Positioning quality evaluation method, apparatus, device, storage medium, and program product

Abstract

This application relates to a positioning quality assessment method, apparatus, computer equipment, storage medium, and computer program product. The method relates to navigation and positioning technology and can be applied to electronic maps, vehicle-to-everything (V2X) systems, autonomous driving, and intelligent transportation scenarios. The method includes: acquiring satellite observation data at the current epoch of the terminal, the satellite observation data including observation pseudorange; acquiring the terminal positioning result calculated based on the satellite observation data; determining the satellite observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, and calculating a first type of statistical parameter regarding the observation pseudorange residual; determining the single-difference observation pseudorange residual between satellites based on the satellite observation pseudorange and the terminal positioning result, and calculating a second type of statistical parameter regarding the single-difference observation pseudorange residual; and assessing the positioning quality of the terminal positioning result at the current epoch based on the first type of statistical parameter and the second type of statistical parameter, which can improve the accuracy of the positioning quality assessment and thus improve the accuracy of the positioning result.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a positioning quality assessment method, apparatus, computer equipment, storage medium, and computer program product. Background Technology

[0002] A Global Navigation Satellite System (GNSS) is a space-based radio navigation and positioning system that provides users with all-weather 3D coordinates, velocity, and time information at any location on the Earth's surface or in near-Earth space. Common satellite navigation systems include GPS, BDS, GLONASS, and GALILEO. Satellite navigation systems are widely used in communications, consumer entertainment, surveying and mapping, time synchronization, vehicle management, and automotive navigation and information services.

[0003] GNSS satellite positioning uses spatially distributed satellites and satellite observation data received by the terminal to estimate the terminal's positioning result, thereby improving the terminal's real-time positioning service. Under normal circumstances, the deviation between the terminal's positioning result and its actual location is within an acceptable range. However, it is often affected by various factors, resulting in a large deviation from the terminal's actual positioning result, leading to inaccurate positioning results. How to accurately and reasonably evaluate the terminal's positioning quality to improve the accuracy of the positioning result is an urgent problem to be solved. Summary of the Invention

[0004] Therefore, it is necessary to provide a positioning quality assessment method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of terminal positioning result evaluation in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for assessing positioning quality. The method includes:

[0006] Acquire satellite observation data of the terminal at the current epoch, wherein the satellite observation data includes the observation pseudorange between the terminal and the satellite;

[0007] Obtain the terminal positioning result calculated based on the satellite observation data;

[0008] Based on the satellite's observed pseudorange and the terminal's positioning results, the satellite's observed pseudorange residual is determined, and a first type of statistical parameter is calculated regarding the observed pseudorange residual.

[0009] Based on the satellite observation pseudorange and the terminal positioning results, determine the single-difference observation pseudorange residual between satellites, and calculate the second type of statistical parameters for the single-difference observation pseudorange residual;

[0010] The positioning quality of the terminal positioning result in the current epoch is evaluated based on the first type of statistical parameters and the second type of statistical parameters.

[0011] Secondly, this application also provides a positioning quality assessment device. The device includes:

[0012] The acquisition module is used to acquire satellite observation data of the terminal at the current epoch, the satellite observation data including the observation pseudorange between the terminal and the satellite; and to acquire the terminal positioning result calculated based on the satellite observation data.

[0013] The first statistical module is used to determine the satellite observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, and to calculate the first type of statistical parameters about the observation pseudorange residual.

[0014] The second statistical module is used to determine the single-difference observation pseudorange residual between satellites based on the satellite observation pseudorange and the terminal positioning result, and to calculate the second type of statistical parameters about the single-difference observation pseudorange residual.

[0015] The quality assessment module is used to assess the positioning quality of the terminal positioning result in the current epoch based on the first type of statistics and the second type of statistical parameters.

