Quality control method and device for state parameters of single Beidou receiver

Abstract

The invention provides a quality control method and device for state parameters of a single Beidou receiver, which are applied to the technical field of navigation positioning, and the method comprises the following steps: estimating a state solution of a current epoch by using a sequential least square method, and then evaluating data quality by calculating an error vector, a weight matrix vector and a variance factor. And finally, setting a threshold value and eliminating satellite parameters exceeding the threshold value, thereby realizing effective quality control on the receiver state parameters. The method not only improves the precision of the state parameters of the single Beidou receiver, but also enhances the resistance of the system to abnormal data, thereby remarkably improving the reliability and stability of single Beidou positioning.

Description

Technical Field

[0001] This application relates to the field of navigation and positioning technologies, and particularly to a method and device for quality control of the state parameters of a single Beidou receiver. Background Art

[0002] Quality control (QC for short) is a very important term in the field of quality management. To meet the predetermined quality requirements and ensure continuous reliability, through certain means, continuous monitoring, detection, and verification are carried out on resources, processes, methods, conditions, products, and services. Activities are carried out to promptly improve the quality problems that do not meet the requirements and make them meet the requirements.

[0003] With the in-depth research on the single Beidou, higher requirements are put forward for the accuracy and real-time performance of the single Beidou. Data quality control has always been an important part of single Beidou data processing. Its main tasks are the detection of errors and the detection and repair of cycle slips. Errors refer to gross errors. The appearance of abnormal single Beidou data will bring a large number of errors. In addition to environmental factors such as complex environments like tree-lined roads, urban canyons, or obstructions, there are also model errors, mechanical abnormal errors, and errors caused by physical factors.

[0004] During the single Beidou positioning and solution process, the appearance of errors will cause great harm to the estimation of positioning parameters. Currently, for the estimation of positioning parameters, sequential least squares estimation and Kalman estimation are commonly used estimation methods. The prerequisite assumptions of these two estimation methods are that the observation vector and the state prediction vector both follow a normal distribution. However, the appearance of errors will cause the observation vector to deviate from the normal distribution assumption, affecting the accuracy of positioning parameters. To ensure the accuracy and precision of the final positioning parameters, it is crucial to detect the errors in the single Beidou data and attenuate or eliminate these errors during the estimation of positioning parameters.

[0005] Therefore, in the prior art, there is a lack of an effective method for detecting the errors in the single Beidou data and attenuating or eliminating these errors. Summary of the Invention

[0006] In view of the deficiencies of the above prior art, this application provides a method and device for quality control of the state parameters of a single Beidou receiver, which is applied to the field of navigation and positioning technologies and has the advantages of being able to effectively detect and eliminate the errors in the single Beidou data in real time and improve the accuracy and precision of the positioning parameters.

[0007] In a first aspect, a method for quality control of the state parameters of a single Beidou receiver, the method includes the steps: S1: obtaining the current epoch data and the previous epoch data of the receiver, and estimating the state solution of the current epoch by sequential least squares; wherein the current epoch data at least includes the current epoch weight matrix, and the previous epoch data at least includes the previous epoch weight matrix; S2: Calculate an error vector according to the state solution of the current epoch; calculate a weight matrix vector according to the current epoch weight matrix and the previous epoch weight matrix; S3: Calculate a variance factor according to the error vector and the weight matrix vector; S4: When the variance factor is greater than a preset error threshold, the corresponding satellite parameters with errors in the current epoch data are removed.

[0008] This application proposes a quality control method for the state parameters of a single Beidou receiver. The method first uses the sequential least squares method to estimate the state solution of the current epoch, and then evaluates the data quality by calculating the error vector, weight matrix vector and variance factor. Finally, by setting a threshold and removing satellite parameters that exceed the threshold, effective quality control of the receiver state parameters is achieved. This method not only improves the accuracy of the state parameters of a single Beidou receiver, but also enhances the system's resistance to abnormal data, thereby significantly improving the reliability and stability of single Beidou positioning.

[0009] Furthermore, in step S1, the equation for estimating the state solution of the current epoch using sequential least squares is: ,in is the current epoch state vector, i.e., the state solution of the current epoch; is the state vector of the previous epoch, Design the matrix for the current epoch, is the weight matrix of the current epoch, is the weight matrix of the previous epoch, is the current epoch observation vector, wherein the current epoch data also includes the current epoch design matrix and the current epoch observation vector; the previous epoch data also includes the previous epoch state vector.

