Satellite positioning result generation method and device, equipment and medium

Abstract

The invention discloses a satellite positioning result generation method and device, equipment and a medium, and relates to the technical field of satellite positioning, and the method comprises the steps: obtaining satellite observation data and a fault detection threshold, selecting a preset number of satellites based on the satellite observation data, building a preselected observation data set, carrying out the calculation of the preselected observation data set, and obtaining a least square result, performing calculation according to the residual statistics to obtain test statistics, adjusting a fault detection threshold to obtain a target fault detection threshold, when the test statistics exceed the target fault detection threshold, judging that a fault exists, identifying a fault satellite and removing the fault satellite, and performing calculation according to an updated data set to obtain a satellite positioning result. By establishing a preselected observation set, adopting a least square method to calculate a resolving value and a test statistic, and combining a least square result to dynamically adjust a fault detection threshold, rapid and accurate fault satellite identification and elimination are realized, and the fault detection efficiency and the positioning precision are improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a method, device, equipment and medium for generating satellite positioning results. Background Art

[0002] The Global Navigation Satellite System (GNSS) provides users with accurate position, velocity and time (PVT) information and is a core component of modern navigation systems. In order to ensure the safety and reliability of the navigation system, in addition to providing accurate positioning information, it is also necessary to have a fault identification function to ensure that users can be informed in a timely manner when a system failure occurs and continue to provide navigation services after the failure is eliminated. GNSS Receiver Autonomous Integrity Monitoring (RAIM) technology is a key means to achieve this goal. Its main purpose is to use the redundant information of the receiver to detect and identify satellite failures, thereby ensuring the reliability of the navigation system results.

[0003] Traditional GNSS receiver fault detection and methods for generating navigation positioning results mainly rely on methods based on least squares and parity vectors. These methods achieve fault detection by using redundant observations for consistency checks, where the detection statistics are usually constructed from pseudorange residuals. Specifically, this type of method requires at least one redundant observation for fault detection, while at least two redundant observations are required for fault identification. In addition, the traditional RAIM algorithm relies on the current observation data for receiver autonomous integrity detection, and does not fully utilize the historical PVT information on the user side. Therefore, a method for fault detection and positioning result generation is needed to improve the accuracy of satellite positioning results. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, equipment and medium for generating satellite positioning results, aiming to solve the technical problem of how to improve the accuracy of satellite positioning results.

[0005] To achieve the above purpose, the present application proposes a method for generating satellite positioning results, the method comprising: Obtain satellite observation data and fault detection thresholds; Selecting a preset number of satellites based on the satellite observation data to establish a preselected observation data set; Calculating the preselected observation data set to obtain a least squares result, wherein the least squares result includes a solution value, a residual statistic, a precision reduction factor, and an error slope value, wherein the solution value includes position, velocity, and time information obtained by solving a satellite receiver; Obtaining an error value according to the preselected observation data set and the solution value; Calculate according to the residual statistic to obtain a test statistic; The fault detection threshold is adjusted according to the error value, the precision reduction factor and a preset error slope value to obtain a target fault detection threshold; When the test statistic exceeds the target fault detection threshold, it is determined that a fault exists, and the faulty satellite is identified and removed; Acquire a target observation data set, where the target observation data set is a pre-selected observation data set remaining after eliminating the observation data corresponding to the faulty satellite; Calculating based on the target observation data set to obtain a target dilution of precision factor and the number of satellites; A judgment is made based on the target precision reduction factor and the number of satellites to obtain a satellite positioning result.

[0006] In one embodiment, the step of adjusting the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value to obtain a target fault detection threshold includes: Calculate a geometric distribution influence coefficient based on the dilution of precision factor; Calculating according to the error value and a preset error slope value to obtain a dynamic adjustment factor; Multiplying the geometric distribution influence coefficient and the dynamic adjustment factor to obtain a comprehensive adjustment coefficient; The fault detection threshold is processed according to the comprehensive adjustment coefficient to obtain a target fault detection threshold.

[0007] In one embodiment, the step of processing the fault detection threshold according to the comprehensive adjustment coefficient to obtain a target fault detection threshold includes: Set the scaling factor calculation method and scaling direction in the scaling processing strategy; The comprehensive adjustment coefficient is adjusted according to the scaling factor calculation method and the scaling direction to obtain the scaled adjustment coefficient; The fault detection threshold is iteratively optimized based on the scaled adjustment coefficient to obtain a target fault detection threshold.

[0008] In one embodiment, the step of determining according to the target dilution of precision factor and the number of satellites to obtain a satellite positioning result includes: When the target precision reduction factor does not exceed a preset threshold and the number of satellites meets a preset number, the satellite positioning result is determined to be valid, and the satellite positioning result is output, where the satellite positioning result is a least squares result; When the target precision reduction factor exceeds a preset threshold or the number of satellites does not meet a preset number, the satellite positioning result is determined to be invalid, and the process returns to the step of selecting a preset number of satellites based on the satellite observation data and establishing a pre-selected observation data set to update the satellite positioning result.

[0009] In one embodiment, the step of obtaining the error value according to the preselected observation data set and the solution value comprises: Inputting the observation data in the pre-selected observation data set into a preset satellite prediction model for processing to obtain predicted elevation and predicted clock error; The predicted elevation and predicted clock error are compared with the solved value to obtain an error value.

[0010] In one embodiment, before the step of inputting the observation data in the preselected observation data set into a preset satellite prediction model for processing to obtain the predicted elevation and predicted clock error, the step includes: Obtain satellite navigation sample data and build an initial satellite prediction model; Training the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model; The step of training the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model includes: Initialize model parameters; Inputting the satellite navigation sample data into the initial satellite prediction model for calculation to obtain a prediction value; Calculating according to the loss function, obtaining an error value between the predicted value and the true value; By calculating through the back propagation algorithm, the gradient of the model parameters is obtained; The model parameters are iteratively updated through an optimization algorithm according to the gradient until a maximum number of iterations is reached or the error value converges to a preset threshold, thereby obtaining a preset satellite prediction model.

[0011] In one embodiment, when the test statistic exceeds the target fault detection threshold, determining that a fault exists, identifying the faulty satellite and removing it comprises: When the test statistic exceeds the target fault detection threshold, it is determined that a fault exists, triggering a fault identification process; Statistical analysis methods are used to identify and obtain the faulty satellite; Eliminate the observation data corresponding to the faulty satellite from the pre-selected observation data set to obtain an updated observation data set; The updated observation data set is updated to calculate the solution value and the updated verification statistic to identify and eliminate other existing faulty satellites.