[0016] In one embodiment, the apparatus further includes: a pseudorange observation equation construction module, configured to calculate the transmission time of the satellite signal based on the observed pseudorange between the current epoch terminal and the satellite and the reception time of the satellite signal received by the terminal; query the real-time navigation ephemeris of the satellite based on the transmission time to obtain the satellite position and satellite clock bias corresponding to the current epoch satellite; construct the pseudorange observation equation between the terminal and the satellite based on the observed pseudorange, satellite position, satellite clock bias of each satellite, as well as the estimated terminal position and estimated terminal clock bias; and solve the pseudorange observation equation based on the satellite observation data to obtain the terminal positioning result.

[0017] In one embodiment, the first statistical module includes a positioning solution unit, used to calculate the estimated distance between the terminal and the satellite during multiple iterations of least-squares solution of the pseudorange observation equation based on the satellite observation data, according to the satellite position corresponding to the current epoch satellite, the satellite clock error, the terminal position estimate obtained in the previous iteration, and the terminal clock error estimate; calculate the residual between the observed pseudorange of each satellite and the estimated distance, perform outlier elimination on the residual based on quartiles, select the target observed pseudorange from the observed pseudoranges of each satellite, and perform least-squares solution of the pseudorange observation equation formed by the target observed pseudorange in the current iteration.

[0018] In one embodiment, the satellite observation data further includes the signal-to-noise ratio of the observed pseudorange. The first statistical module includes a positioning solution unit, used to obtain the estimated parameters of the previous iteration during the current iteration of the least squares solution. The estimated parameters include the estimated position and the estimated clock error. It also obtains the target satellites selected after the previous iteration; calculates the observation pseudorange variance of the target satellites based on the signal-to-noise ratio of the observed pseudorange of the target satellites and the satellite elevation angle determined based on the satellite position corresponding to the target satellite and the terminal estimated position of the previous iteration; and calculates the variance of the observed pseudorange of the target satellites based on each target satellite. The pseudorange variance of the observations is used to construct an observation pseudorange variance matrix; based on the satellite observation data of the target satellite, the pseudorange observation equation for the current iteration is determined; the differential matrix of the pseudorange observation equation for the current iteration with respect to the parameters to be estimated, including the terminal position and the terminal clock error to be estimated, is obtained; based on the differential matrix, the pseudorange variance matrix, and the pseudorange observation residual of the target satellite, the correction amount of the estimated parameters for the current iteration is determined; the estimated parameters of the previous iteration are corrected based on the correction amount of the estimated parameters for the current iteration to obtain the estimated parameters for the current iteration.

[0019] In one embodiment, the positioning calculation unit is further configured to: continue the iteration process when the estimated parameter correction amount of the current iteration is greater than a first preset threshold; when the estimated parameter correction amount of the current iteration is less than the first preset threshold, determine the post-observation pseudorange residual sequence and the post-observation pseudorange variance matrix of the current iteration based on the estimated parameters of the current iteration, and calculate the chi-square test statistic based on the post-observation pseudorange variance matrix and the post-observation pseudorange residual sequence; stop the iteration when the chi-square test statistic is less than a second preset threshold, and obtain the positioning result of the terminal based on the estimated parameters of the current iteration; when the chi-square test statistic is greater than the second preset threshold, perform a normality test on the post-observation pseudorange residual sequence based on the post-observation pseudorange residual covariance matrix of the current iteration, remove the observed pseudoranges that fail the normality test, and continue the iteration process using the observed pseudoranges of the selected target satellite.

[0020] In one embodiment, the first statistical module includes a residual parameter statistical unit, used to calculate the positioning distance between the terminal and the satellite based on the satellite position and satellite clock error corresponding to the current epoch satellite, as well as the calculated terminal position and terminal clock error; calculate the residual between the observation pseudorange of each satellite and the corresponding positioning distance to obtain the observation pseudorange residual sequence; and calculate the root mean square and absolute median of the observation pseudorange residual sequence.