[0010] The present application proposes a quality control method for the state parameters of a single Beidou receiver. By using the equation, the data of the previous epoch can be effectively used to improve the estimation of the state solution of the current epoch. Taking into account the weights of the current epoch data and the previous epoch data, The current epoch data and the previous epoch data are combined, and the historical data and current observations are fully utilized through the use of the sequential least squares estimation method, making the state estimation more accurate and reliable.

[0011] Furthermore, in step S2, the formula for calculating the error vector according to the state solution of the current epoch is: , , ;in is the posterior error vector of the previous epoch, is the a posteriori error vector of the current epoch, is the observation vector of the previous epoch, is the observation vector of the current epoch, is the error vector.

[0012] The present application proposes a quality control method for the state parameters of a single Beidou receiver. The above formula can be used to specifically determine how to calculate the error vector so as to more accurately reflect the difference between the observed data and the estimated state.

[0013] Furthermore, in step S2, the formula for calculating the weight matrix vector according to the current epoch weight matrix and the previous epoch weight matrix is: ,in, is the weight matrix vector.

[0014] This application proposes a quality control method for the state parameters of a single Beidou receiver. The method converts the weight matrix of the previous epoch into and the weight matrix of the current epoch Combine into a new matrix This calculation method takes into account the previous epoch data and the current epoch data, so that the weight matrix vector can more comprehensively reflect the changes in the receiver state parameters.

[0015] Furthermore, the formula for calculating the variance factor according to the error vector and the weight matrix vector is: , where represents the variance factor, is the degree of freedom, is the dimension of the error vector, is the number of parameters to be estimated, yes The transposed matrix of .

[0016] Further, step S4 includes: S41: When the variance factor is greater than a preset error threshold, calculating a design vector according to the current epoch data; S42: Calculate a cofactor matrix according to the design vector and the weight matrix vector; S43: Calculate an error condition matrix according to the cofactor matrix and the weight matrix vector; S44: Calculating the dimension where the error occurs according to the error condition matrix, and removing the corresponding satellite parameters where the error occurs in the current epoch data from the dimension where the error occurs.

[0017] Further, in step S41, the formula for calculating the design vector based on the current epoch data is: , where is the local identity matrix, is the design matrix of the current epoch, is the design vector.

[0018] Further, in step S42, the formula for calculating the cofactor matrix based on the design vector and the weight matrix vector is: ; where is the cofactor matrix, is the weight matrix vector, is the transpose matrix of In step S43, the formula for calculating the error condition matrix based on the cofactor matrix and the weight matrix vector is: , where is the error condition matrix, which is a square matrix with the same dimension as the error vector .

[0019] Further, step S44 includes: S441: Calculate the dimension where an error occurs based on the error condition matrix, and calculate the satellite error in the dimension where the error occurs; S442: Find the maximum value among the satellite errors. When the maximum value of the satellite errors is greater than or equal to the preset satellite error, eliminate the corresponding satellite parameters.

[0020] In a second aspect, a quality control device for the state parameters of a single Beidou receiver operates according to the steps in any of the above methods. The device includes: The first calculation module: used to obtain the current epoch data and the previous epoch data of the receiver, and estimate the state solution of the current epoch by sequential least squares; where the current epoch data includes at least the current epoch weight matrix, and the previous epoch data includes at least the previous epoch weight matrix; The second calculation module: used to calculate the error vector based on the state solution of the current epoch; calculate the weight matrix vector based on the current epoch weight matrix and the previous epoch weight matrix; The third calculation module: used to calculate the variance factor based on the error vector and the weight matrix vector; The error elimination module: used to eliminate the corresponding satellite parameters with errors in the current epoch data when the variance factor is greater than the preset error threshold.

[0021] Beneficial effects: A quality control method and device for the state parameters of a single Beidou receiver proposed in this application. This method first estimates the state solution of the current epoch using the sequential least squares method, and then evaluates the data quality by calculating the error vector, weight matrix vector, and variance factor. Finally, by setting thresholds and eliminating satellite parameters that exceed the thresholds, effective quality control of the receiver state parameters is achieved. This method not only improves the accuracy of the state parameters of the single Beidou receiver but also enhances the system's resistance to abnormal data, thus significantly improving the reliability and stability of single Beidou positioning. Description of the Drawings

[0022] Figure 1 It is a schematic flowchart of a quality control method for the state parameters of a single Beidou receiver proposed in this application.

[0023] Figure 2 It is a schematic structural diagram of a quality control device for the state parameters of a single Beidou receiver proposed in this application.

[0024] Label description: 201, the first calculation module; 202, the second calculation module; 203, the third calculation module; 204, the error elimination module. Detailed Embodiments

[0025] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and marked in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0026] It should be noted that: Similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first, second" are only used for differential description and cannot be understood as indicating or implying relative importance.