[0012] In addition, to achieve the above purpose, the present application also proposes a satellite positioning result generating device, the satellite positioning result generating device comprising: An acquisition module, used to acquire satellite observation data and fault detection thresholds; A construction module, configured to select a preset number of satellites based on the satellite observation data to establish a preselected observation data set; A calculation module, used to calculate the pre-selected observation data set to obtain a least squares result, wherein the least squares result includes a solution value, a residual statistic, a precision reduction factor and an error slope value, and the solution value includes position, speed and time information obtained by solving the satellite receiver; The calculation module is further used to obtain an error value according to the preselected observation data set and the solution value; The calculation module is further used to calculate according to the residual statistic to obtain a test statistic; An optimization module, configured to adjust the fault detection threshold according to the error value, the precision reduction factor and a preset error slope value to obtain a target fault detection threshold; A processing module, used for determining the existence of a fault, identifying the faulty satellite and removing it when the test statistic exceeds the target fault detection threshold; An updating module is used to obtain a target observation data set, where the target observation data set is a pre-selected observation data set remaining after eliminating the observation data corresponding to the faulty satellite; The calculation module is further used to perform calculations based on the target observation data set to obtain a target precision reduction factor and a number of satellites; The result module is used to make a judgment based on the target precision reduction factor and the number of satellites to obtain a satellite positioning result.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable medium, and a computer program is stored on the medium. When the computer program is executed by a processor, the steps of the satellite positioning result generating method as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the satellite positioning result generating method as described above are implemented.

[0015] The present application obtains satellite observation data and fault detection threshold, selects a preset number of satellites based on satellite observation data, establishes a pre-selected observation data set, calculates the pre-selected observation data set, obtains the least squares result, obtains the error value based on the pre-selected observation data set and the solution value, calculates based on the residual statistic, obtains the test statistic, adjusts the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value, obtains the target fault detection threshold, and when the test statistic exceeds the target fault detection threshold, determines that there is a fault, identifies the faulty satellite and removes it, obtains the target observation data set, calculates based on the target observation data set, obtains the target precision reduction factor and the number of satellites, and judges based on the target precision reduction factor and the number of satellites to obtain the satellite positioning result. By using historical data and current observation data to establish a pre-selected observation set, using the least squares method to calculate the solution value, and calculating the test statistic based on the residual statistic, the fault detection threshold is dynamically adjusted in combination with the error value, the precision reduction factor and the error slope, so as to achieve fast and accurate identification and removal of faulty satellites, and improve fault detection efficiency and positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 This is a flowchart of the first embodiment of the satellite positioning result generation method of the present application; Figure 2 This is a SLOPE basic principle diagram of the first embodiment of the satellite positioning result generation method of the present application; Figure 3 A schematic diagram of the relationship between the measurement error and the positioning error of the first embodiment of the satellite positioning result generation method of the present application; Figure 4 This is a schematic diagram of simulation results of the first embodiment of the satellite positioning result generation method of the present application; Figure 5 This is a flow chart of a second embodiment of the satellite positioning result generating method of the present application; Figure 6 This is a flowchart of a third embodiment of the satellite positioning result generation method of the present application; Figure 7 This is a schematic diagram of the module structure of the satellite positioning result generating device according to an embodiment of the present application; Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the satellite positioning result generation method in the embodiment of the present application.

[0018] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0020] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0021] In the Global Navigation Satellite System (GNSS), it is crucial to ensure the accuracy of position, velocity and time information. However, satellite signals may be interfered by various factors, resulting in data errors or loss, affecting positioning accuracy. Traditional fault detection methods rely on redundant observation data for error detection and elimination, but there are problems such as high requirements on the number of visible satellites and high computational complexity.

[0022] Therefore, in order to overcome these problems, the method of the present application is proposed. The main solution of the embodiment of the present application is: by acquiring satellite observation data and fault detection thresholds, selecting a preset number of satellites based on satellite observation data, establishing a pre-selected observation data set, calculating the pre-selected observation data set, obtaining the least squares result, obtaining the error value according to the pre-selected observation data set and the solution value, calculating according to the residual statistic, obtaining the test statistic, adjusting the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value, obtaining the target fault detection threshold, when the test statistic exceeds the target fault detection threshold, determining that there is a fault, identifying the faulty satellite and eliminating it, obtaining the target observation data set, calculating based on the target observation data set, obtaining the target precision reduction factor and the number of satellites, judging according to the target precision reduction factor and the number of satellites, and obtaining the satellite positioning result.

[0023] Based on this, the present application embodiment provides a method for generating satellite positioning results, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the satellite positioning result generation method of the present application.

[0024] In this embodiment, the satellite positioning result generating method includes steps S10 to S100: Step S10, obtaining satellite observation data and fault detection threshold.

[0025] It should be noted that in the Global Navigation Satellite System (GNSS), obtaining accurate satellite observation data is the key to achieving high-precision positioning. Satellite observation data mainly includes pseudorange, carrier phase, Doppler frequency shift and other information, which are not only used to calculate the user's position, velocity and time (PVT), but are also crucial for fault detection.

[0026] Specifically, the signals from different satellites are captured by the receiver antenna and converted into usable observation data. These data contain the distance information of each satellite relative to the receiver and signal quality parameters such as carrier-to-noise ratio. The setting of the fault detection threshold needs to comprehensively consider the dynamic changes of the dilution of precision (DOP), the error slope (SLOPE) and the test statistic. The SLOPE is related to the satellite geometry and the number of visible satellites. It reflects the relative relationship between the positioning error and the RAIM detection quantity when a single satellite pseudorange deviates. When performing fault monitoring, due to the geometric distribution of satellites to users, the positioning error may exceed the limit value, while the monitoring statistics are less than the threshold, that is, missed detection. Therefore, to achieve fault detection integrity assurance, it is necessary to determine whether the current satellite geometry distribution is suitable for integrity monitoring. The commonly used judgment standard is to use the approximate radial error protection value ARP to detect the availability of satellite geometry distribution, such as Figure 2 The basic principle diagram of SLOPE shown in the figure shows that the horizontal axis represents the size of the statistical monitoring quantity, and the vertical axis represents the approximate radial error caused by the navigation satellite failure. It can be seen from the figure that in the positioning solution process, the most difficult satellite to detect is the satellite whose deviation can produce the largest slope (Slopemax), and the greater the approximate radial positioning error caused by this, that is, the largest navigation satellite is the most difficult to monitor when it fails, and it is most likely to exceed the alarm limit and cause missed detection. Figure 3 The diagram of the relationship between the measurement error and the positioning error shows that under the same DOP value, the positioning error and the observation error (such as pseudorange noise, multipath effect, etc.) are approximately linearly related, that is, the larger the observation error, the larger the positioning error. Under the same observation error, the change in the DOP value will significantly affect the degree of positioning error amplification. When the DOP value is high (such as DOP=6), the satellite geometry is poor, the observation error will be amplified, and the positioning error will increase significantly; while at low DOP values ​​(such as DOP=1.5), the satellite geometry is better, and the observation error has less impact on the positioning results. This shows that in GNSS positioning, it is necessary not only to improve the accuracy of the observation, but also to optimize the spatial distribution of satellites to reduce the DOP value, thereby improving the overall positioning performance.