[0021] In one embodiment, the residual parameter statistics unit is further configured to: calculate the observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange and the elevation angle of the satellite determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position; construct an observation pseudorange variance matrix based on the observation pseudorange variance of each satellite; calculate the unit weighted mean square error of the observation pseudorange residual sequence based on the observation pseudorange residual sequence and the observation pseudorange variance matrix; and output the observation pseudorange residual sequence, as well as the unit weighted mean square error, root mean square, and absolute median of the observation pseudorange residuals.

[0022] In one embodiment, the second statistical module includes a single-difference residual construction unit, used to determine a reference satellite from the observed satellites of the terminal at the current epoch; for each non-reference satellite among the observed satellites, calculate the single observation difference between the observed pseudorange of the non-reference satellite and the observed pseudorange of the reference satellite, calculate the single positioning difference between the positioning distance corresponding to the non-reference satellite and the positioning distance corresponding to the reference satellite; and determine the single-difference observed pseudorange residual sequence based on the residual between the single observation difference corresponding to each non-reference satellite and the single positioning difference.

[0023] In one embodiment, the second statistical module includes a single-difference residual parameter statistical unit, used to calculate the root mean square and absolute median of the single-difference observation pseudorange residual sequence formed by the single-difference observation pseudorange residuals of each non-reference satellite; calculate the posterior observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange, the satellite elevation angle determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position, and the unit weighted mean error of the observation pseudorange residual in the first type of statistical parameters; construct the posterior observation pseudorange variance matrix based on the posterior observation pseudorange variance of each satellite; calculate the unit weighted mean error of the single-difference observation pseudorange residual sequence based on the posterior observation pseudorange variance matrix and the single-difference observation pseudorange residual sequence; and output the unit weighted mean error, root mean square, and absolute median of the single-difference observation pseudorange residual.

[0024] In one embodiment, the first type of statistical parameters includes the unit weighted mean square error of the observed pseudorange residual; the second type of statistical parameters includes the unit weighted mean square error of the single-difference observed pseudorange residual; the device further includes a terminal scene determination module, used to determine the scene in which the terminal is located based on the unit weighted mean square error of the observed pseudorange residual and the unit weighted mean square error of the single-difference observed pseudorange residual; the quality assessment module is further used to assess the positioning quality of the terminal positioning result in the current epoch based on the scene in which the terminal is located.

[0025] In one embodiment, the terminal scene determination module is further configured to calculate the posterior parameter covariance matrix of the current epoch and the posterior parameter covariance matrix of the previous epoch; calculate the relative change in posterior variance based on the posterior parameter covariance matrices of adjacent epochs; and determine the scene in which the terminal is located based on the relative change in posterior variance, the unit weighted mean square error of the observed pseudorange residual, and the unit weighted mean square error of the single-difference observed pseudorange residual.

[0026] In one embodiment, the first type of statistical parameters includes the unit weighted mean square error of the observation pseudorange residual; the terminal scene decision module is further configured to calculate the observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange at the current epoch and the satellite elevation angle determined based on the satellite position corresponding to the satellite at the current epoch and the calculated terminal position; construct an observation pseudorange variance matrix based on the observation pseudorange variance of each satellite; obtain the differential matrix of the pseudorange observation equation for the parameters to be estimated, the parameters to be estimated including the terminal position to be estimated and the terminal clock error to be estimated; and calculate the posterior parameter covariance matrix of the current epoch based on the observation pseudorange variance matrix, the differential matrix, and the unit weighted mean square error of the observation pseudorange residual at the current epoch.