[0027] In the quality control process of the state parameters of a single BeiDou receiver, there is a key technical problem: how to effectively detect and process the errors in the single BeiDou data and ensure the accuracy and precision of the positioning parameters. The core of this problem lies in that the existing positioning parameter estimation methods, such as sequential least squares estimation and Kalman estimation, are all based on the assumption that the observation vector and the state prediction vector follow a normal distribution. However, in practical applications, due to environmental factors, model errors, mechanical anomalies, physical factors, etc., the observed data often deviates from the normal distribution, resulting in a serious impact on the accuracy of the positioning parameter estimation. Therefore, a method that can effectively identify and process these abnormal data is needed to maintain the high precision and reliability of the positioning system.

[0028] To solve this problem, this application proposes a quality control method and device for the state parameters of a single BeiDou receiver.

[0029] In the first aspect, referring to Figure 1 , a quality control method for the state parameters of a single BeiDou receiver, the method includes the steps: S1: Obtain the current epoch data and the previous epoch data of the receiver, and use sequential least squares to estimate the state solution of the current epoch; wherein, the current epoch data includes at least the current epoch weight matrix, and the previous epoch data includes at least the previous epoch weight matrix; S2: Calculate the error vector according to the state solution of the current epoch; calculate the weight matrix vector according to the current epoch weight matrix and the previous epoch weight matrix; S3: Calculate the variance factor according to the error vector and the weight matrix vector; S4: When the variance factor is greater than the preset error threshold, eliminate the corresponding satellite parameters with errors in the current epoch data.

[0030] Among them, in step S1, the current epoch data refers to the observation data obtained by the receiver at the current moment, and can be specifically obtained by means of internal storage or real-time transmission of the receiver. Similarly, the previous epoch data refers to the observation data obtained by the receiver at the previous moment.

[0031] Among them, sequential least squares estimation is a recursive estimation method for processing continuous observation data, and can be specifically implemented by using a recursive formula to update the state estimation each time new observation data arrives.

[0032] Among them, in step S2, the error vector is the difference between the observed value and the estimated value, and can be specifically calculated by using the observation equation.

[0033] Among them, the weight matrix vector is a matrix used to represent the reliability of the observation data, and can be specifically constructed according to the accuracy of the observation data.

[0034] Among them, in step S3, the variance factor is an index to measure the overall observation accuracy, and it can be specifically calculated through the error vector and the weight matrix vector.

[0035] Among them, in step S4, the preset error threshold is a standard for judging data quality, and it can be specifically set according to actual application requirements and historical data analysis.

[0036] The core innovation of this application lies in proposing a quality control method for the state parameters of a single Beidou receiver based on sequential least squares estimation and variance factor analysis. This method combines historical data and current observation data, uses sequential least squares estimation to improve the state estimation accuracy, and introduces variance factor analysis to evaluate the overall observation quality. In particular, when the variance factor exceeds the preset error threshold, dynamic quality control is achieved by removing the satellite parameters with errors. This method can not only effectively identify and process abnormal data, but also improve the reliability and stability of the system while ensuring the positioning accuracy.

[0037] The working principle of this application can be described in detail as follows: First, the receiver obtains the observation data of the current epoch and the previous epoch. The acquisition of these data can be completed by reading from the internal memory of the receiver or receiving real-time data streams.

[0038] Next, the sequential least squares method is used to estimate the state solution of the current epoch. This step uses a recursive formula to combine the state estimate of the previous epoch with the current epoch data to obtain an updated state estimate. The choice of the sequential least squares method is based on its ability to effectively process continuous observation data and its high computational efficiency.

[0039] Then, according to the state solution of the current epoch obtained by estimation, the error vector is calculated, and the weight matrix vector is calculated according to the current epoch weight matrix and the previous epoch weight matrix. The error vector reflects the difference between the observed value and the estimated value, while the weight matrix vector represents the reliability of the current epoch weight matrix and the previous epoch weight matrix. The calculation of these two vectors provides the basis for subsequent variance factor analysis.

[0040] Next, the variance factor is calculated using the error vector and the weight matrix vector. As an index to measure the overall observation accuracy, the variance factor can reflect the degree of consistency between the observed data and the estimation model. The calculation of the variance factor involves the ratio of the sum of squares of the error vector to the degrees of freedom, and this step is crucial for evaluating data quality.

[0041] Finally, compare the calculated variance factor with the preset error threshold. When the variance factor is greater than the preset error threshold, it indicates that there may be outliers in the current epoch data. In this case, the system will initiate a rejection procedure to identify and reject the corresponding satellite parameters with errors in the current epoch data. This step is executed after the data processing of each epoch is completed but before the start of the next epoch.

