Method for determining position signal error model and method for determining position signal error

Abstract

The present application discloses a method for determining a positioning confidence error model and a method for determining a positioning confidence error, and belongs to the field of communication technology. The method comprises: obtaining the global navigation satellite system GNSS observation information of the receiver; solving the GNSS observation information to obtain the positioning result, and obtaining the confidence error characteristic value based on the intermediate solution variables in the solution process; inputting the confidence error characteristic value and the confidence error true value corresponding to the positioning result into the regression model for training to obtain the positioning confidence error model. According to the embodiment of the present application, the accuracy of the positioning confidence error of the real-time positioning result can be improved, thereby improving the reliability of the evaluation of the positioning result.

Description

Technical Field

[0001] The present application belongs to the field of positioning technology, and specifically relates to a method for determining a positioning signal error model and a method for determining a positioning signal error. Background Art

[0002] With the development of positioning technology, satellite positioning technology has been applied in various fields, such as map navigation, unmanned driving, etc. At the same time, the demand for high-precision positioning in various fields is becoming increasingly urgent.

[0003] Since the positioning accuracy of the global satellite navigation system is determined by the positioning signal error, wherein the positioning signal error is determined based on the observed value of the satellite positioning information and a threshold, and the threshold is set by an empirical model, however, the threshold set by the empirical model is inaccurate, resulting in inaccurate positioning signal error. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method for determining a positioning signal error model and a method for determining a positioning signal error, which can solve the problem that the threshold set by the empirical model in the related art is inaccurate, thereby causing inaccurate positioning signal error.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a positioning signal error model, which is applied to an electronic device, and the method includes:

[0006] Obtaining the receiver's Global Navigation Satellite System (GNSS) observation information;

[0007] Solve the GNSS observation information to obtain the positioning result, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0008] The confidence error characteristic value and the true value of the confidence error corresponding to the positioning result are input into the regression model for training to obtain the positioning confidence error model.

[0009] In a second aspect, an embodiment of the present application provides a method for determining a positioning signal error, comprising:

[0010] Obtain real-time GNSS observation information from the receiver;

[0011] Solve the GNSS observation information to obtain real-time positioning results, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0012] The confidence error characteristic value is input into the positioning confidence error model as in the first aspect to obtain the positioning confidence error of the real-time positioning result.

[0013] In a third aspect, an embodiment of the present application provides a method for determining a positioning information error model, including:

[0014] Get the GNSS observation information of the receiver;

[0015] Solve the GNSS observation information to obtain the positioning result, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0016] Inputting the confidence error characteristic value and the confidence error true value corresponding to the positioning result into the regression model for training to obtain a positioning confidence error model, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model and a single point solution sub-model;

[0017] When the positioning result is a floating point solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning results of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance, so as to obtain a floating point solution sub-model;

[0018] When the positioning result is a fixed solution, the confidence error characteristic value includes at least one of a sign of a Doppler observation value, a Doppler unit weighted mean error, a pseudorange unit weighted mean error, a carrier unit weighted mean error, an average carrier-to-noise ratio, a unit weighted mean error of an ambiguity search optimal solution, a position difference calculated by the ambiguity fixed by the first frequency point and the ambiguity fixed by the second frequency point, a positioning east direction covariance, and a positioning north direction covariance, so as to obtain a fixed solution sub-model;

[0019] When the positioning result is a single-point solution, the confidence error characteristic value includes at least one of the position posterior residual, the velocity posterior residual calculated by the Kalman filter, the Dilution of Precision (DOP) value, the posterior residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio to obtain a single-point solution sub-model.

[0020] In a fourth aspect, an embodiment of the present application provides a method for determining a positioning signal error, comprising:

[0021] Obtain real-time GNSS observation information from the receiver;

[0022] Solve the GNSS observation information to obtain real-time positioning results, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0023] Inputting the confidence error characteristic value into the positioning confidence error model in the third aspect to obtain the positioning confidence error of the real-time positioning result, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model, and a single point solution sub-model;

[0024] Among them, when the real-time positioning result is a floating-point solution, the confidence error eigenvalue is input into the floating-point solution sub-model to obtain the positioning confidence error;

[0025] When the real-time positioning result is a fixed solution, the confidence error eigenvalue is input into the fixed solution sub-model to obtain the positioning confidence error;

[0026] When the real-time positioning result is a single-point solution, the confidence error eigenvalue is input into the single-point solution sub-model to obtain the positioning confidence error.

[0027] In a fifth aspect, an embodiment of the present application provides a device for determining a positioning signal error model, including:

[0028] An acquisition module is used to obtain the GNSS observation information of the receiver;

[0029] A solution module is used to solve the GNSS observation information to obtain the positioning result, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0030] The training module is used to input the confidence error characteristic value and the confidence error true value corresponding to the positioning result into the regression model for training to obtain the positioning confidence error model.

[0031] In a sixth aspect, an embodiment of the present application provides a device for determining a positioning signal error, comprising:

[0032] An acquisition module is used to obtain real-time GNSS observation information of the receiver;

[0033] The solution module is used to solve the GNSS observation information to obtain the real-time positioning result, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0034] The confidence error determination module is used to input the confidence error characteristic value into the positioning confidence error model according to any one of claims 1 to 7 to obtain the positioning confidence error of the real-time positioning result.

[0035] In a seventh aspect, an embodiment of the present application provides a device for determining a positioning signal error model, comprising:

[0036] An acquisition module is used to obtain the GNSS observation information of the receiver;

[0037] A solution module is used to solve the GNSS observation information to obtain the positioning result, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0038] A training module, used for inputting the confidence error characteristic value and the confidence error true value corresponding to the positioning result into the regression model for training to obtain a positioning confidence error model, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model and a single point solution sub-model;

[0039] Wherein, when the positioning result is a floating point solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning results of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance, so as to obtain a floating point solution sub-model;

[0040] When the positioning result is a fixed solution, the confidence error characteristic value includes at least one of a sign of a Doppler observation value, a Doppler unit weighted mean error, a pseudorange unit weighted mean error, a carrier unit weighted mean error, an average carrier-to-noise ratio, a unit weighted mean error of an ambiguity search optimal solution, a position difference calculated by the ambiguity fixed by the first frequency point and the ambiguity fixed by the second frequency point, a positioning east direction covariance, and a positioning north direction covariance, so as to obtain a fixed solution sub-model;

[0041] When the positioning result is a single-point solution, the confidence error characteristic value includes at least one of the position posterior residual, the velocity posterior residual calculated by the Kalman filter, the precision factor DOP value, the posterior residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio to obtain a single-point solution sub-model.