[0027] In one embodiment, the first type of statistical parameters includes the unit weighted mean square error, root mean square, and absolute median of the observed pseudorange residuals; the second type of statistical parameters includes the unit weighted mean square error, root mean square, and absolute median of the single-difference observed pseudorange residuals; the quality assessment module is further configured to: statistically analyze the relative changes in the unit weighted mean square error, root mean square, and absolute median of the observed pseudorange residuals between the current epoch and the previous epoch; statistically analyze the relative changes in the unit weighted mean square error, root mean square, and absolute median of the single-difference observed pseudorange residuals between the current epoch and the previous epoch; and evaluate the positioning quality of the terminal positioning result in the current epoch based on the statistically analyzed relative changes between the first type of statistical parameters and the relative changes between the second type of statistical parameters corresponding to the current epoch and the previous epoch.

[0028] In one embodiment, the quality assessment module is further configured to determine the maximum value among the relative changes in the post-hoc variance, the relative changes in the unit weighted mean square error of the observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median, the relative changes in the unit weighted mean square error of the single-difference observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median; compare the determined maximum value with the threshold corresponding to the smoothness of each level of positioning trajectory to determine the smoothness of the positioning trajectory corresponding to the current epoch; and evaluate the positioning quality of the terminal positioning result in the current epoch based on the smoothness of the positioning trajectory.

[0029] In one embodiment, the satellite observation data further includes the signal-to-noise ratio of the observed pseudorange, and the device further includes: a signal-to-noise ratio information statistics module, used to obtain the signal-to-noise ratio of the observed pseudorange corresponding to each satellite whose satellite transmission signal is received by the terminal from the satellite observation data; and output statistical information about the signal-to-noise ratio based on the maximum value, minimum value, standard deviation and absolute median difference of the signal-to-noise ratio of the observed pseudorange of each satellite.

[0030] The quality assessment module is also used to assess the positioning quality of the terminal positioning result in the current epoch based on statistical information about the signal-to-noise ratio.

[0031] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0032] Acquire satellite observation data of the terminal at the current epoch, wherein the satellite observation data includes the observation pseudorange between the terminal and the satellite;

[0033] Obtain the terminal positioning result calculated based on the satellite observation data;

[0034] Based on the satellite's observed pseudorange and the terminal's positioning results, the satellite's observed pseudorange residual is determined, and a first type of statistical parameter is calculated regarding the observed pseudorange residual.

[0035] Based on the satellite observation pseudorange and the terminal positioning results, determine the single-difference observation pseudorange residual between satellites, and calculate the second type of statistical parameters for the single-difference observation pseudorange residual;

[0036] The positioning quality of the terminal positioning result in the current epoch is evaluated based on the first type of statistical parameters and the second type of statistical parameters.

[0037] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0038] Acquire satellite observation data of the terminal at the current epoch, wherein the satellite observation data includes the observation pseudorange between the terminal and the satellite;

[0039] Obtain the terminal positioning result calculated based on the satellite observation data;

[0040] Based on the satellite's observed pseudorange and the terminal's positioning results, the satellite's observed pseudorange residual is determined, and a first type of statistical parameter is calculated regarding the observed pseudorange residual.

[0041] Based on the satellite observation pseudorange and the terminal positioning results, determine the single-difference observation pseudorange residual between satellites, and calculate the second type of statistical parameters for the single-difference observation pseudorange residual;

[0042] The positioning quality of the terminal positioning result in the current epoch is evaluated based on the first type of statistical parameters and the second type of statistical parameters.

[0043] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0044] Acquire satellite observation data of the terminal at the current epoch, wherein the satellite observation data includes the observation pseudorange between the terminal and the satellite;

[0045] Obtain the terminal positioning result calculated based on the satellite observation data;

[0046] Based on the satellite's observed pseudorange and the terminal's positioning results, the satellite's observed pseudorange residual is determined, and a first type of statistical parameter is calculated regarding the observed pseudorange residual.

[0047] Based on the satellite observation pseudorange and the terminal positioning results, determine the single-difference observation pseudorange residual between satellites, and calculate the second type of statistical parameters for the single-difference observation pseudorange residual;

[0048] The positioning quality of the terminal positioning result in the current epoch is evaluated based on the first type of statistical parameters and the second type of statistical parameters.