[0042] Through this series of steps, the method of the present application can effectively identify and process abnormal data while maintaining high-precision positioning, thereby improving the quality control effect of the state parameters of a single Beidou receiver.

[0043] As a preferred implementation manner, the specific embodiments of the present application can be described as follows: In a practical application scenario, assume that a single Beidou receiver is used for high-precision positioning tasks. The receiver receives signals from 10 visible satellites per second to form the observation data of one epoch.

[0044] First, the receiver obtains the data of the current epoch (at time k) and the data of the previous epoch (at time k - 1). These data include 10 sets of satellite pseudorange observations, carrier phase observations, and satellite position information. Through this information, the weight matrix of the current epoch, the design matrix of the current epoch, and the observation vector of the current epoch, the state vector of the previous epoch, and the weight matrix of the previous epoch can be calculated. The data is temporarily stored in the high-speed cache inside the receiver.

[0045] Next, use the sequential least squares method to estimate the state solution of the current epoch. Specifically, the following recurrence formula is adopted: .

[0046] Then, calculate the error vector and the weight matrix vector. The error vector is calculated by the following formula: , , ; The weight matrix vector is composed of the weight matrix of the previous epoch and the weight matrix of the current epoch: ; . Next, calculate the variance factor : , where is the degree of freedom, is the dimension of the error vector, is the number of parameters to be estimated, is The transposed matrix. Specifically, the parameters to be estimated include, but are not limited to, the position of the receiver (usually coordinates in three-dimensional space, i.e., three parameters x, y, z), the clock bias of the receiver (one parameter), and other possible model parameters (such as atmospheric delay parameters, etc.). Therefore, The specific value depends on the positioning model and assumptions used.

[0047] Assume that the preset error threshold is 500. When the calculated variance factor is greater than 500, it indicates that there is a large difference between the state parameters estimated in the current epoch and the actual state of the single Beidou receiver. The system will eliminate the satellite data that causes the error and make it not participate in the subsequent calculations.

[0048] This process is repeated in each epoch (i.e., every second), ensuring continuous high-quality control of the state parameters of the single Beidou receiver. In this way, even in the presence of abnormal observation values, the system can maintain stable high-precision positioning performance.

[0049] In the above process, in step S1, the equation for estimating the state solution of the current epoch using sequential least squares in this application is: , where is the state vector of the current epoch, that is, the state solution of the current epoch; is the state vector of the previous epoch, is the design matrix of the current epoch, is the weight matrix of the current epoch, is the weight matrix of the previous epoch, is the observation vector of the current epoch. Among them, the data of the current epoch also includes the design matrix of the current epoch and the observation vector of the current epoch; the data of the previous epoch also includes the state vector of the previous epoch.

[0050] Specifically, in this method considers the weights of the current epoch data and the previous epoch data and can be regarded as a weighting factor that balances the influence of the current observation and the historical state. combines the current epoch data and the previous epoch data. By adjusting the value of , the contribution degree of the current epoch data and the previous epoch data to the final state solution can be controlled. For example, in the case of weak signals or poor observation conditions, the weight of can be increased to rely more on historical data and improve the stability of state estimation.

[0051] By providing this specific calculation method, the present application not only fills the gap in the prior art where there are no detailed calculation steps, but also provides a powerful tool for improving the quality control of the state parameters of a single Beidou receiver. Compared with the situation lacking a clear calculation method, the method of the present application can estimate the state solution of the current epoch more accurately and efficiently, laying a solid foundation for subsequent error detection and parameter quality control.

[0052] In the above process, in step S2, the formula for calculating the error vector based on the state solution of the current epoch is: , , ; where is the posterior error vector of the previous epoch, is the posterior error vector of the current epoch, is the observation vector of the previous epoch, is the observation vector of the current epoch, is the error vector.

[0053] During the single Beidou positioning calculation process, the occurrence of errors will bring great harm to the estimation of positioning parameters. Currently, for the estimation of positioning parameters, sequential least squares estimation and Kalman estimation are commonly used estimation methods. The hypothesis premise of these two estimation methods is that both the observation vector and the state prediction vector follow a normal distribution, and the occurrence of errors will cause the observation vector to deviate from the normal distribution hypothesis, affecting the accuracy of positioning parameters.

[0054] To ensure the accuracy and precision of the final positioning parameters, the present application proposes a specific method for calculating the error vector. This method calculates the posterior error vectors of the previous epoch and the current epoch respectively, and then combines them into a complete error vector. The posterior error vector of the previous epoch is calculated by the difference between the observation vector of the previous epoch and the state solution of the current epoch. The posterior error vector of the current epoch is calculated by the difference between the observation vector of the current epoch and the difference after mapping the state solution of the current epoch through the design matrix . Finally, and are combined into a complete error vector .