[0042] In an eighth aspect, an embodiment of the present application provides a device for determining a positioning signal error, comprising:

[0043] An acquisition module is used to obtain real-time GNSS observation information of the receiver;

[0044] The solution module is used to solve the GNSS observation information to obtain the real-time positioning result, and obtain the confidence error characteristic value based on the intermediate solution variables in the solution process;

[0045] A confidence error determination module, used for inputting the confidence error characteristic value into the positioning confidence error model as claimed in claim 9 or 10 to obtain the positioning confidence error of the real-time positioning result, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model, and a single point solution sub-model;

[0046] Among them, the confidence error determination module is specifically used to input the confidence error characteristic value into the floating-point solution sub-model to obtain the positioning confidence error when the real-time positioning result is a floating-point solution;

[0047] The confidence error determination module is specifically used to input the confidence error characteristic value into the fixed solution sub-model to obtain the positioning confidence error when the real-time positioning result is a fixed solution;

[0048] The confidence error determination module is specifically used to input the confidence error characteristic value into the single-point solution sub-model to obtain the positioning confidence error when the real-time positioning result is a single-point solution.

[0049] In the ninth aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and when the program or instruction is executed by the processor, the steps of the method in the first aspect, the second aspect, the third aspect, or the fourth aspect are implemented.

[0050] In the tenth aspect, an embodiment of the present application provides a readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, the steps in the first aspect, the second aspect, the third aspect, or the fourth aspect are implemented.

[0051] The method for determining a positioning confidence error model and a method for determining a positioning confidence error provided in the embodiments of the present application obtain positioning results by solving GNSS observation information. During the solving process, intermediate solution variables can be obtained, thereby determining the confidence error characteristic values ​​corresponding to the intermediate solution variables, and then the confidence error characteristic values ​​and the true values ​​of the confidence errors of the GNSS observation information are input into a regression model, and the regression model is trained to obtain a positioning confidence error model. Since the intermediate solution variables have a strong correlation with the positioning confidence errors, and the positioning confidence error model is obtained through training based on the intermediate solution variables, the positioning confidence error of the real-time positioning result is determined using the trained positioning confidence error model, which can improve the accuracy of the positioning confidence error of the real-time positioning result, thereby improving the reliability of evaluating the positioning result. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a method for determining a positioning signal error model provided in an embodiment of the present application;

[0053] Figure 2 It is a structural schematic diagram of a neural network regression model provided in an embodiment of the present application;

[0054] Figure 3 It is a flowchart of a method for determining a positioning signal error provided in an embodiment of the present application;

[0055] Figure 4 It is a flowchart of another method for determining a positioning signal error model provided in an embodiment of the present application;

[0056] Figure 5It is a flowchart of another method for determining a positioning signal error provided in an embodiment of the present application;

[0057] Figure 6 It is a structural schematic diagram of a device for determining a positioning signal error model provided in an embodiment of the present application;

[0058] Figure 7 It is a structural schematic diagram of a device for determining a positioning signal error provided in an embodiment of the present application;

[0059] Figure 8 It is a structural schematic diagram of another device for determining a positioning signal error model provided in an embodiment of the present application;

[0060] Fig. 9 It is a structural schematic diagram of another device for determining a positioning signal error provided in an embodiment of the present application;

[0061] Fig.10 It is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0063] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0064] During the GNSS positioning process, there will be a positioning signal error between the GNSS positioning result and the actual position. Since the positioning accuracy of the global satellite navigation system is determined by the positioning signal error, the positioning signal error is determined based on the observed value of the satellite positioning information and the threshold, and the threshold is set by the empirical model. However, the threshold set by the empirical model is inaccurate, which leads to inaccurate positioning signal error, affecting the evaluation of the GNSS positioning results.

[0065] Based on this, the embodiments of the present application provide a method for determining a positioning signal error model, a method, device, equipment and storage medium for determining a positioning signal error, which can improve the accuracy of the positioning signal error, thereby improving the reliability of evaluating the positioning results.

[0066] Figure 1 A schematic flow chart of a method 100 for determining a positioning signal error model provided in an embodiment of the present application is shown.

[0067] like Figure 1 As shown, the method 100 for determining a positioning signal error model provided in an embodiment of the present application may include S101 to S103.

[0068] S101: Acquire GNSS observation information of the receiver.

[0069] The GNSS observation information may be the GNSS observation information received by the receiver in a historical time period. Before training the regression model, it is necessary to obtain training samples from the receiver, wherein the training samples are the GNSS observation information.

[0070] Here, in order to ensure the accuracy of the trained model, there can be multiple GNSS observation information.

[0071] S102: Solve the GNSS observation information to obtain a positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process.

[0072] After obtaining the GNSS observation information, it is necessary to solve the GNSS observation information to obtain the positioning result corresponding to the GNSS observation information. In the process of solving the GNSS observation information, intermediate solution variables will be generated. The intermediate solution variables have a strong relationship with the positioning confidence error. Therefore, in order to ensure the accuracy of the positioning confidence error determined by the trained model, during the model training process, it is necessary to determine the confidence error eigenvalue corresponding to each intermediate solution variable based on the intermediate solution variables.

[0073] S103: Inputting the confidence error characteristic value and the confidence error true value corresponding to the GNSS observation information into a regression model for training to obtain a positioning confidence error model.

[0074] The regression model can show the significant relationship between the independent variable and the dependent variable, and can also show the influence strength of multiple independent variables on a dependent variable. In order to determine the significant relationship between the intermediate solution variable and the fixed position confidence error, as well as the influence strength of multiple intermediate solution variables on the fixed position confidence error, it is necessary to input the confidence error eigenvalue corresponding to the intermediate solution variable and the true value of the fixed position confidence error corresponding to the GNSS observation information into the regression model for model training, so as to obtain the fixed position confidence error model.

[0075] In some embodiments, the regression model may be a neural network regression model, a support vector machine, or a decision tree.

[0076] When the regression model is a neural network regression model, such as Figure 2 As shown, the neural network regression model includes an input layer, a hidden layer and an output layer. The number of hidden layers of the neural network regression model can be set to LN, and LN can be any value from 1 to 5, and the number of each hidden neuron can be set to N, and N can be any value from 10 to 50. The activation function of the hidden layer L1-Ln-1 can be a tanh function. The activation function from the hidden layer to the input layer can be a ReLU function, that is, the activation function corresponding to the hidden layer Ln is the ReLU function.