[0027] Furthermore, the receiver autonomous integrity monitoring (RAIM) algorithm based on least squares residuals detects and identifies satellite failures by calculating pseudorange residuals, providing an effective self-protection mechanism for the global navigation satellite system (GNSS). In the case where only a single satellite pseudorange has a deviation, fault detection can be achieved when the number of visible satellites reaches 5; and when the number of visible satellites increases to 6 or more, it is possible to further identify which specific satellite has a fault. In order to verify the effectiveness of this method, simulation tests were carried out on different numbers of GPS satellites (5-8) in a simulated environment, and in each test, a pseudorange deviation ranging from 0m to 200m was added to one of the satellites. Each deviation was tested 50 times to ensure the reliability of the results. Figure 4 The schematic diagram of the simulation results shows that as the Slope value (i.e., the error slope) increases, the positioning error shows an increasing trend, while the pseudorange residual (absolute value) gradually decreases. This means that for satellites with larger Slope values, even if there is a certain pseudorange deviation, it is difficult to be accurately detected by the RAIM algorithm because its residual is too small. In this embodiment, in particular, when the Slope exceeds 20, the rate of decrease of the pseudorange residual slows down significantly, indicating that any abnormality on the satellite becomes extremely difficult to capture at this time. For example, in the case of the largest Slope (assuming a constant deviation of 50m), as the Slope increases, the RAIM test statistic will continue to decrease until it is lower than the set threshold value, resulting in the system being unable to effectively identify the presence of a fault.

[0028] In addition, the threshold value needs to be dynamically adjusted in combination with the prediction model of historical PVT data. For example, a quadratic polynomial prediction model can be established by utilizing the slowly changing characteristics of the receiver clock error and the user elevation to correct the anomaly detection standard at the current moment in real time.

[0029] Step S20, selecting a preset number of satellites based on the satellite observation data to establish a pre-selected observation data set.

[0030] It should be noted that the receiver captures signals from multiple satellites and extracts key parameters such as pseudorange, carrier phase and Doppler shift from them. However, not all received signals are suitable for subsequent calculations because some satellites are affected by environmental factors such as building obstruction or ionospheric disturbances, resulting in unreliable observation data. Therefore, it is crucial to choose a suitable satellite combination. According to the needs of the actual application scenario and the number and geometric distribution of currently visible satellites, certain criteria are used to select the best satellite subset, that is, the pre-selected observation data set. This usually involves considering multiple factors, such as the carrier-to-noise ratio ( ), the elevation angle between the satellite and the user, and the influence of geometric distribution on the DOP. Ideally, satellites with high signal-to-noise ratio and good geometric layout should be selected to ensure the accuracy and reliability of the solution results.

[0031] Step S30, calculating the pre-selected observation data set to obtain a least squares result.

[0032] It should be noted that the least square method is a commonly used mathematical optimization technique for finding the model parameter estimation value closest to the observed data, that is, minimizing the residual sum of squares between the observed data and the model prediction value. In this embodiment, the least squares result includes a solution value, a residual statistic, a precision reduction factor, and an error slope value, and the solution value includes the position, speed, and time information obtained by solving the satellite receiver.

[0033] Specifically, the solution value is the position, velocity and time (PVT) information obtained by the satellite receiver based on the pre-selected observation data set. Accurate PVT solution is one of the core goals of the GNSS system, which is directly related to whether users can obtain accurate positioning services. The solution process involves complex mathematical models and algorithm optimization to ensure that reliable positioning information can be provided even in the presence of small observation errors. The residual statistic refers to the difference between the actual observation value and the theoretical value calculated based on the current estimate. By calculating the residual for all observation data and further analyzing its statistical characteristics such as the sum of squares or the sum of absolute values, the quality of the entire observation data set can be evaluated. Large residuals may indicate the presence of abnormal observations or potential faulty satellites. The precision reduction factor is an important indicator to measure the impact of satellite geometry on positioning accuracy. It reflects the positioning error amplification effect caused by the unsatisfactory distribution of satellite positions. A lower DOP value means a better satellite geometry distribution, which can provide higher positioning accuracy. Conversely, a higher DOP value indicates a poor satellite geometry layout, resulting in inaccurate positioning results. The error slope value describes the degree of influence on the positioning error when a single satellite pseudorange deviates. Generally, as the slope value increases, the positioning error will also increase accordingly, but the pseudorange residual may decrease. This means that some satellites with high slope values ​​may be difficult to be effectively detected by the RAIM algorithm even if there are large pseudorange deviations. Therefore, when designing a fault detection algorithm, special attention should be paid to satellites with high slope values, and consideration should be given to adjusting the detection threshold or adopting other strategies to improve the detection sensitivity.

[0034] Combining the above results, not only can the user's position be accurately located, but potential faults in the GNSS system can also be effectively monitored and identified to ensure the reliability and stability of the system.

[0035] Step S40, obtaining an error value according to the preselected observation data set and the solution value.

[0036] It should be noted that in the GNSS system, in order to further improve the accuracy and reliability of fault detection, a key step is to use historical observation data and current solution values ​​to predict future elevation and clock errors, and compare them with the actual solution values ​​to evaluate the system error.

[0037] Specifically, step S40 includes: inputting the observation data in the preselected observation data set into a preset satellite prediction model for processing to obtain a predicted elevation and a predicted clock error; comparing the predicted elevation and the predicted clock error with the solution value to obtain an error value. Specifically, based on the observation data in the preselected observation data set, it is input into a preset satellite prediction model for processing. The model is usually established based on the historical PVT (position, velocity, time) information of the receiver, and in particular, a quadratic polynomial model or other suitable time series prediction model is constructed using historical data of elevation and clock error. In this way, the predicted elevation and predicted clock error at the current moment can be obtained. These predicted values ​​reflect the ideal state that the receiver should be in when there is no fault. Next, the predicted elevation and clock error are compared with the actual solution value calculated by the least squares method. This comparison can reveal the difference between the two, that is, the error value. Specifically, the error value can be obtained by calculating the absolute difference or relative difference between the predicted value and the solution value. A large error may indicate the presence of certain problems, such as signal interference, multipath effect, or satellite failure.