[0055] The error vector calculation method of this application has synergistic effects with the sequential least squares estimation method. The sequential least squares estimation provides the state solution of the current epoch, and the error vector calculation method of this application utilizes this state solution and combines the observation data of the previous epoch and the current epoch to generate a more comprehensive error vector. This synergistic effect enables the system to more accurately identify and quantify the differences between the observation data and the estimated state, thereby providing a more reliable basis for subsequent abnormal data detection and processing.

[0056] Further, in step S2, the formula for calculating the weight matrix vector based on the current epoch weight matrix and the previous epoch weight matrix is: , where is the weight matrix vector.

[0057] In this application, the calculation method of the weight matrix vector has various possible implementation manners. For example, the matrix splicing method can be adopted to splice the weight matrix of the previous epoch and the weight matrix of the current epoch vertically into a new matrix . Another implementation manner is to perform matrix operations to perform weighted combination on the two weight matrices to obtain the final weight matrix vector.

[0058] This calculation method of the weight matrix vector has a positive interaction with the aforementioned sequential least squares estimation method. The sequential least squares estimation provides the state solution of the current epoch, and the calculation of the weight matrix vector utilizes this state solution and also considers the information of the previous epoch. This combination enables the quality control process to more comprehensively consider the state changes between epochs, thereby improving the accuracy of error detection.

[0059] Specifically, the dimension of the weight matrix vector P depends on the dimensions of the weight matrix of the previous epoch and the weight matrix of the current epoch. Assume that is an a×a matrix, and is a b×b matrix, then the finally obtained weight matrix vector will be an (a + b)×(a + b) matrix.

[0060] Further, in step S3, the formula for calculating the variance factor based on the error vector and the weight matrix vector is: , where in the formula, represents the variance factor, is the degree of freedom, is the dimension of the error vector, is the number of parameters to be estimated, is 's transposed matrix.

[0061] This application provides a specific method for calculating the variance factor. Specifically, the variance factor is obtained by multiplying the transpose of the error vector by the weight matrix vector and the error vector , and then dividing by the degrees of freedom . Here, is the dimension of the error vector, is the number of parameters to be estimated.

[0062] This calculation method takes into account the error vector and the weight matrix vector. By introducing the degrees of freedom (n - m), it can adapt to datasets of different scales and different numbers of parameters to be estimated. And matrix operations can be used to efficiently process large amounts of data.

[0063] The variance factor calculated by this method can accurately reflect the quality of the data at the current epoch. When the variance factor is greater than the preset error threshold, it can be determined that there are quality problems in the data at the current epoch, thus providing a basis for subsequent data processing and quality control. This method improves the accuracy and reliability of the quality control of the state parameters of a single BeiDou receiver.

[0064] In some of the above embodiments of this application, step S4 is proposed to eliminate the corresponding satellite parameters with errors in the data at the current epoch. However, in this process, simply eliminating satellite parameters based on the variance factor being greater than the preset error threshold may not be precise and comprehensive enough. This method may result in the incorrect elimination of useful satellite parameters or the omission of some satellite parameters with errors. Therefore, a more precise and comprehensive method is needed to identify and eliminate the satellite parameters with errors.

[0065] Furthermore, step S4 includes: S41: When the variance factor is greater than the preset error threshold, calculate the design vector according to the data at the current epoch; S42: Calculate the covariance matrix according to the design vector and the weight matrix vector; S43: Calculate the error condition matrix according to the covariance matrix and the weight matrix vector; S44: Calculate the dimension with errors according to the error condition matrix, and in the dimension with errors, eliminate the corresponding satellite parameters with errors in the data at the current epoch.

[0066] Specifically, when the variance factor is greater than the preset error threshold, first calculate the design vector according to the data at the current epoch. This step establishes the relationship between the observed values and the unknown parameters, providing a mathematical basis for subsequent analysis. Various methods can be used to calculate the design vector, such as the least squares method or the Kalman filter, etc. The specific choice depends on the actual application scenario and accuracy requirements.

[0067] Next, calculate the covariance matrix according to the design vector and the weight matrix vector. The covariance matrix reflects the correlation and precision between the observed values, and its calculation can adopt matrix operations or iterative methods. For example, the Cholesky decomposition method can be used to improve the calculation efficiency. The accurate calculation of the covariance matrix is crucial for subsequent error analysis.