[0049] The aforementioned positioning quality assessment method, apparatus, computer equipment, storage medium, and computer program product acquire satellite observation data at the current epoch of the terminal, calculate the terminal positioning result based on the observation pseudorange in the satellite observation data, and then determine the observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, calculating a first type of statistical parameter regarding the observation pseudorange residual. Furthermore, by using the satellite observation pseudorange and the aforementioned calculated terminal positioning result, the single-difference observation pseudorange residual is determined, and a second type of statistical parameter regarding the single-difference observation pseudorange residual can be calculated. Therefore, combining the first type of statistical parameter and the second type of statistical parameter as a positioning quality assessment index, the positioning quality of the terminal positioning result at the current epoch can be accurately assessed, thereby improving the accuracy of the terminal positioning result. Attached Figure Description

[0050] Figure 1 This is a diagram illustrating the application environment of a location quality assessment method in one embodiment.

[0051] Figure 2 This is a schematic diagram of the overall framework of the location quality assessment method in a specific embodiment;

[0052] Figure 3 This is a flowchart illustrating a location quality assessment method in one embodiment;

[0053] Figure 4 This is a flowchart illustrating the process of constructing the pseudorange observation equation between the terminal and the satellite in one embodiment;

[0054] Figure 5 This is a schematic diagram of the localization calculation process in one embodiment;

[0055] Figure 6 This is a schematic diagram of the iterative process for least squares localization solution in one embodiment;

[0056] Figure 7 This is a flowchart illustrating the calculation of the first type of statistical parameters of the observed pseudorange residuals in one embodiment;

[0057] Figure 8 This is a flowchart illustrating the output of the first type of statistical information in one embodiment;

[0058] Figure 9 This is a flowchart illustrating the process of determining the pseudorange residual of a single-difference observation in one embodiment.

[0059] Figure 10 This is a flowchart illustrating the output of the second type of statistical information in one embodiment;

[0060] Figure 11 This is a schematic diagram illustrating the determination of the smoothness of the current positioning trajectory of a terminal in one embodiment;

[0061] Figure 12 This is a schematic diagram illustrating the scenario in which the terminal is located, as shown in one embodiment.

[0062] Figure 13 This is a structural block diagram of a positioning quality assessment device in one embodiment;

[0063] Figure 14 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] First, the concepts involved in the embodiments of this application will be explained:

[0066] Satellite positioning equipment: Electronic devices used to process satellite signals and measure the geometric distance (observed pseudorange) between the device and the satellite, as well as the Doppler effect of the satellite signal (Doppler observations). Satellite positioning equipment typically includes modules such as an antenna, a satellite signal receiving loop, and baseband signal processing. A terminal integrating satellite positioning equipment can calculate the terminal's current position coordinates based on pseudorange and Doppler observations. Satellite positioning equipment is widely used in map navigation, surveying, location services, and deep space exploration, such as smartphone map navigation, high-precision geodesy, and civil aviation.

[0067] Satellite observation data from satellite positioning equipment includes the observed pseudorange, pseudorange rate, and accumulated delta range (ADR) between the terminal and the satellite. The observed pseudorange measures the geometric distance from the satellite to the positioning equipment; the observed pseudorange rate measures the Doppler effect caused by the relative motion between the positioning equipment and the satellite; and the ADR measures the change in the geometric distance between the satellite and the positioning equipment.

[0068] A Global Navigation Satellite System (GNSS) is a space-based radio navigation and positioning system that provides users with all-weather 3D coordinates, velocity, and time information at any location on the Earth's surface or in near-Earth space. Common satellite navigation systems include GPS (Global Positioning System), BDS (BeiDou Navigation Satellite System), GLONASS (Global Navigation Satellite System), and GALILEO (Galileo Navigation System). Satellite navigation systems are widely used in communications, consumer entertainment, surveying and mapping, time synchronization, vehicle management, and automotive navigation and information services.