[0077] In order to save computing resources, the number of neurons in the input layer of the neural network regression model can be equal to the number of confidence error eigenvalues ​​corresponding to the intermediate solution. For example, if the number of confidence error eigenvalues ​​is n, that is, {feature 1, feature 2, ..., feature n-1, feature n}, the input layer can include n input nodes.

[0078] In the process of training the neural network regression model, the output layer can output the positioning confidence error of the positioning result. The loss function value between the positioning confidence error and the true value of the confidence error of the positioning result is calculated, and the parameters of each layer in the neural network regression model are adjusted based on the loss function value, thereby obtaining the positioning confidence error model.

[0079] In some embodiments, the positioning result may be any one of a floating point solution, a fixed solution, and a single point solution.

[0080] In order to ensure the accuracy of the positioning signal error of different types of positioning results, in the process of training the regression model, the regression model can be trained for different types of positioning results, so as to obtain different types of positioning signal error models. Among them, the positioning signal error model can be a floating point solution positioning signal error model, a fixed solution positioning signal error model, or a single point solution positioning signal error model.

[0081] Therefore, each type of positioning result corresponds to a positioning signal error model, so that the positioning signal error of the real-time positioning result can be identified based on the positioning signal error model corresponding to the real-time positioning result, thereby improving the accuracy of the positioning signal error.

[0082] When the positioning result is a floating point solution, the intermediate solution variables include at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning result of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance. The confidence error eigenvalue corresponding to the floating point solution positioning result is input into the regression model for training to obtain the floating point solution positioning confidence error model.

[0083] In some embodiments, in order to further ensure the accuracy of the positioning confidence error, when the positioning result is a floating point solution, in S102, an intermediate statistical value can also be obtained in the process of solving the GNSS observation information. The intermediate statistical value is the number of each intermediate solution variable and the number of each GNSS observation data in the GNSS observation information. After obtaining the intermediate statistical value, the intermediate statistical value is added to the confidence error characteristic value. The confidence error characteristic value can also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common view satellites.

[0084] Therefore, the confidence error characteristic value also includes the intermediate statistical value, so that the trained positioning confidence error model can accurately identify the positioning confidence error of the real-time positioning result.

[0085] When the positioning result is a fixed solution, the intermediate solution variables include at least one of the flag of Doppler observation value, Doppler unit weighted mean error, pseudorange unit weighted mean error, carrier unit weighted mean error, average carrier-to-noise ratio, unit weighted mean error of ambiguity search optimal solution, position difference calculated by ambiguity fixed by the first frequency point and ambiguity fixed by the second frequency point, positioning east direction covariance, and positioning north direction covariance. The confidence error eigenvalue corresponding to the fixed solution positioning result is input into the regression model for training to obtain the fixed solution positioning confidence error model.

[0086] In some embodiments, in order to further ensure the accuracy of the positioning confidence error, when the positioning result is a fixed solution, in S102, an intermediate statistical value can also be obtained in the process of solving the GNSS observation information. The intermediate statistical value is the number of each intermediate solution variable and the number of each GNSS observation data in the GNSS observation information. After obtaining the intermediate statistical value, the intermediate statistical value is added to the confidence error characteristic value. The confidence error characteristic value can also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common view satellites.

[0087] Therefore, the confidence error characteristic value also includes the intermediate statistical value, so that the trained positioning confidence error model can accurately identify the positioning confidence error of the real-time positioning result.

[0088] When the positioning result is a single point solution, the intermediate solution variables include at least one of the position posterior residual, the velocity posterior residual of the Kalman filter solution, the precision factor DOP value, the posterior residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio. The confidence error eigenvalue corresponding to the single point solution positioning result is input into the regression model for training to obtain the single point solution positioning confidence error model.

[0089] In some embodiments, in order to further ensure the accuracy of the positioning confidence error, when the positioning result is a single-point solution, in S102, intermediate statistical values ​​can also be obtained in the process of solving the GNSS observation information. The intermediate statistical values ​​are the number of each intermediate solution variable and the number of each GNSS observation data in the GNSS observation information. After obtaining the intermediate statistical values, the intermediate statistical values ​​are added to the confidence error characteristic value. The confidence error characteristic value can also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the proportion of the number of downgraded satellites to all satellites involved in positioning.

[0090] Therefore, the confidence error characteristic value also includes the intermediate statistical value, so that the trained positioning confidence error model can accurately identify the positioning confidence error of the real-time positioning result.

[0091] The method for determining the positioning confidence error model provided in the embodiment of the present application obtains the positioning result by solving the GNSS observation information. During the solving process, the intermediate solution variable can be obtained, thereby determining the confidence error characteristic value corresponding to the intermediate solution variable, and then inputting the confidence error characteristic value and the confidence error true value of the GNSS observation information into the regression model, and training the regression model to obtain the positioning confidence error model. Since the intermediate solution variable has a strong correlation with the positioning confidence error, and the positioning confidence error model is obtained by training based on the intermediate solution variable, the positioning confidence error of the real-time positioning result is determined by using the trained positioning confidence error model, which can improve the accuracy of the positioning confidence error of the real-time positioning result, thereby improving the reliability of the evaluation of the positioning result.

[0092] based on Figure 1 The corresponding embodiment provides a method for determining a positioning confidence error model. Accordingly, the present application provides a method for determining a positioning confidence error according to an embodiment.

[0093] Figure 3 3 is a flow chart of a method 300 for determining a positioning signal error provided in an embodiment of the present application.

[0094] like Figure 3 As shown, the method 300 for determining a positioning signal error provided in an embodiment of the present application may include S301 to S303.

[0095] S301: Acquire real-time GNSS observation information of the receiver.

[0096] Before determining the real-time positioning result, it is necessary to obtain real-time GNSS observation information from the receiver.

[0097] S302: Solve the GNSS observation information to obtain a real-time positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process.

[0098] After obtaining the real-time GNSS observation information, it is necessary to solve the real-time GNSS observation information to obtain the real-time positioning result corresponding to the real-time GNSS observation information. In the process of solving the real-time GNSS observation information, intermediate solution variables will be generated. The relationship between the intermediate solution variables and the positioning confidence error is strong. Therefore, in order to improve the accuracy of the positioning confidence error, it is necessary to determine the confidence error eigenvalue corresponding to the intermediate solution variable.

[0099] S303: Input the confidence error characteristic value into the positioning confidence error model to obtain the positioning confidence error of the real-time positioning result.