[0068] Then, based on the covariance matrix and the weight matrix vector, calculate the error condition matrix. The error condition matrix describes the distribution characteristics of the errors, and its calculation can adopt methods such as matrix multiplication or eigenvalue decomposition. The accuracy of the error condition matrix directly affects the effect of the final error identification.

[0069] Finally, calculate the dimensions where errors occur according to the error condition matrix, and eliminate the corresponding satellite parameters in these dimensions. This step can adopt the threshold method or statistical test methods to judge the error dimensions. For example, an error threshold can be set, and when the error in a certain dimension exceeds this threshold, it is considered that there are significant errors in this dimension.

[0070] This step-by-step method realizes more accurate and comprehensive error identification and elimination through gradual refinement of the analysis. Compared with the method that only relies on the variance factor, the technical solution of this application can more accurately locate the error source, reduce the risk of miseliminating useful data, and at the same time improve the ability to identify real errors.

[0071] Furthermore, in step S41, the formula for calculating the design vector according to the current epoch data is: , where is the local identity matrix, is the design matrix of the current epoch, is the design vector.

[0072] Specifically, the design matrix calculation method proposed in this application has the following characteristics and advantages: First, the introduction of the local identity matrix enhances the stability of the design matrix. The identity matrix has special mathematical properties, with elements on its diagonal being 1 and other elements being 0. This structure can improve the invertibility and numerical stability of the matrix while maintaining the original information.

[0073] Second, combining the design matrix of the current epoch with the local identity matrix forms a more complete design vector . This combination method retains the observation information of the current epoch and at the same time introduces additional constraint conditions, which helps to improve the accuracy and reliability of the overall estimation.

[0074] Furthermore, this calculation method of the design matrix can adapt to different observation conditions and the number of satellites. When the number of satellites or the observation conditions change, only the dimension of needs to be adjusted, while keeping the calculation formula unchanged, thus ensuring the flexibility and adaptability of the method. The dimension, while keeping the calculation formula unchanged, thus ensuring the flexibility and adaptability of the method.

[0075] Thus, the design matrix calculation method proposed in this application and the foregoing steps form a complete quality control process. After obtaining the current epoch data and calculating the variance factor, if the variance factor exceeds the preset error threshold, this calculation step of the design matrix will be triggered. The accurate design matrix lays a foundation for the subsequent calculation of the cofactor matrix and the error condition matrix, and finally realizes the accurate identification and elimination of the satellite parameters with errors.

[0076] By providing this specific calculation formula, this application solves the problem of the lack of a clear calculation method in traditional data quality control. The calculation method provided in this application can ensure the accuracy of the design matrix, thereby improving the calculation accuracy in subsequent steps. In addition, this method also improves the repeatability and verifiability of the entire quality control process because there is now a clear mathematical expression to guide the calculation process.

[0077] Furthermore, in step S42, the formula for calculating the cofactor matrix according to the design vector and the weight matrix vector is: ; where is the cofactor matrix, is the weight matrix vector, is the transpose matrix of; In step S43, the formula for calculating the error condition matrix according to the cofactor matrix and the weight matrix vector is: , where is the error condition matrix, which is a square matrix with the same dimension as the error vector .

[0078] Among them, the calculation of the cofactor matrix involves the inverse matrix of the weight matrix vector , the design matrix and its transpose matrix. This formula takes into account the weights of the observed data and the influence of the design matrix, and can reflect the correlation between the observed values.

[0079] The calculation of the error condition matrix is based on the cofactor matrix and the weight matrix vector . This matrix has the same dimension as the error vector and can be directly used in the subsequent error detection process. For example, in a specific implementation, a threshold, such as 0.05, can be set to judge the error condition matrix whether the elements in are significant. If a diagonal element in

[0080] is greater than this threshold, it can be considered that the corresponding observed value may be abnormal and further inspection and processing are required.

[0081] Further, step S44 includes: S441: Calculate the dimensions where errors occur based on the error condition matrix, and calculate the satellite errors in the dimensions where errors occur; S442: Find the maximum value among the satellite errors. When the maximum value of the satellite errors is greater than or equal to the preset satellite error, eliminate the corresponding satellite parameters.

[0082] Among them, the specific process of eliminating the satellite parameters corresponding to the errors in this application is as follows: First, based on the error condition matrix calculate the dimensions where errors occur. The error condition matrix is a square matrix with the same dimension as the error vector , and its diagonal elements reflect the error contributions in each dimension. By analyzing 's diagonal elements, it is possible to determine which dimensions may have significant errors.