[0038] On this basis, further analysis of the changing trend and distribution characteristics of the error values ​​is particularly important for fault detection. For example, if the error value continues to increase over multiple consecutive epochs, this may be due to an abnormality in a specific satellite. At this point, combined with other indicators such as pseudorange residuals, DOP values, and Slope values, the source of the fault can be more accurately locked. In addition, by making similar comparisons of different satellite subsets, it can also help identify specific faulty satellites, thereby providing a basis for subsequent elimination operations.

[0039] Step S50, performing calculations based on the residual statistic to obtain a test statistic.

[0040] It should be noted that the test statistic is used to quantify the consistency of the observed data and determine whether there is a potential satellite failure in the system. The pseudorange residuals obtained by the least squares method are used to construct a residual vector, which contains the measurement deviation of each satellite relative to the solved position. Then, preliminary statistics are generated by calculating the residual sum of squares or its normalized form. For example, one of the commonly used test statistics is the F statistic based on the residual sum of squares, which reflects the difference in the degree of fit between the observed data and the model. A larger F statistic indicates that there is a significant deviation, suggesting that some satellites are faulty.

[0041] Furthermore, a Gauss test based on residual elements or other statistical methods can be used to refine the fault identification of a single satellite. This method can not only detect overall system faults, but also accurately locate faults on a satellite-by-satellite basis. Specifically, for each satellite subset, the above process is repeated and the corresponding test statistics are calculated, thereby forming a set of statistics under multiple fault hypotheses.

[0042] Step S60, adjusting the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value to obtain a target fault detection threshold.

[0043] It should be noted that a dynamic adjustment mechanism is established to optimize the fault detection threshold. Specifically, the fault detection threshold can be adjusted in real time based on the mathematical relationship between the error value, DOP value and Slope value obtained from the simulation statistical model. For example, when the error value is large and the DOP value is high, the system will automatically lower the fault detection threshold to ensure that even small deviations can be discovered and processed in time. On the contrary, if the error value is small and the DOP value is low, the threshold can be appropriately relaxed to reduce unnecessary alarms.

[0044] Step S70: When the test statistic exceeds the target fault detection threshold, it is determined that a fault exists, and the faulty satellite is identified and removed.

[0045] It should be noted that when the test statistic exceeds the preset target fault detection threshold, it means that there are potential satellite faults or abnormal observation data in the system. At this time, it is necessary to accurately identify and eliminate these faulty satellites to ensure the accuracy and reliability of the positioning results.

[0046] Further, when the check statistic exceeds the target fault detection threshold, it is determined that there is a fault and the fault identification process is triggered. Specifically, once it is detected that the check statistic exceeds the set threshold value, it is first determined that there is a potential fault. This step marks the transition from the normal operating state to the fault detection and elimination mode. Then, the statistical analysis method is used to identify and obtain the faulty satellite. In this embodiment, the subset comparison method is adopted. The above method is a method of removing one satellite from all currently available satellites one by one, and re-solving and calculating new test statistics based on the remaining satellites. By comparing the changes in the test statistics after each removal, it can be determined which satellite is most likely to be the source of the fault. Generally, if the test statistic decreases significantly after a certain satellite is removed, it means that the satellite may have a fault or abnormality. The observation data corresponding to the faulty satellite is removed from the pre-selected observation data set to obtain an updated observation data set. Specifically, after the faulty satellite is identified, the next step is to remove the observation data corresponding to the satellite from the pre-selected observation data set to obtain an updated observation data set. This process not only includes deleting relevant observation information such as pseudorange, carrier phase and Doppler shift, but also requires synchronous updating of other auxiliary information, such as satellite ephemeris and clock correction parameters, to ensure data consistency and integrity. Finally, the updated observation data set is used to update the solution value and update the verification statistic to identify and eliminate other faulty satellites. Specifically, the updated observation data set is used to re-perform the least squares solution to obtain a new position, velocity and time (PVT) solution. At the same time, the verification statistic is recalculated to verify whether there are other faulty satellites. If the new verification statistic is still higher than the set threshold value, it indicates that there may be multiple faulty satellites in the system, and the above identification and elimination steps need to be repeated until all faulty satellites are correctly identified and eliminated.

[0047] Step S80, obtaining a target observation data set.

[0048] It should be noted that the above-mentioned target observation data set is the remaining pre-selected observation data set after the observation data corresponding to the faulty satellite is removed. After the faulty satellite is removed, its observation data set is automatically updated to remove all observation information such as pseudorange, carrier phase and Doppler shift related to the faulty satellite. This process needs to be accurate and efficient to avoid the impact of any residual erroneous data on subsequent calculations. In order to ensure the consistency and integrity of the data, the system also needs to synchronously update related auxiliary information, such as satellite ephemeris and clock correction parameters. Next, based on the updated target observation data set, the least squares solution will be re-performed to obtain a new position, velocity and time (PVT) solution result. Since the faulty satellite is removed, the new solution value is usually more accurate and reliable. In addition, by recalculating the dilution of precision (DOP), the impact of the current satellite geometry on positioning accuracy can be evaluated to ensure that the system can maintain a high positioning quality even when the number of available satellites is reduced.

[0049] Ultimately, using the target observation dataset for subsequent processing and analysis can not only improve the robustness and stability of the system, but also enhance the user experience. For example, in a dynamic environment, real-time updating of the observation dataset helps to quickly respond to environmental changes and provide continuous and stable navigation services.

[0050] Step S90, performing calculations based on the target observation data set to obtain the target dilution of precision factor and the number of satellites.

[0051] It should be noted that, first, the pseudo-range observations of the remaining satellites in the target observation data set are used to re-perform the least squares solution to obtain the new position, velocity and time (PVT) solution results. On this basis, the target dilution of precision (DOP) is calculated through the geometric matrix. The DOP value reflects the impact of the satellite geometric layout on the positioning accuracy, including the horizontal dilution of precision (HDOP), the vertical dilution of precision (VDOP), the geometric dilution of precision (GDOP), etc. At the same time, the number of currently available satellites is counted. Ensuring a sufficient number of visible satellites is the basis for maintaining the normal operation of the GNSS system. Normally, at least four satellites are required to provide three-dimensional position information and time correction. However, in practical applications, more satellites can significantly improve positioning accuracy and reliability. Therefore, in addition to calculating the DOP value, it is also necessary to evaluate the number of remaining satellites and their geometric distribution to ensure that the system can provide stable positioning services under various conditions.