[0100] Enter the confidence error characteristic value into Figure 1The positioning signal error obtained in the corresponding embodiment is determined in the model, thereby determining the positioning signal error of the real-time positioning result.

[0101] In some embodiments, the position signal error model may be a floating point solution position signal error model, a fixed point solution position signal error model, or a single point solution position signal error model.

[0102] When the real-time positioning result is a floating point solution, the intermediate solution variables include at least one of the sign of Doppler observation value, Doppler unit weighted mean error, pseudorange unit weighted mean error, carrier unit weighted mean error, average carrier-to-noise ratio, unit weighted mean error of ambiguity search optimal solution, the difference between the heading angle calculated by the positioning result of the previous and next epochs and the heading angle calculated by Doppler, positioning east direction covariance, and positioning north direction covariance. The confidence error characteristic value of the intermediate solution variable is input into the floating point solution positioning confidence error model to determine the confidence error of the real-time positioning result.

[0103] When the real-time positioning result is a fixed solution, the intermediate solution variables include at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the position difference calculated by the ambiguity fixed by the first frequency point and the ambiguity fixed by the second frequency point, the positioning east direction covariance, and the positioning north direction covariance. The confidence error characteristic value of the intermediate solution variable is input into the fixed solution position confidence error model to determine the confidence error of the real-time positioning result.

[0104] When the real-time positioning result is a single-point solution, the intermediate solution variables include at least one of the position a posteriori residual, the velocity a posteriori residual of the Kalman filter solution, the precision factor DOP value, the a posteriori residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio. The confidence error characteristic value of the intermediate solution variable is input into the single-point solution position confidence error model to determine the confidence error of the real-time positioning result.

[0105] The method for determining the positioning confidence error provided in the embodiment of the present application obtains the real-time GNSS observation information in the receiver and solves the real-time GNSS observation information to determine the real-time positioning result, and can determine the intermediate solution variables in the solution process, and input the confidence error characteristic value of the intermediate solution variable into the pre-trained positioning confidence error model, so as to obtain the positioning confidence error corresponding to the real-time positioning result. Therefore, the positioning confidence error of the real-time positioning result is determined by the confidence error characteristic value of the intermediate solution variable, which can improve the accuracy of the positioning confidence error, thereby improving the accuracy of the evaluation of the real-time positioning result.

[0106] The embodiment of the present application also provides another method for determining a positioning signal error model.

[0107] Figure 4 4 is a flow chart of a method 400 for determining a positioning signal error model provided in an embodiment of the present application.

[0108] like Figure 4 As shown, the method 400 for determining a positioning signal error model provided in the embodiment of the present application may include S401 to S403.

[0109] S401: Obtain GNSS observation information of the receiver.

[0110] S402: Solve the GNSS observation information to obtain a positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process.

[0111] It should be noted that S401 is the same as S101, and S402 is the same as S102. For the sake of brevity, the detailed description of S401 and S402 can be found in the detailed description of S101 and S102. Only the differences are described in detail here.

[0112] S403: Input the confidence error characteristic value and the confidence error true value corresponding to the GNSS observation information into the regression model for training to obtain a positioning confidence error model, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model and a single point solution sub-model.

[0113] Here, the regression model may include multiple sub-models. In the process of training the regression model, different sub-models need to be trained according to the type of positioning result, so as to obtain a positioning signal error model. Among them, the type of positioning result can be a floating point solution, a fixed solution, or a single point solution. The trained positioning signal error model includes a floating point solution sub-model, a fixed solution sub-model, and a single point solution sub-model.

[0114] Specifically, when the positioning result is a floating point solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning result of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance. The confidence error characteristic value of the intermediate solution variable is input into the first sub-model in the regression model for training, so as to obtain the floating point solution sub-model.

[0115] When the positioning result is a fixed solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the position difference calculated by the ambiguity fixed by the first frequency point and the ambiguity fixed by the second frequency point, the positioning east direction covariance, and the positioning north direction covariance. The confidence error characteristic value of the intermediate solution variable is input into the second sub-model in the regression model for training, so as to obtain the fixed solution sub-model.

[0116] When the positioning result is a single point solution, the confidence error characteristic value includes at least one of the position a posteriori residual, the velocity a posteriori residual calculated by the Kalman filter, the precision factor DOP value, the a posteriori residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio. The confidence error characteristic value of the intermediate solution variable is input into the third sub-model in the regression model for training, thereby obtaining the single point solution sub-model.

[0117] In some embodiments, in order to further ensure the accuracy of the positioning confidence error, in S402, in S102, an intermediate statistical value can also be obtained in the process of solving the GNSS observation information. The intermediate statistical value is the number of each intermediate solution variable and the number of each GNSS observation data in the GNSS observation information. After obtaining the intermediate statistical value, the intermediate statistical value is added to the confidence error characteristic value.

[0118] Here, the confidence error characteristic value input to the floating-point solution submodel may also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of co-viewed satellites. The confidence error characteristic value input to the fixed solution submodel may also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of co-viewed satellites. The confidence error characteristic value input to the single-point solution submodel may also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of co-viewed satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the ratio of the number of downgraded satellites to the number of all satellites involved in positioning.

[0119] Therefore, the confidence error characteristic value also includes the intermediate statistical value, so that the trained positioning confidence error model can accurately identify the positioning confidence error of the real-time positioning result.

[0120] The method for determining the positioning confidence error model provided in the embodiment of the present application obtains the positioning result by solving the GNSS observation information. During the solving process, the intermediate solution variable can be obtained, thereby determining the confidence error characteristic value corresponding to the intermediate solution variable, and then inputting the confidence error characteristic value and the confidence error true value of the GNSS observation information into the regression model, and training the regression model to obtain the positioning confidence error model. Since the positioning confidence error model includes a floating-point solution sub-model, a fixed solution sub-model, and a single-point solution sub-model, the positioning confidence error of the real-time positioning result can be determined in a targeted manner, and the positioning confidence error model is obtained by training based on the intermediate solution variable. Therefore, using the trained positioning confidence error model to determine the positioning confidence error of the real-time positioning result can improve the accuracy of the positioning confidence error corresponding to the real-time GNSS observation information, thereby improving the reliability of evaluating the positioning result.

[0121] based on Figure 4 The corresponding embodiment provides a method for determining a positioning confidence error model. Accordingly, the present application provides a method for determining a positioning confidence error in one embodiment.