[0069] The basic principle of GNSS satellite positioning is to calculate the positioning result of the terminal by using the spatially distributed satellites and the distance intersection between the satellites and the terminal. The terminal receives signals from four or more GNSS satellites at the same time. The terminal uses satellite positioning equipment to determine the geometric distance between the observed satellites and the terminal. Based on this, the spatial position of the terminal and the receiver clock error are calculated using the distance intersection method.

[0070] Least Squares Method: The least squares method (also known as the least squares method) is a mathematical optimization technique. It finds the best function match for data by minimizing the sum of squares of the errors. The least squares method can be used to easily obtain unknown data while minimizing the sum of squares of the errors between the obtained data and the actual data.

[0071] The positioning quality assessment method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another server.

[0072] In a specific application scenario, terminal 102 can be the aforementioned satellite positioning device. Terminal 102 integrates a Global Navigation Satellite System (GNSS) positioning chip, which can process satellite signals and accurately locate the user of terminal 102, enabling location services. Specifically, at each epoch, terminal 102, based on the integrated GNSS positioning chip, acquires satellite observation data of various satellites in space. This satellite observation data includes observation pseudorange, signal-to-noise ratio (SNR), etc. Furthermore, terminal 102 obtains real-time satellite navigation ephemeris from a server. Real-time satellite navigation ephemeris is an expression describing the position and velocity of a spacecraft, which can be used to query satellite positions and clock errors. Based on this satellite observation data and the satellite positions and clock errors obtained from the real-time navigation ephemeris, the positioning result of terminal 102 is determined. An epoch, specifically an observation epoch, refers to the observation time corresponding to the satellite observation data. Satellite observation data obtained at different observation times are different. To compare or process satellite observation data from different times, such a time is called an observation epoch.

[0073] Then, the terminal 102 can further evaluate the positioning quality of the positioning result. When the quality evaluation result indicates that the positioning quality of the positioning result is good, the positioning result is output. When the quality evaluation result indicates that the positioning quality of the positioning result is poor, the positioning result is filtered.

[0074] In one embodiment, terminal 102 acquires satellite observation data for the current epoch, including the observation pseudorange between the terminal and the satellite; acquires the terminal positioning result calculated based on the satellite observation data; determines the satellite observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, and calculates a first type of statistical parameter regarding the observation pseudorange residual; determines the single-difference observation pseudorange residual between the satellites based on the satellite observation pseudorange and the terminal positioning result, and calculates a second type of statistical parameter regarding the single-difference observation pseudorange residual; and evaluates the positioning quality of the terminal positioning result for the current epoch based on the first type of statistical parameter and the second type of statistical parameter.

[0075] Optionally, a target client supporting positioning functionality, such as an electronic map, is installed and runs on terminal 102. The positioning quality assessment method provided in this embodiment can be executed by the target client. Based on the positioning quality assessment result of the current epoch, the target client determines whether to filter the positioning results of the current epoch, thereby filtering out positioning results with low positioning quality and poor positioning accuracy, and outputting and displaying positioning results with higher positioning accuracy. Optionally, the positioning quality assessment method provided in this embodiment can also be executed by server 104 providing services to the target client. Server 104 can obtain satellite observation data of the current epoch of the terminal from terminal 102, calculate the positioning result of the terminal based on the satellite observation data, perform a positioning quality assessment, and feed back the positioning quality assessment result to terminal 102 to instruct terminal 102 to filter positioning results with low positioning quality and poor positioning accuracy.

[0076] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, smart voice interaction devices, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart vehicle terminals. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. This application embodiment can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0077] like Figure 2 The diagram shown is a schematic representation of the overall framework of the positioning quality assessment method provided in this application embodiment. (Refer to...) Figure 2 The diagram divides the positioning quality assessment process into the following modules: satellite signal-to-noise ratio statistics module 202, single-epoch least squares solution module 204, single-difference observation residual statistics module 206, calculation of relative change in variance of the parameter to be estimated after verification module 208, positioning trajectory smoothness discrimination module 210, terminal scene discrimination module 212, and terminal positioning quality assessment module 214.