[0052] Step S100, making a judgment based on the target precision reduction factor and the number of satellites to obtain a satellite positioning result.

[0053] It should be noted that the calculated target dilution of precision factor can be used to evaluate the impact of the current satellite geometry on positioning accuracy. Therefore, in practical applications, a DOP threshold is usually set. When the calculated DOP value is lower than the threshold, the current satellite geometry is considered suitable for high-precision positioning; otherwise, additional measures may need to be taken, such as extending the observation time or waiting for more satellites to enter the field of view, to improve the satellite geometry. At the same time, it is also crucial to consider the number of currently available satellites. Although the GNSS system theoretically only needs four satellites to provide three-dimensional position information and time correction, in practical applications, more satellites can significantly improve positioning accuracy and reliability. By counting the number of currently available satellites and combining their geometric distribution, the positioning results can be further optimized. For example, if the number of available satellites is large and evenly distributed, higher positioning accuracy can be expected; in the case of a small number of satellites or uneven distribution, the positioning results need to be handled more carefully, and redundant observations need to be added or other enhancement technologies need to be used to improve reliability.

[0054] Further, step S100 includes: when the target precision reduction factor does not exceed the preset threshold and the number of satellites meets the preset number, the satellite positioning result is determined to be valid, and the satellite positioning result is output, and the above satellite positioning result is a least squares result. Specifically, when the target precision reduction factor does not exceed the preset threshold and the number of currently available satellites meets the preset number, the satellite positioning result can be determined to be valid. In this embodiment, it is assumed that a certain vehicle-mounted GNSS receiver observes 6 satellites in real time, and HDOP=2.5 (the preset threshold is 3.0) is calculated, and the satellite signal carrier-to-noise ratio is higher than 35 dB-Hz. At this time: for DOP detection, HDOP=2.5<3.0, the satellite geometry distribution is good; for satellite number detection: 6 satellites> minimum requirement 4 satellites, meet the redundancy requirements, use the least squares method to calculate the user position, and no faulty satellites are found after RAIM residual test, and finally output the positioning result (such as latitude and longitude error ±2m). In this case, the position, velocity and time (PVT) result obtained by the least squares solution is output as the final satellite positioning result. This result is usually of high accuracy and reliability, suitable for a variety of application scenarios, including high-precision navigation and safety-critical applications.

[0055] Further, when the target precision reduction factor exceeds the preset threshold or the number of satellites does not meet the preset number, the satellite positioning result is determined to be invalid, and the step of selecting the preset number of satellites based on the satellite observation data and establishing the pre-selected observation data set is returned to update the satellite positioning result. Specifically, if the target precision reduction factor exceeds the preset threshold or the current number of available satellites does not meet the preset requirements, the satellite positioning result needs to be determined to be invalid. For example, when the DOP is greater than 3.0 and the number of satellites is less than 4, it means that the current satellite geometric distribution is not sufficient to provide reliable positioning services, or the basic positioning requirements cannot be met due to insufficient number of satellites. In this case, unreliable positioning results will not be output, but a series of measures will be taken to update and optimize the observation data set in order to obtain higher quality positioning results. Specifically, first, the optimal satellite subset will be re-evaluated and selected based on the latest observation data and signal quality parameters (such as carrier-to-noise ratio CN0). Satellites with high signal-to-noise ratios and good geometric layouts are given priority to ensure the accuracy and reliability of the solution results. Second, if the number of currently available satellites is insufficient, the observation time will be tried to extend to wait for more satellites to enter the field of view or the quality of existing satellite signals to improve. By increasing the time span of the observation data, the stability and accuracy of the positioning results can be improved. Third, in some cases, auxiliary technologies can be used to compensate for the problem of insufficient number of satellites, such as differential GPS (DGPS) and real-time kinematic positioning (RTK). These technologies can significantly improve positioning accuracy by introducing additional reference station data or other external information sources. Finally, once the pre-selected observation data set is updated, the least squares solution will be performed again, and the target precision reduction factor and the number of satellites will be recalculated. If the new result meets the preset conditions, the updated positioning result is output; otherwise, the above steps are continued to iterate until the requirements are met. In addition, in some specific scenarios (such as only 2 satellites are available in urban canyons), if an effective solution cannot be obtained in the end, it will automatically switch to the degraded mode: combining base station differential data (RTK) or map matching to provide an approximate position; displaying "weak positioning signal" through the human-machine interface, and recording the failure time and environmental data for subsequent analysis. For example, when a drone is flying among high-rise buildings, the HDOP suddenly rises to 4.5 due to signal obstruction. The obstructed satellites (elevation angle <10°) are automatically eliminated, and the top three satellites with high signal-to-noise ratio are prioritized. The IMU data is integrated to complete the positioning and the error is controlled within 5m.

[0056] Through this dynamic adjustment mechanism, the GNSS system can continue to provide reliable services in complex and changing environments. This approach not only improves the robustness and responsiveness of the system, but also enhances the user experience, ensuring that stable and accurate positioning information can be provided even when some satellites fail or are interfered with.

[0057] This embodiment provides a method for generating satellite positioning results, by acquiring satellite observation data and fault detection threshold, selecting a preset number of satellites based on the satellite observation data, establishing a pre-selected observation data set, calculating the pre-selected observation data set to obtain a least squares result, obtaining an error value based on the pre-selected observation data set and the solution value, calculating based on the residual statistic to obtain a test statistic, adjusting the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value to obtain a target fault detection threshold, determining that a fault exists when the test statistic exceeds the target fault detection threshold, identifying the faulty satellite and eliminating it, acquiring a target observation data set, calculating based on the target observation data set to obtain a target precision reduction factor and the number of satellites, judging based on the target precision reduction factor and the number of satellites, and obtaining a satellite positioning result. By using historical data and current observation data to establish a pre-selected observation set, using the least squares method to calculate the solution value, and calculating the test statistic based on the residual statistic, the fault detection threshold is dynamically adjusted in combination with the error value, the precision reduction factor and the error slope, so as to achieve fast and accurate identification and elimination of faulty satellites, thereby improving fault detection efficiency and positioning accuracy.

[0058] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 5 The satellite positioning result generation method step S60 further includes steps S201 to S204: Step S201, calculating the geometric distribution influence coefficient based on the precision reduction factor.

[0059] It should be noted that, assuming the value of the precision reduction factor is DOP, the geometric distribution influence coefficient can be defined as for: This formula reflects the impact of the DOP on the overall geometric layout. The closer the value is to 1, the better the satellite geometric distribution is and the higher the positioning accuracy is.