[0083] Secondly, in the identified dimensions that may have errors, calculate the specific satellite errors. The formula for calculating the satellite errors is: where i is the row of the error condition matrix and the error vector , j is the column of the error condition matrix and the error vector , is the number of parameters to be estimated, is the satellite error, represents the parameter in the i-th row and j-th column of the condition matrix, represents the parameter in the i-th row of the condition matrix, represents the parameter in the j-th column of the error vector.

[0084] Then, find the maximum value among the calculated satellite errors, that is: .

[0085] Finally, compare the found maximum satellite error value with the preset satellite error. If the maximum satellite error value is greater than or equal to the preset satellite error, the satellite parameters corresponding to the maximum satellite error are excluded. The preset satellite error can be adjusted according to specific application scenarios and accuracy requirements. For example, if the preset satellite error is 5.0 and the maximum value in the satellite errors is greater than or equal to 5.0, the satellite parameters corresponding to this error are excluded and do not participate in subsequent iterative parameter estimation.

[0086] In a second aspect, referring to Figure 2 , a quality control device for single BeiDou receiver status parameters operates according to the steps in any of the above methods. The device includes: The first calculation module 201: is used to obtain the current epoch data and the previous epoch data of the receiver, and estimate the status solution of the current epoch by using sequential least squares; wherein, the current epoch data includes at least the current epoch weight matrix, and the previous epoch data includes at least the previous epoch weight matrix; The second calculation module 202: is used to calculate the error vector according to the status solution of the current epoch; calculate the weight matrix vector according to the current epoch weight matrix and the previous epoch weight matrix; The third calculation module 203: is used to calculate the variance factor according to the error vector and the weight matrix vector; The error elimination module 204: is used to eliminate the corresponding satellite parameters with errors in the current epoch data when the variance factor is greater than the preset error threshold.

[0087] The quality control device for single BeiDou receiver status parameters proposed in this application includes four key modules. The first calculation module 201 estimates the status solution of the current epoch by using the sequential least squares method through obtaining the current epoch data and the previous epoch data, providing the basic data for subsequent error detection. The second calculation module 202 calculates the error vector and the weight matrix vector based on the result of the first calculation module 201, and these vectors are used to quantify the errors and reliabilities of the observed data. The third calculation module 203 calculates the variance factor by using the result of the second calculation module, and this factor reflects the overall consistency between the observed data and the estimation result. The error elimination module 204 identifies and eliminates the satellite parameters containing significant errors by comparing the variance factor with the preset error threshold, thereby improving the positioning accuracy.

[0088] Specifically, the first calculation module 201 can be implemented in various ways. For example, the recursive least squares method or the batch least squares method can be used to estimate the status solution of the current epoch. Among them, the recursive least squares method can process new observed data in real time and is suitable for status estimation in dynamic environments. The batch least squares method, on the other hand, can collect data of multiple epochs within a certain time period for overall optimization and is suitable for static positioning scenarios with high accuracy requirements.

[0089] When calculating the error vector and weight matrix vector, the second calculation module 202 can consider different weight allocation strategies. For example, the weights of the observation values can be dynamically adjusted according to factors such as satellite elevation angle and signal-to-noise ratio to improve the reliability of the calculation results. In addition, an adaptive weight algorithm can be introduced to automatically adjust the weights of each observation value according to the performance of historical data.

[0090] When calculating the variance factor, the third calculation module 203 can adopt different statistical models. In addition to the traditional chi-square test, robust estimation methods such as the Huber weight function or the Denmark method can also be considered to reduce the influence of outliers on the calculation of the variance factor.

[0091] Multiple strategies can be adopted for the implementation of the error rejection module 204. In addition to simple threshold judgment, fuzzy logic or machine learning algorithms can be introduced to identify and reject abnormal data. For example, a neural network model trained based on historical data can be established to predict and identify potential abnormal satellite parameters.

[0092] Through their collaborative work, these four modules achieve effective quality control of the state parameters of a single Beidou receiver. The state solution provided by the first calculation module 201 lays the foundation for the calculations of the subsequent modules. The second calculation module 202 and the third calculation module 203 quantify the quality of the observation data through the calculations of the error vector, weight matrix vector, and variance factor. Finally, based on these calculation results, the error rejection module 204 specifically rejects low-quality satellite parameters.

[0093] Thus, this device can systematically detect and reject errors in single Beidou data, solving the problem in the prior art of lacking effective methods to handle these errors. By real-time monitoring and processing data quality, this device significantly improves the accuracy and precision of the positioning parameters. Especially in complex environments such as urban canyons or areas with severe multipath effects, the advantages of this device are more obvious. It can effectively identify and reject satellite data that is greatly affected by the environment, thereby ensuring the quality of the remaining data and further improving the reliability of the positioning results.