[0078] The satellite signal-to-noise ratio (SNR) statistics module is used to calculate the maximum, minimum, standard deviation, and absolute median of the satellite SNR based on satellite observation data. These statistical parameters about the SNR can be used alone to evaluate positioning quality, or they can be combined with other statistical parameters to evaluate positioning quality.

[0079] The single-epoch least squares solution module is used to perform single-epoch least squares solution by combining pseudorange observation equations and satellite observation data to obtain satellite positioning results. Based on the positioning results, the pseudorange observation residuals of the satellite are obtained, and the first type of statistical information about the pseudorange observation residuals is output, including root mean square, unit weighted mean error, and absolute median.

[0080] The single-difference observation residual statistics module is used to calculate the posterior error model based on the prior error model and the unit weighted mean error of the pseudorange observation residuals. It is also used to determine the single-difference observation pseudorange residuals based on the positioning results and to output the second type of statistical information about the single-difference observation pseudorange residuals using the posterior error model, including the root mean square, unit weighted mean error, and absolute median.

[0081] The module for calculating the relative change in variance of the estimated parameter after verification is used to calculate the relative change in variance of the estimated parameter after verification in adjacent epochs.

[0082] The positioning trajectory smoothness discrimination module is used to determine the smoothness of the positioning trajectory based on one or more of the following parameters: satellite signal-to-noise ratio statistical parameters, relative change of posterior variance of position parameters in adjacent epochs, relative change of first-type statistical parameters of least squares solution in adjacent epochs, and relative change of second-type statistical information of pseudorange residual of single-epoch single-difference observation.

[0083] The terminal scene discrimination module is used to determine the scene in which the terminal is located based on one or more of the following parameters: satellite signal-to-noise ratio statistical parameters, unit weighted mean square error of single epoch least squares solution, unit weighted mean square error of single difference observation pseudorange residual, relative change of variance of adjacent epoch position parameters, etc.

[0084] The terminal positioning quality assessment module is used to perform positioning quality assessment based on one or more of the parameters output by the above modules, or it can combine one or more of the above parameters, positioning trajectory smoothness, and terminal scene to perform positioning quality assessment, and obtain the quality assessment result of the terminal positioning result output by the single epoch least squares solution module, filtering out positioning results with poor positioning indicators or obvious deviations.

[0085] In one embodiment, such as Figure 3 As shown, a positioning quality assessment method is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps:

[0086] Step 302: Obtain satellite observation data for the current epoch of the terminal, including the observation pseudorange between the terminal and the satellite; obtain the terminal positioning result calculated based on the satellite observation data.

[0087] In one embodiment, the terminal can acquire satellite observation data at the current epoch. The satellite observation data includes the pseudorange between the terminal and the satellite. Based on the pseudorange, a pseudorange observation equation between the terminal and the satellite is constructed. The terminal can solve the pseudorange observation equation based on the satellite observation data at the current epoch to obtain the terminal positioning result.

[0088] Here, epoch refers to the observation epoch, specifically the observation time corresponding to the satellite observation data. Satellite observation data obtained at different observation times are different. In order to compare or process satellite observation data at different times, such a time is called the observation epoch. For example, the (j+1)th epoch is the current epoch, the jth epoch is the previous epoch, and the jth epoch and the (j+1)th epoch are two adjacent epochs.

[0089] The satellite observation data at the current epoch of the terminal consists of the individual observation data received by the terminal at that epoch. This includes information such as the pseudorange of each satellite and the signal-to-noise ratio of the received satellite signals. The pseudorange is the geometric distance between the terminal and each satellite observed by the terminal; typically, there are at least four satellites in total. It is called pseudorange because this geometric distance includes errors caused by clock errors and atmospheric refraction delays, rather than being the "true distance." Clock errors include satellite clock errors and terminal clock errors. Clock errors refer to the difference between the clock reading and the actual system time; satellite clock errors refer to the difference between the satellite clock and the actual system time; and terminal clock errors refer to the difference between the terminal clock and the actual system time.