[0060] Step S202, calculating according to the error value and the preset error slope value to obtain a dynamic adjustment factor.

[0061] It should be noted that the error value can be represented by a pseudorange residual or other measurement error. The error slope describes the change in positioning error caused by a unit pseudorange deviation. In this embodiment, the preset error slope is the maximum error slope. Dynamic adjustment factor The definition is as follows: in, Indicates the error, Indicates the maximum error slope. The larger A value of indicates that the current observed data has a large deviation or anomaly, requiring a stricter fault detection standard; while a smaller A value of indicates that the observed data is relatively stable and the fault detection conditions can be appropriately relaxed.

[0062] Step S203, multiplying the geometric distribution influence coefficient and the dynamic adjustment factor to obtain a comprehensive adjustment coefficient.

[0063] It should be noted that in order to comprehensively evaluate the impact of each satellite on the overall performance of the system, the geometric distribution influence coefficient and the dynamic adjustment factor can be multiplied to obtain the comprehensive adjustment coefficient , the larger The value indicates that the satellite not only has a great influence on the system in terms of geometric distribution, but also its observation data may have large deviations or anomalies, which requires special attention.

[0064] Step S204: Process the fault detection threshold according to the comprehensive adjustment coefficient to obtain a target fault detection threshold.

[0065] It should be noted that in order to further optimize the fault detection threshold and improve the robustness and accuracy of the GNSS system, a scaling processing strategy can be introduced. This strategy adjusts the comprehensive adjustment coefficient by setting the scaling factor calculation method and scaling direction, thereby achieving a more accurate fault detection threshold setting.

[0066] Furthermore, the scaling factor calculation method and scaling direction in the scaling processing strategy are set. Specifically, the scaling factor It can be dynamically calculated based on the current state of the system and historical data. A common method is to determine it based on error statistics and system performance indicators. The specific formula is: in is the maximum error value, is the average error value. The scaling direction depends on the performance requirements of the current system. If the system requires higher sensitivity to detect potential faults, the amplification direction should be selected ( ); On the contrary, if the system wants to reduce the false alarm rate, it can choose to narrow the direction (i.e. ).

[0067] Then, the comprehensive adjustment coefficient is adjusted according to the scaling factor calculation method and the scaling direction to obtain the scaled adjustment coefficient. Specifically, the scaling factor is multiplied by the scaled adjustment coefficient. ,Finally, the fault detection threshold is iteratively optimized to obtain the target fault detection threshold.

[0068] Finally, the comprehensive adjustment coefficient is used to adjust the fault detection threshold. Assume that the initial fault detection threshold is , then the target fault detection threshold for the i-th satellite is It can be expressed as: Where k is the adjustment coefficient, which is set according to historical data. The comprehensive adjustment coefficient is calculated based on the geometric distribution influence coefficient and the dynamic adjustment factor, and the fault detection threshold is dynamically processed accordingly, so that the GNSS system can continue to provide stable and reliable navigation services in complex environments.

[0069] This embodiment calculates a comprehensive adjustment coefficient based on the geometric distribution influence coefficient and the dynamic adjustment factor, and dynamically processes the fault detection threshold accordingly, thereby enhancing the adaptive capability of the system, improving user experience and system reliability.

[0070] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above description, and will not be described in detail later. Figure 6 The satellite positioning result generating method further includes steps S301 to S302 before step S40: Step S301, obtaining satellite navigation sample data and constructing an initial satellite prediction model.

[0071] It should be noted that obtaining satellite navigation sample data is the basis for building a prediction model. These data usually include key information such as pseudorange, carrier phase, and Doppler shift. Signals from different satellites can be captured by GNSS receivers, and the observation data of each epoch can be recorded. In addition, relevant auxiliary information such as satellite ephemeris, clock correction parameters, and environmental conditions (such as ionospheric delay and tropospheric effects) needs to be collected. In order to ensure the diversity and comprehensiveness of the data, it is recommended to collect data from different geographical locations, time periods, and satellite constellations. For example, in a typical experimental setting, multiple fixed observation stations can be selected to continuously record data for several days, covering different time periods during the day and night to capture changes under various environmental conditions. At the same time, considering the characteristics of different satellite constellations (such as GPS, GLONASS, Galileo, and BeiDou), multiple satellite signals can also be mixed to improve the versatility and adaptability of the model. After obtaining enough sample data, the next step is to build an initial satellite prediction model, which is mainly used to predict the changing trends of elevation and clock errors.

[0072] Step S302: training the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model.

[0073] It should be noted that to train the initial satellite prediction model, the model parameters are initialized first, the satellite navigation sample data is input into the initial satellite prediction model for calculation, and the prediction value is obtained. The error value between the prediction value and the true value is calculated according to the loss function, and the gradient of the model parameters is calculated by the back propagation algorithm. The model parameters are iteratively updated according to the gradient through the optimization algorithm until the maximum number of iterations is reached or the error value converges to the preset threshold, and the preset satellite prediction model is obtained. Specifically, assuming that the user's elevation and clock error change slowly over time, in this embodiment, a quadratic polynomial model can be used to fit the historical data as the initial satellite prediction model. Specifically, assuming Indicates time, Indicates elevation, represents the clock error, then the model form is as follows: in, and is the model parameter to be estimated, which can be estimated by the least squares method. Next, the satellite navigation sample data is input into the initial model for calculation to obtain the predicted value. The sample data includes the timestamp and the corresponding elevation and clock difference observations. For each epoch , calculate the predicted elevation output by the model and predicted clock error In order to evaluate the performance of the model, a loss function is defined to calculate the error between the predicted value and the true value. Commonly used loss functions such as mean square error (MSE) are in the form of: Where n is the number of samples, and They are the actual elevation value and the actual clock difference value respectively. Based on the calculated error value, the back propagation algorithm is used to calculate the gradient of the model parameters. The back propagation calculates the partial derivatives of the loss function with respect to each model parameter layer by layer through the chain rule to obtain the gradient information. Then, the model parameters are updated according to the gradient using an optimization algorithm such as gradient descent or Adam iteration. This process continues until the preset maximum number of iterations is reached or the error value converges to the preset threshold. Usually, when the change in the loss function is less than a certain minimum value after several consecutive iterations, the model is considered to have converged. Finally, after multiple iterations, the preset satellite prediction model is obtained. This model can accurately predict the changing trend of elevation and clock difference, and provides strong support for fault detection and positioning accuracy optimization of GNSS systems.