[0094] In the embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. Also, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0095] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] Furthermore, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0097] In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0098] The above description is only for the embodiments of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A quality control method for the state parameters of a single Beidou receiver, characterized in that, The method comprises the steps of: S1: obtaining the current epoch data and the previous epoch data of the receiver, and estimating the state solution of the current epoch by sequential least squares; wherein the current epoch data at least includes the current epoch weight matrix, and the previous epoch data at least includes the previous epoch weight matrix; S2: Calculate an error vector according to the state solution of the current epoch; calculate a weight matrix vector according to the current epoch weight matrix and the previous epoch weight matrix; S3: Calculate a variance factor according to the error vector and the weight matrix vector; S4: When the variance factor is greater than a preset error threshold, the corresponding satellite parameters with errors in the current epoch data are removed.

2. The quality control method for the state parameters of a single Beidou receiver according to claim 1, characterized in that, In step S1, the equation for estimating the state solution of the current epoch using sequential least squares is as follows: , where is the state vector of the current epoch, i.e., the state solution of the current epoch; is the state vector of the previous epoch, is the design matrix of the current epoch, is the weight matrix of the current epoch, is the weight matrix of the previous epoch, is the observation vector of the current epoch. Among them, the current epoch data further includes the design matrix of the current epoch and the observation vector of the current epoch; the previous epoch data further includes the state vector of the previous epoch.

3. A quality control method for the state parameters of a single Beidou receiver according to claim 2, characterized in that, In step S2, the formula for calculating the error vector according to the state solution of the current epoch is: , , ; where is the posterior error vector of the previous epoch, is the posterior error vector of the current epoch, is the observation vector of the previous epoch, is the observation vector of the current epoch, is the error vector.

4. The quality control method for the state parameters of a single Beidou receiver according to claim 3, characterized in that, In step S2, the formula for calculating the weight matrix vector based on the current epoch weight matrix and the previous epoch weight matrix is: , where is the weight matrix vector.

5. A quality control method for the state parameters of a single Beidou receiver according to claim 1, characterized in that, In step S3, the formula for calculating the variance factor based on the error vector and the weight matrix vector is: , where represents the variance factor, is the degree of freedom, is the dimension of the error vector, is the number of parameters to be estimated, is the transpose matrix of 6. A quality control method for the state parameters of a single Beidou receiver according to claim 5, characterized in that, Step S4 includes: S41: When the variance factor is greater than a preset error threshold, calculating a design vector according to the current epoch data; S42: Calculate a cofactor matrix according to the design vector and the weight matrix vector; S43: Calculate an error condition matrix according to the cofactor matrix and the weight matrix vector; S44: Calculating the dimension where the error occurs according to the error condition matrix, and removing the corresponding satellite parameters where the error occurs in the current epoch data from the dimension where the error occurs.

7. A quality control method for the state parameters of a single BeiDou receiver according to claim 6, characterized in that In step S41, the formula for calculating the design vector based on the current epoch data is: , where is the local identity matrix, is the design matrix of the current epoch, is the design vector.

8. A quality control method for the state parameters of a single BeiDou receiver according to claim 7, characterized in that In step S42, the formula for calculating the cofactor matrix based on the design vector and the weight matrix vector is: ; where is the cofactor matrix, is the weight matrix vector, is the transpose matrix of In step S43, the formula for calculating the error condition matrix based on the cofactor matrix and the weight matrix vector is: , where is the error condition matrix, which is a square matrix with the same dimension as the error vector .

9. A quality control method for the state parameters of a single Beidou receiver according to claim 8, characterized in that, Step S44 includes: S441: Calculating the dimension where the error occurs according to the error condition matrix, and calculating the satellite error in the dimension where the error occurs; S442: Find the maximum value of the satellite errors, and when the maximum value of the satellite errors is greater than or equal to a preset satellite error, remove the corresponding satellite parameters.

10. A quality control device for the state parameters of a single Beidou receiver, which runs the steps in the method according to any one of claims 1-9, characterized in that, The device comprises: The first calculation module is used to obtain the current epoch data and the previous epoch data of the receiver, and estimate the state solution of the current epoch by sequential least squares; wherein the current epoch data at least includes the current epoch weight matrix, and the previous epoch data at least includes the previous epoch weight matrix; A second calculation module: used to calculate an error vector according to the state solution of the current epoch; and calculate a weight matrix vector according to the current epoch weight matrix and the previous epoch weight matrix; A third calculation module: used to calculate the variance factor according to the error vector and the weight matrix vector; The error elimination module is used to eliminate the corresponding satellite parameters with errors in the current epoch data when the variance factor is greater than a preset error threshold.

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