, and uses the horizontal protection level, the vertical protection level, and the ambiguity fixation state as the credibility identifier of the positioning coordinates. The integrity determination result is determined based on a comparison between the horizontal protection level and the horizontal risk threshold and / or a comparison between the vertical protection level and the vertical risk threshold.

4. The method for improving the reliability of differential RTK solution for satellite systems according to claim 1, characterized in that, The method further includes: When the integrity monitoring module generates the feedback control command, it acquires the anomaly source identification information. The anomaly source identification information includes at least a suspicious satellite identifier and / or a suspicious observation type identifier. The RTK solver performs weight reduction or elimination on the corresponding satellite observations based on the anomaly source identification information before solving.

5. The method for improving the reliability of differential RTK solution for satellite systems according to claim 1, characterized in that, The formula used by the integrity monitoring module to calculate the horizontal protection level and / or the vertical protection level is as follows: in, For horizontal protection level, For vertical protection level, The risk coefficient is mentioned above. The standard deviation of the horizontal position error. The absolute value of the projection factor in the fault direction. This represents the standard deviation of the vertical position error. The fault direction projection factor is determined by the satellite's geometric matrix.

6. The method for improving the reliability of differential RTK solution for satellite systems according to claim 1, characterized in that, The horizontal risk threshold and / or the vertical risk threshold are determined based on a preset integrity risk target and confidence level, and the risk threshold is adaptively adjusted as the ambiguity is fixed. Wherein, the risk threshold when the ambiguity fixation state is completely fixed is less than the risk threshold when the ambiguity fixation state is unfixed or partially fixed.

7. The method for improving the reliability of differential RTK solution for satellite systems according to claim 1, characterized in that, The method further includes: After the RTK solver re-solves the floating-point ambiguity and its covariance in response to the feedback control command, it uses integer least squares search to fix the integer ambiguity.

8. The method for improving the reliability of differential RTK solution for satellite systems according to claim 7, characterized in that, The method further includes: The integer least squares search is implemented using the LAMBDA method, and the ambiguity fixation is determined by ratio test and / or residual consistency test. If the inspection fails, the RTK solver outputs an unfixed state or a partially fixed state and continues to perform the integrity monitoring.

9. A device for improving the reliability of differential RTK solution in a satellite system, characterized in that, include: The memory is configured to store instructions; A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the satellite system differential RTK solution reliability improvement method according to any one of claims 1 to 8.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the satellite system differential RTK solution reliability improvement method according to any one of claims 1 to 8.

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