[0074] After obtaining the preset satellite prediction model, the method further includes: evaluating the preset satellite prediction model to obtain an evaluation result; if the evaluation result does not meet the detection requirements, retraining the preset satellite prediction model until the detection requirements are met.

[0075] This embodiment trains the model through sample data and iteratively optimizes until convergence to obtain a preset satellite prediction model, thereby improving positioning accuracy and system robustness, reducing errors and failure rates, and ensuring the stability and reliability of navigation services in complex environments.

[0076] This application also provides a satellite positioning result generation device, please refer to Figure 7 , the device comprises: The acquisition module 10 is used to acquire satellite observation data and fault detection thresholds.

[0077] The construction module 20 is used to select a preset number of satellites based on the satellite observation data to establish a pre-selected observation data set.

[0078] The calculation module 30 is used to calculate the pre-selected observation data set to obtain the least squares result.

[0079] The calculation module 30 is also used to obtain an error value based on the preselected observation data set and the solution value.

[0080] The calculation module 30 is further used to perform calculations based on the residual statistics to obtain a test statistic.

[0081] The optimization module 40 is used to adjust the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value to obtain a target fault detection threshold.

[0082] The processing module 50 is used to determine the existence of a fault, identify the faulty satellite and remove it when the test statistic exceeds the target fault detection threshold.

[0083] The updating module 60 is used to obtain a target observation data set.

[0084] The calculation module 30 is further used to perform calculations based on the target observation data set to obtain the target precision reduction factor and the number of satellites.

[0085] The result module 70 is used to make a judgment based on the target precision reduction factor and the number of satellites to obtain the satellite positioning result.

[0086] The satellite positioning result generating device provided by the present application adopts the satellite positioning result generating method in the above-mentioned embodiment, and can solve the technical problem of how to improve the accuracy of the satellite positioning result. Compared with the prior art, the beneficial effects of the satellite positioning result generating device provided by the present application are the same as the beneficial effects of the satellite positioning result generating method provided by the above-mentioned embodiment, and the other technical features in the satellite positioning result generating device are the same as the features disclosed in the above-mentioned embodiment method, which will not be described in detail here.

[0087] In one embodiment, the optimization module 40 is also used to calculate the geometric distribution influence coefficient based on the precision attenuation factor, calculate according to the error value and the preset error slope value to obtain the dynamic adjustment factor, multiply the geometric distribution influence coefficient and the dynamic adjustment factor to obtain the comprehensive adjustment coefficient, and process the fault detection threshold according to the comprehensive adjustment coefficient to obtain the target fault detection threshold.

[0088] In one embodiment, the optimization module 40 is also used to set the scaling factor calculation method and scaling direction in the scaling processing strategy; adjust the comprehensive adjustment coefficient according to the scaling factor calculation method and scaling direction to obtain the scaled adjustment coefficient; iteratively optimize the fault detection threshold based on the scaled adjustment coefficient to obtain the target fault detection threshold.

[0089] In one embodiment, the result module 70 is also used to determine that the satellite positioning result is valid and output the satellite positioning result when the target precision attenuation factor does not exceed the preset threshold and the number of satellites meets the preset number; when the target precision attenuation factor exceeds the preset threshold or the number of satellites does not meet the preset number, determine that the satellite positioning result is invalid, return to the step of selecting a preset number of satellites based on the satellite observation data, and establish a pre-selected observation data set to update the satellite positioning result.

[0090] In one embodiment, the calculation module 30 is also used to input the observation data in the pre-selected observation data set into a preset satellite prediction model for processing to obtain the predicted elevation and predicted clock error; compare the predicted elevation and predicted clock error with the solution value to obtain the error value.

[0091] In one embodiment, the computing module 30 is also used to obtain satellite navigation sample data and construct an initial satellite prediction model; train the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model; the steps of training the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model include: initializing model parameters; inputting the satellite navigation sample data into the initial satellite prediction model to calculate and obtain a predicted value; calculating according to the loss function to obtain an error value between the predicted value and the true value; calculating through a back propagation algorithm to obtain the gradient of the model parameters; iteratively updating the model parameters through an optimization algorithm according to the gradient until the maximum number of iterations is reached or the error value converges to a preset threshold, thereby obtaining a preset satellite prediction model.

[0092] In one embodiment, the processing module 50 is also used to determine that a fault exists and trigger a fault identification process when the verification statistic exceeds the target fault detection threshold; use a statistical analysis method to identify and obtain a faulty satellite; eliminate the observation data corresponding to the faulty satellite from a pre-selected observation data set to obtain an updated observation data set; and update the solution value and update the calculation of the verification statistic on the updated observation data set to identify and eliminate other existing faulty satellites.

[0093] The present application provides a satellite positioning result generating device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the satellite positioning result generating method in the above-mentioned embodiment 1.

[0094] Reference below Figure 8 , which shows a schematic diagram of the structure of a satellite positioning result generating device suitable for implementing the embodiment of the present application. The satellite positioning result generating device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The satellite positioning result generating device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0095] like Figure 8The satellite positioning result generating device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a ROM (Read Only Memory) 1002 or a program loaded from a storage device 1003 to a RAM (Random Access Memory) 1004. Various programs and data required for the operation of the satellite positioning result generating device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the satellite positioning result generating device to communicate with other devices wirelessly or by wire to exchange data. Although the satellite positioning result generating device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0096] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method described in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0097] The satellite positioning result generation device provided by the present application adopts the satellite positioning result generation method in the above embodiment, which can solve the technical problem of how to improve the accuracy of the satellite positioning result. Compared with the prior art, the beneficial effects of the satellite positioning result generation device provided by the present application are the same as the beneficial effects of the satellite positioning result generation method provided by the above embodiment, and the other technical features in the satellite positioning result generation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0098] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0099] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0100] The present application provides a computer-readable medium having computer-readable program instructions (ie, computer programs) stored thereon for performing calculations to obtain machine-readable program instructions for executing the satellite positioning result generating method in the above-mentioned embodiment.

[0101] The computer-readable medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the calculation is performed to obtain a machine-readable medium that may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0102] The computer-readable medium may be included in the satellite positioning result generating device; or may exist independently without being assembled into the satellite positioning result generating device.

[0103] The computer-readable medium carries one or more programs. When the one or more programs are executed by the satellite positioning result generating device, the satellite positioning result generating device can write a computer program code for performing the operation of the present application in one or more programming languages ​​or a combination thereof. The programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and a conventional procedural programming language, such as "C" language or a similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0104] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0106] The readable medium provided by the present application is a computer-readable medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned satellite positioning result generation method, and can solve the technical problem of how to improve the accuracy of satellite positioning results. Compared with the prior art, the beneficial effects of the computer-readable medium provided by the present application are the same as the beneficial effects of the satellite positioning result generation method provided by the above-mentioned embodiment, and will not be repeated here.