[0122] Figure 5 4 is a flow chart of another method 500 for determining a positioning signal error provided in an embodiment of the present application.

[0123] like Figure 5 As shown, the method 500 for determining a positioning signal error provided in an embodiment of the present application may include S501 to S503.

[0124] S501: Acquire real-time GNSS observation information of the receiver.

[0125] S502: Solve the GNSS observation information to obtain a real-time positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process.

[0126] It should be noted that S501 is the same as S301, and S502 is the same as S302. For the sake of brevity, the detailed description of S501 and S502 can be found in the detailed description of S301 and S302. Only the differences are described in detail here.

[0127] S503: Input the confidence error eigenvalue into the positioning confidence error model to obtain the positioning confidence error of the real-time positioning result, the positioning confidence error model includes a floating-point solution sub-model, a fixed solution sub-model, and a single-point solution sub-model; wherein, when the real-time positioning result is a floating-point solution, the confidence error eigenvalue is input into the floating-point solution sub-model to obtain the positioning confidence error; when the real-time positioning result is a fixed solution, the confidence error eigenvalue is input into the fixed solution sub-model to obtain the positioning confidence error; when the real-time positioning result is a single-point solution, the confidence error eigenvalue is input into the single-point solution sub-model to obtain the positioning confidence error.

[0128] Here, the positioning confidence error model includes multiple sub-models, namely, floating point solution sub-model, fixed solution sub-model, and single point solution sub-model. After the real-time positioning result is obtained by solution, the confidence error eigenvalue corresponding to the real-time positioning result needs to be input into the sub-model corresponding to the type of the real-time positioning result according to the type of the real-time positioning result.

[0129] Specifically, when the real-time positioning result is a floating-point solution, the confidence error eigenvalue is input into the floating-point solution sub-model to obtain the positioning confidence error; when the real-time positioning result is a fixed solution, the confidence error eigenvalue is input into the fixed solution sub-model to obtain the positioning confidence error; when the real-time positioning result is a single-point solution, the confidence error eigenvalue is input into the single-point solution sub-model to obtain the positioning confidence error.

[0130] The method for determining the positioning confidence error provided in the embodiment of the present application obtains the real-time GNSS observation information in the receiver and solves the real-time GNSS observation information to determine the real-time positioning result, and can determine the intermediate solution variables in the solution process, and input the confidence error characteristic value of the intermediate solution variable into the pre-trained positioning confidence error model, so as to obtain the positioning confidence error corresponding to the real-time positioning result. Since the positioning confidence error model includes a floating-point solution sub-model, a fixed solution sub-model, and a single-point solution sub-model, the positioning confidence error of the real-time positioning result can be determined in a targeted manner, and the positioning confidence error model is obtained based on the training of the intermediate solution variables. Therefore, using the trained positioning confidence error model to determine the positioning confidence error of the real-time positioning result can improve the accuracy of the positioning confidence error corresponding to the real-time GNSS observation information, thereby improving the reliability of evaluating the positioning result.

[0131] based on Figure 1 Corresponding to the method for determining the positioning signal error model provided in the embodiment, the present application provides a device for determining the positioning signal error model in one embodiment. The device for determining the positioning signal error model provided in the embodiment of the present application is described below.

[0132] Figure 6 A schematic diagram of the structure of a device 600 for determining a positioning signal error model provided in the present application.

[0133] like Figure 6 As shown, the device 600 for determining a positioning signal error model provided by the present application may include: an acquisition module 601 , a solution module 602 , and a training module 603 .

[0134] An acquisition module 601 is used to acquire GNSS observation information of a receiver;

[0135] A solution module 602 is used to solve the GNSS observation information to obtain a positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process;

[0136] The training module 603 is used to input the confidence error characteristic value and the confidence error true value corresponding to the GNSS observation information into the regression model for training to obtain a positioning confidence error model.

[0137] In some embodiments of the present application, when the positioning result is a floating point solution, the intermediate solution variables include at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning result of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance;

[0138] The positioning signal error model obtained through training is a floating-point positioning signal error model.

[0139] In some embodiments of the present application, the device 600 also includes an adding module for obtaining intermediate statistical values ​​through GNSS observation information and adding confidence error characteristic values, and the confidence error characteristic values ​​also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites.

[0140] In some embodiments of the present application, when the positioning result is a fixed solution, the intermediate solution variables include at least one of a sign of a Doppler observation value, a Doppler unit weighted mean error, a pseudorange unit weighted mean error, a carrier unit weighted mean error, an average carrier-to-noise ratio, a unit weighted mean error of an ambiguity search optimal solution, a position difference calculated by using the ambiguity fixed at the first frequency point and the ambiguity fixed at the second frequency point, a positioning east direction covariance, and a positioning north direction covariance;

[0141] The trained positioning signal error model is a fixed solution positioning signal error model.

[0142] In some embodiments of the present application, the device 600 also includes an adding module for obtaining intermediate statistical values ​​through GNSS observation information and adding confidence error characteristic values, and the confidence error characteristic values ​​also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites.

[0143] In some embodiments of the present application, when the positioning result is a single-point solution, the intermediate solution variables include at least one of a posteriori residual of position, a posteriori residual of velocity solved by Kalman filter, a precision factor DOP value, a posteriori residual of a least squares solution, a straight-line distance between a Kalman filter solution and a least squares solution, and an average carrier-to-noise ratio;

[0144] The positioning signal error model obtained through training is a single-point positioning signal error model.

[0145] In some embodiments of the present application, the device 600 also includes an adding module for obtaining intermediate statistical values ​​through GNSS observation information and adding confidence error characteristic values, and the confidence error characteristic values ​​also include at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common-view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the proportion of the number of downgraded satellites to all satellites participating in positioning.

[0146] Figure 6 Each module / unit in the device shown has the function of realizing Figure 1 The functions of each step in the process can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0147] The method for determining the positioning confidence error model provided in the embodiment of the present application obtains the positioning result by solving the GNSS observation information. During the solving process, the intermediate solution variable can be obtained, thereby determining the confidence error characteristic value corresponding to the intermediate solution variable, and then inputting the confidence error characteristic value and the confidence error true value of the GNSS observation information into the regression model, and training the regression model to obtain the positioning confidence error model. Since the intermediate solution variable has a strong correlation with the positioning confidence error, and the positioning confidence error model is obtained by training based on the intermediate solution variable, the positioning confidence error of the real-time positioning result is determined by using the trained positioning confidence error model, which can improve the accuracy of the positioning confidence error of the real-time positioning result, thereby improving the reliability of the evaluation of the positioning result.

[0148] based on Figure 3 Corresponding to the method for determining the positioning signal error provided in the embodiment, the present application provides a device for determining the positioning signal error in one embodiment. The device for determining the positioning signal error provided in the embodiment of the present application is described below.

[0149] Figure 7 A schematic diagram of the structure of a device 700 for determining a positioning signal error provided by the present application.

[0150] like Figure 7As shown, the device 700 for determining a positioning confidence error provided by the present application may include: an acquisition module 701 , a solution module 702 , and a confidence error determination module 703 .

[0151] An acquisition module 701 is used to acquire real-time GNSS observation information of a receiver;

[0152] A solution module 702 is used to solve the GNSS observation information to obtain a real-time positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process;

[0153] The confidence error determination module 703 is used to input the confidence error characteristic value into the positioning confidence error model to obtain the positioning confidence error of the real-time positioning result.

[0154] Figure 7 Each module / unit in the device shown has the function of realizing Figure 3 The functions of each step in the process can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0155] The method for determining the positioning confidence error provided in the embodiment of the present application obtains the real-time GNSS observation information in the receiver and solves the real-time GNSS observation information to determine the real-time positioning result, and can determine the intermediate solution variables in the solution process, and input the confidence error characteristic value of the intermediate solution variable into the pre-trained positioning confidence error model, so as to obtain the positioning confidence error corresponding to the real-time positioning result. Therefore, by determining the positioning confidence error of the real-time positioning result through the confidence error characteristic value of the intermediate solution variable, the accuracy of the positioning confidence error can be improved, thereby improving the accuracy of the evaluation of the real-time positioning result.

[0156] based on Figure 4 Corresponding to the method for determining the positioning signal error model provided in the embodiment, the present application provides a device for determining the positioning signal error model in one embodiment. The device for determining the positioning signal error provided in the embodiment of the present application is described below.

[0157] Figure 8 A schematic diagram of the structure of a device 800 for determining a positioning signal error model provided in the present application.

[0158] like Figure 8 As shown, the device 800 for determining a positioning signal error model provided by the present application may include: an acquisition module 801 , a solution module 802 , and a training module 803 .

[0159] An acquisition module 801 is used to acquire GNSS observation information of a receiver;

[0160] A solution module 802 is used to solve the GNSS observation information to obtain a positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process;

[0161] A training module 803 is used to input the confidence error characteristic value and the confidence error true value corresponding to the GNSS observation information into the regression model for training to obtain a positioning confidence error model, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model and a single point solution sub-model;

[0162] Wherein, when the positioning result is a floating point solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning results of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance, so as to obtain a floating point solution sub-model;

[0163] When the positioning result is a fixed solution, the confidence error characteristic value includes at least one of a sign of a Doppler observation value, a Doppler unit weighted mean error, a pseudorange unit weighted mean error, a carrier unit weighted mean error, an average carrier-to-noise ratio, a unit weighted mean error of an ambiguity search optimal solution, a position difference calculated by the ambiguity fixed by the first frequency point and the ambiguity fixed by the second frequency point, a positioning east direction covariance, and a positioning north direction covariance, so as to obtain a fixed solution sub-model;

[0164] When the positioning result is a single-point solution, the confidence error characteristic value includes at least one of the position posterior residual, the velocity posterior residual calculated by the Kalman filter, the precision factor DOP value, the posterior residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio to obtain a single-point solution sub-model.

[0165] In some embodiments of the present application, the apparatus 800 further includes an adding module, configured to obtain intermediate statistical values ​​through GNSS observation information and add the confidence error characteristic values;

[0166] The confidence error characteristic value of the floating-point solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites;

[0167] The confidence error characteristic value of the fixed solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites;

[0168] The confidence error characteristic value of the single-point solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common-view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the ratio of the number of downgraded satellites to all satellites involved in positioning.

[0169] Figure 8 Each module / unit in the device shown has the function of realizing Figure 4 The functions of each step in the process can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0170] The method for determining the positioning confidence error model provided in the embodiment of the present application obtains the positioning result by solving the GNSS observation information. During the solving process, the intermediate solution variable can be obtained, thereby determining the confidence error characteristic value corresponding to the intermediate solution variable, and then inputting the confidence error characteristic value and the confidence error true value of the GNSS observation information into the regression model, and training the regression model to obtain the positioning confidence error model. Since the positioning confidence error model includes a floating-point solution sub-model, a fixed solution sub-model, and a single-point solution sub-model, the positioning confidence error of the real-time positioning result can be determined in a targeted manner, and the positioning confidence error model is obtained by training based on the intermediate solution variable. Therefore, using the trained positioning confidence error model to determine the positioning confidence error of the real-time positioning result can improve the accuracy of the positioning confidence error corresponding to the real-time GNSS observation information, thereby improving the reliability of evaluating the positioning result.

[0171] based on Figure 5 Corresponding to the method for determining the positioning signal error provided in the embodiment, the present application provides a device for determining the positioning signal error in one embodiment. The device for determining the positioning signal error provided in the embodiment of the present application is described below.

[0172] Fig. 9 A schematic diagram of the structure of a device 900 for determining a positioning signal error provided in the present application.

[0173] like Fig. 9 As shown, the device 900 for determining a positioning confidence error provided by the present application may include: an acquisition module 901 , a solution module 902 , and a confidence error determination module 903 .

[0174] An acquisition module 901 is used to acquire real-time GNSS observation information of a receiver;

[0175] A solution module 902 is used to solve the GNSS observation information to obtain a real-time positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process;

[0176] A confidence error determination module 903, used for inputting the confidence error characteristic value into the positioning confidence error model as claimed in claim 9 or 10 to obtain the positioning confidence error of the real-time positioning result, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model, and a single point solution sub-model;

[0177] The confidence error determination module 903 is specifically used to input the confidence error characteristic value into the floating point solution sub-model to obtain the positioning confidence error when the real-time positioning result is a floating point solution;

[0178] The confidence error determination module 903 is specifically used to input the confidence error characteristic value into the fixed solution sub-model to obtain the positioning confidence error when the real-time positioning result is a fixed solution;

[0179] The confidence error determination module 903 is specifically used to input the confidence error characteristic value into the single point solution sub-model to obtain the positioning confidence error when the real-time positioning result is a single point solution.