[0107] The present application also provides a computer program product, including a computer program, which implements the steps of the satellite positioning result generating method as described above when the computer program is executed by a processor.

[0108] The computer program product provided by this application can solve the technical problem of how to improve the accuracy of satellite positioning results. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the satellite positioning result generation method provided by the above embodiment, which will not be repeated here.

[0109] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for generating satellite positioning results, characterized in that: The method comprises: Obtain satellite observation data and fault detection thresholds; Selecting a preset number of satellites based on the satellite observation data to establish a preselected observation data set; Calculating the preselected observation data set to obtain a least squares result, wherein the least squares result includes a solution value, a residual statistic, a precision reduction factor, and an error slope value, wherein the solution value includes position, velocity, and time information obtained by solving a satellite receiver; Obtaining an error value according to the preselected observation data set and the solution value; Calculate according to the residual statistic to obtain a test statistic; The fault detection threshold is adjusted according to the error value, the precision reduction factor and a preset error slope value to obtain a target fault detection threshold; When the test statistic exceeds the target fault detection threshold, it is determined that a fault exists, and the faulty satellite is identified and removed; Acquire a target observation data set, where the target observation data set is a pre-selected observation data set remaining after eliminating the observation data corresponding to the faulty satellite; Calculating based on the target observation data set to obtain a target dilution of precision factor and the number of satellites; A judgment is made based on the target precision reduction factor and the number of satellites to obtain a satellite positioning result.

2. The method according to claim 1, characterized in that The step of adjusting the fault detection threshold according to the error value, the precision reduction factor and the preset error slope value to obtain a target fault detection threshold includes: Calculate a geometric distribution influence coefficient based on the dilution of precision factor; Calculating according to the error value and a preset error slope value to obtain a dynamic adjustment factor; Multiplying the geometric distribution influence coefficient and the dynamic adjustment factor to obtain a comprehensive adjustment coefficient; The fault detection threshold is processed according to the comprehensive adjustment coefficient to obtain a target fault detection threshold.

3. The method according to claim 2, characterized in that The step of processing the fault detection threshold according to the comprehensive adjustment coefficient to obtain a target fault detection threshold includes: Set the scaling factor calculation method and scaling direction in the scaling processing strategy; The comprehensive adjustment coefficient is adjusted according to the scaling factor calculation method and the scaling direction to obtain the scaled adjustment coefficient; The fault detection threshold is iteratively optimized based on the scaled adjustment coefficient to obtain a target fault detection threshold.

4. The method according to claim 1, characterized in that The step of determining according to the target dilution of precision factor and the number of satellites to obtain a satellite positioning result comprises: When the target precision reduction factor does not exceed a preset threshold and the number of satellites meets a preset number, the satellite positioning result is determined to be valid, and the satellite positioning result is output, where the satellite positioning result is a least squares result; When the target precision reduction factor exceeds a preset threshold or the number of satellites does not meet a preset number, the satellite positioning result is determined to be invalid, and the process returns to the step of selecting a preset number of satellites based on the satellite observation data and establishing a pre-selected observation data set to update the satellite positioning result.

5. The method according to claim 1, characterized in that The step of obtaining the error value according to the preselected observation data set and the solution value comprises: Inputting the observation data in the pre-selected observation data set into a preset satellite prediction model for processing to obtain predicted elevation and predicted clock error; The predicted elevation and predicted clock error are compared with the solved value to obtain an error value.

6. The method according to claim 5, characterized in that Before the step of inputting the observation data in the pre-selected observation data set into a preset satellite prediction model for processing to obtain predicted elevation and predicted clock error, the method includes: Obtain satellite navigation sample data and build an initial satellite prediction model; Training the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model; The step of training the initial satellite prediction model based on the satellite navigation sample data to obtain a preset satellite prediction model includes: Initialize model parameters; Inputting the satellite navigation sample data into the initial satellite prediction model for calculation to obtain a prediction value; Calculating according to the loss function, obtaining an error value between the predicted value and the true value; By calculating through the back propagation algorithm, the gradient of the model parameters is obtained; The model parameters are iteratively updated through an optimization algorithm according to the gradient until a maximum number of iterations is reached or the error value converges to a preset threshold, thereby obtaining a preset satellite prediction model.

7. The method according to claim 1, characterized in that When the test statistic exceeds the target fault detection threshold, the step of determining that a fault exists, identifying the faulty satellite and removing it comprises: When the test statistic exceeds the target fault detection threshold, it is determined that a fault exists, triggering a fault identification process; The statistical analysis method is used to identify and obtain the faulty satellite; Eliminate the observation data corresponding to the faulty satellite from the pre-selected observation data set to obtain an updated observation data set; The updated observation data set is updated to calculate the solution value and the updated verification statistic to identify and eliminate other existing faulty satellites.

8. A satellite positioning result generating device, characterized in that: The device comprises: An acquisition module, used to acquire satellite observation data and fault detection thresholds; A construction module, configured to select a preset number of satellites based on the satellite observation data to establish a preselected observation data set; A calculation module, used to calculate the pre-selected observation data set to obtain a least squares result, wherein the least squares result includes a solution value, a residual statistic, a precision reduction factor and an error slope value, and the solution value includes position, speed and time information obtained by solving the satellite receiver; The calculation module is further used to obtain an error value according to the preselected observation data set and the solution value; The calculation module is further used to calculate according to the residual statistic to obtain a test statistic; An optimization module, configured to adjust the fault detection threshold according to the error value, the precision reduction factor and a preset error slope value to obtain a target fault detection threshold; A processing module, used for determining the existence of a fault, identifying the faulty satellite and removing it when the test statistic exceeds the target fault detection threshold; An updating module is used to obtain a target observation data set, where the target observation data set is a pre-selected observation data set remaining after eliminating the observation data corresponding to the faulty satellite; The calculation module is further used to perform calculations based on the target observation data set to obtain a target precision reduction factor and a number of satellites; The result module is used to make a judgment based on the target precision reduction factor and the number of satellites to obtain a satellite positioning result.

9. A satellite positioning result generating device, characterized in that: The device comprises: a memory, a processor, and a satellite positioning result generation program stored in the memory and running on the processor, wherein the satellite positioning result generation program is configured to implement the steps of the satellite positioning result generation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a satellite positioning result generation program, and when the satellite positioning result generation program is executed by the processor, the steps of the satellite positioning result generation method according to any one of claims 1 to 7 are implemented.

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