[0180] Fig. 9 Each module / unit in the device shown has the function of realizing Figure 5 The functions of each step in the process can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0181] The method for determining the positioning confidence error provided in the embodiment of the present application obtains the real-time GNSS observation information in the receiver and solves the real-time GNSS observation information to determine the real-time positioning result, and can determine the intermediate solution variables in the solution process, and input the confidence error characteristic value of the intermediate solution variable into the pre-trained positioning confidence error model, so as to obtain the positioning confidence error corresponding to the real-time positioning result. Since the positioning confidence error model includes a floating-point solution sub-model, a fixed solution sub-model, and a single-point solution sub-model, the positioning confidence error of the real-time positioning result can be determined in a targeted manner, and the positioning confidence error model is obtained based on the training of the intermediate solution variables. Therefore, using the trained positioning confidence error model to determine the positioning confidence error of the real-time positioning result can improve the accuracy of the positioning confidence error corresponding to the real-time GNSS observation information, thereby improving the reliability of evaluating the positioning result.

[0182] Fig.10 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0183] like Fig.10 As shown, the electronic device may include a processor 1001 and a memory 1002 storing computer program instructions.

[0184] Specifically, the processor 1001 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0185] The memory 1002 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 1002 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In one example, the memory 1002 may include a removable or non-removable (or fixed) medium, or the memory 1002 is a non-volatile solid-state memory. The memory 1002 may be inside or outside the integrated gateway disaster recovery device.

[0186] In one example, the memory 1002 may be a read-only memory (ROM). In one example, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0187] The memory 1002 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods in the present application.

[0188] The processor 1001 reads and executes the computer program instructions stored in the memory 1002 to implement Figure 1 , Figure 3 , Figure 4 and Figure 5 The method / steps in the embodiments shown in Figure 1 , Figure 3 , Figure 4 and Figure 5 The corresponding technical effects achieved by executing the methods / steps in the example shown are not repeated here for the sake of brevity.

[0189] In one example, the electronic device may further include a communication interface 1003 and a bus 1010. Fig.10 As shown, the processor 1001, the memory 1002, and the communication interface 1003 are connected via a bus 1010 and communicate with each other.

[0190] The communication interface 1003 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0191] Bus 1010 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics bus, enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) bus, front side bus (Front Side Bus, FSB), Hyper Transport (Hyper Transport, HT) interconnection, industry standard architecture (Industry Standard Architecture, ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1010 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0192] The electronic device can execute the method in the embodiment of the present application based on the currently intercepted junk text messages and the text messages reported by the user, thereby realizing the combination of Figure 1 , Figures 3 to 9 The method and apparatus described.

[0193] In addition, in combination with the method for determining the location signal error model and the method for determining the location signal error in the above embodiments, the present application embodiment may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any method for determining the location signal error model and / or the method for determining the location signal error in the above embodiments is implemented.

[0194] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0195] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), appropriate firmware, plug-in, function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0196] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0197] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0198] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for determining a positioning signal error model, characterized in that: include: Obtaining GNSS observation information from the receiver; Solving the GNSS observation information to obtain a positioning result, and obtaining a confidence error characteristic value based on an intermediate solution variable in the solution process; Inputting the confidence error characteristic value and the confidence error true value corresponding to the positioning result into a regression model for training to obtain a positioning confidence error model; The method further comprises: The intermediate statistical value obtained through the GNSS observation information is added to the confidence error characteristic value; wherein, When the positioning result is a floating point solution, the positioning confidence error model obtained by training is a floating point solution positioning confidence error model; the confidence error characteristic value corresponding to the floating point solution positioning confidence error model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common view satellites; When the positioning result is a fixed solution, the positioning confidence error model obtained by training is a fixed solution positioning confidence error model; the confidence error characteristic value corresponding to the fixed solution positioning confidence error model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common view satellites; When the positioning result is a single-point solution, the positioning confidence error model obtained by training is a single-point solution positioning confidence error model; the confidence error characteristic value corresponding to the single-point solution positioning confidence error model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common-view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the proportion of the number of downgraded satellites to all satellites involved in positioning.

2. The method according to claim 1, characterized in that: When the positioning result is a floating point solution, the intermediate solution variables include at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle solved by the positioning results of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance.

3. The method according to claim 1, characterized in that When the positioning result is a fixed solution, the intermediate solution variables include at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the position difference calculated by the ambiguity fixed by the first frequency point and the ambiguity fixed by the second frequency point, the positioning east direction covariance, and the positioning north direction covariance.

4. The method according to claim 1, characterized in that When the positioning result is a single-point solution, the intermediate solution variables include at least one of the position posterior residual, the velocity posterior residual of the Kalman filter solution, the precision factor DOP value, the posterior residual of the least squares solution, the straight-line distance between the Kalman filter solution and the least squares solution, and the average carrier-to-noise ratio.

5. A method for determining a positioning signal error, characterized in that: include: Obtain real-time GNSS observation information from the receiver; Solving the GNSS observation information to obtain a real-time positioning result, and obtaining a confidence error characteristic value based on an intermediate solution variable in the solution process; The confidence error characteristic value is input into the positioning confidence error model as described in any one of claims 1 to 4 to obtain the positioning confidence error of the real-time positioning result.

6. A method for determining a positioning signal error model, characterized in that: include: Get the GNSS observation information of the receiver; Solving the GNSS observation information to obtain a positioning result, and obtaining a confidence error characteristic value based on an intermediate solution variable in the solution process; Inputting the confidence error characteristic value and the confidence error true value corresponding to the GNSS observation information into a regression model for training to obtain a positioning confidence error model, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model and a single point solution sub-model; When the positioning result is a floating point solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning results of the previous and next epochs and the heading angle calculated by Doppler, the positioning east direction covariance, and the positioning north direction covariance, so as to obtain the floating point solution sub-model; When the positioning result is a fixed solution, the confidence error characteristic value includes at least one of a sign of a Doppler observation value, a Doppler unit weighted mean error, a pseudorange unit weighted mean error, a carrier unit weighted mean error, an average carrier-to-noise ratio, a unit weighted mean error of an ambiguity search optimal solution, a position difference calculated by using the ambiguity fixed at the first frequency point and the ambiguity fixed at the second frequency point, a positioning east direction covariance, and a positioning north direction covariance, so as to obtain the fixed solution sub-model; When the positioning result is a single point solution, the confidence error characteristic value includes at least one of a posterior position residual, a posterior velocity residual calculated by Kalman filter, a precision factor DOP value, a posterior residual of a least squares solution, a straight-line distance between a Kalman filter solution and a least squares solution, and an average carrier-to-noise ratio, so as to obtain the single point solution sub-model; The method further comprises: An intermediate statistical value is obtained through the GNSS observation information and added to a confidence error characteristic value; The confidence error characteristic value of the floating-point solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites; The confidence error characteristic value of the fixed solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites; The confidence error characteristic value of the single-point solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common-view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the ratio of the number of downgraded satellites to the number of all satellites involved in positioning.

7. A method for determining a positioning signal error, characterized in that: include: Obtain real-time GNSS observation information from the receiver; Solving the GNSS observation information to obtain a real-time positioning result, and obtaining a confidence error characteristic value based on an intermediate solution variable in the solution process; Inputting the confidence error characteristic value into the positioning confidence error model as claimed in claim 6 to obtain the positioning confidence error of the real-time positioning result, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model, and a single point solution sub-model; Wherein, when the real-time positioning result is a floating-point solution, the confidence error characteristic value is input into the floating-point solution sub-model to obtain the positioning confidence error; When the real-time positioning result is a fixed solution, inputting the confidence error characteristic value into the fixed solution sub-model to obtain the positioning confidence error; When the real-time positioning result is a single-point solution, the confidence error characteristic value is input into the single-point solution sub-model to obtain the positioning confidence error.

8. A device for determining a positioning signal error model, characterized in that: include: An acquisition module is used to obtain the GNSS observation information of the receiver; A solution module is used to solve the GNSS observation information to obtain a positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process; A training module, used for inputting the confidence error characteristic value and the confidence error true value corresponding to the positioning result into a regression model for training to obtain a positioning confidence error model; An adding module is used to obtain intermediate statistical values ​​through the GNSS observation information and add the confidence error characteristic value; wherein, When the positioning result is a floating point solution, the positioning confidence error model obtained by training is a floating point solution positioning confidence error model; the confidence error characteristic value corresponding to the floating point solution positioning confidence error model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common view satellites; When the positioning result is a fixed solution, the positioning confidence error model obtained by training is a fixed solution positioning confidence error model; the confidence error characteristic value corresponding to the fixed solution positioning confidence error model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common view satellites; When the positioning result is a single-point solution, the positioning confidence error model obtained by training is a single-point solution positioning confidence error model; the confidence error characteristic value corresponding to the single-point solution positioning confidence error model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common-view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the proportion of the number of downgraded satellites to all satellites involved in positioning.

9. A device for determining a positioning signal error, characterized in that: include: An acquisition module is used to obtain real-time GNSS observation information of the receiver; A solution module is used to solve the GNSS observation information to obtain a real-time positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process; A confidence error determination module is used to input the confidence error characteristic value into the positioning confidence error model according to any one of claims 1 to 4 to obtain the positioning confidence error of the real-time positioning result.

10. A device for determining a positioning signal error model, characterized in that: include: An acquisition module is used to obtain the GNSS observation information of the receiver; A solution module is used to solve the GNSS observation information to obtain a positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process; A training module, used for inputting the confidence error characteristic value and the confidence error true value corresponding to the positioning result into a regression model for training to obtain a positioning confidence error model, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model and a single point solution sub-model; Wherein, when the positioning result is a floating point solution, the confidence error characteristic value includes at least one of the sign of the Doppler observation value, the Doppler unit weighted mean error, the pseudorange unit weighted mean error, the carrier unit weighted mean error, the average carrier-to-noise ratio, the unit weighted mean error of the ambiguity search optimal solution, the difference between the heading angle calculated by the positioning results of the previous and next epochs and the heading angle calculated using Doppler, the positioning east direction covariance, and the positioning north direction covariance, so as to obtain the floating point solution sub-model; When the positioning result is a fixed solution, the confidence error characteristic value includes at least one of a sign of a Doppler observation value, a Doppler unit weighted mean error, a pseudorange unit weighted mean error, a carrier unit weighted mean error, an average carrier-to-noise ratio, a unit weighted mean error of an ambiguity search optimal solution, a position difference calculated by using the ambiguity fixed at the first frequency point and the ambiguity fixed at the second frequency point, a positioning east direction covariance, and a positioning north direction covariance, so as to obtain the fixed solution sub-model; When the positioning result is a single point solution, the confidence error characteristic value includes at least one of a posterior position residual, a posterior velocity residual calculated by Kalman filter, a precision factor DOP value, a posterior residual of a least squares solution, a straight-line distance between a Kalman filter solution and a least squares solution, and an average carrier-to-noise ratio, so as to obtain the single point solution sub-model; An adding module, used for obtaining intermediate statistical values ​​through the GNSS observation information and adding the confidence error characteristic value; The confidence error characteristic value of the floating-point solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites; The confidence error characteristic value of the fixed solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, and the number of common-view satellites; The confidence error characteristic value of the single-point solution sub-model also includes at least one of the number of Dopplers used, the number of pseudoranges involved in positioning, the number of common-view satellites, the number of satellites with a carrier-to-noise ratio greater than a first threshold, and the ratio of the number of downgraded satellites to the number of all satellites involved in positioning.

11. A device for determining a positioning signal error, characterized in that: include: An acquisition module is used to obtain real-time GNSS observation information of the receiver; A solution module is used to solve the GNSS observation information to obtain a real-time positioning result, and obtain a confidence error characteristic value based on an intermediate solution variable in the solution process; A confidence error determination module, used for inputting the confidence error characteristic value into the positioning confidence error model as claimed in claim 6 to obtain the positioning confidence error of the real-time positioning result, wherein the positioning confidence error model includes a floating point solution sub-model, a fixed solution sub-model, and a single point solution sub-model; Wherein, the confidence error determination module is specifically used to input the confidence error characteristic value into the floating-point solution sub-model to obtain the positioning confidence error when the real-time positioning result is a floating-point solution; The confidence error determination module is specifically used to input the confidence error characteristic value into the fixed solution sub-model to obtain the positioning confidence error when the real-time positioning result is a fixed solution; The confidence error determination module is specifically used to input the confidence error characteristic value into the single point solution sub-model to obtain the positioning confidence error when the real-time positioning result is a single point solution.

12. An electronic device, characterized in that: The device includes: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for determining the positioning signal error model as described in any one of claims 1-4 and 6, or the method for determining the positioning signal error as described in claim 5 or 7.

13. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method for determining a positioning signal error model as described in any one of claims 1-4 and 6, or implement the method for determining a positioning signal error as described in claim 5 or 7.

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