[0090] like Figure 4 As shown, in one embodiment, constructing the pseudorange observation equation between the terminal and the satellite based on the observed pseudorange includes:

[0091] Step 402: Calculate the satellite signal transmission time based on the observation pseudorange between the current epoch terminal and the satellite, and the reception time of the satellite signal received by the terminal.

[0092] Suppose that the terminal receives satellite signals from n satellites in the current epoch, and the terminal can obtain the observation pseudorange of n satellites. The observation pseudorange of satellite i is denoted as . The reception time of satellite signals can be denoted as: Based on the observed pseudorange, reception time, and speed of light, the transmission time of the satellite signals of each satellite observed by the terminal at the current epoch is determined.

[0093] Step 404: Query the satellite's real-time navigation ephemeris based on the launch time to obtain the satellite position and satellite clock difference corresponding to the current epoch.

[0094] Specifically, the terminal can query the real-time navigation ephemeris of satellites based on the launch time to obtain the satellite positions and clock biases of each satellite observed by the terminal at the current epoch. In one embodiment, the terminal can send an ephemeris acquisition request to the satellite positioning base station server. After receiving the ephemeris acquisition request from the terminal, the satellite positioning base station server can obtain the real-time navigation ephemeris of satellites from the satellite ephemeris database and transmit the real-time navigation ephemeris of satellites to the terminal in the form of a binary stream. The terminal can then query the real-time navigation ephemeris of satellites observed at the current epoch to obtain the satellite positions and clock biases of each satellite observed at the current epoch based on the launch time of the satellite signals of each satellite observed at the current epoch.

[0095] Step 406: Based on the pseudorange of each satellite, the satellite position, the satellite clock error, the estimated terminal position, and the estimated terminal clock error, construct the pseudorange observation equation between the terminal and the satellite.

[0096] Specifically, the terminal can construct a pseudorange observation equation between the terminal and the satellite based on a priori error model formed by the relationship between the observed pseudorange and the actual distance between the satellite and the terminal. As mentioned earlier, the observed pseudorange contains errors caused by clock errors and atmospheric refraction delays. Based on this priori relationship, the pseudorange observation equation is constructed as follows:

[0097]

[0098] in, r represents the observation pseudorange of satellite i. u Indicates the location of the terminal to be estimated, r i (i = 1, 2, ..., n) represents the position of satellite i, dt r dt represents the estimated terminal clock bias. i (i = 1, 2, ..., n) represents the satellite clock error of satellite i, c is the speed of light in vacuum, and ζ i (i = 1, 2, ..., n) represents the error correction for satellite i, which is the error caused by atmospheric refraction delay, including errors from the ionosphere, troposphere, and Earth's rotation. This error correction can be calculated using an empirical model. Both the satellite position and the terminal position are represented in three-dimensional coordinates (x, y, z).

[0099] Step 304: Based on the satellite's observation pseudorange and the terminal positioning results, determine the satellite's observation pseudorange residual and calculate the first type of statistical parameters related to the observation pseudorange residual.

[0100] Specifically, the terminal can use the least squares method to perform single-epoch positioning calculations on the pseudorange observation equation. This positioning calculation process requires multiple iterations based on satellite observation data (including observed pseudorange and its signal-to-noise ratio) and the estimated parameters obtained from previous iterations, until the iteration stopping condition is met, at which point the terminal positioning result is obtained. This terminal positioning result includes the terminal position and the terminal clock error. Then, the terminal can determine the observed pseudorange residual for each satellite based on the observed pseudorange, satellite position, satellite clock error, and the terminal position and clock error obtained from the positioning calculation, obtaining an observed pseudorange residual sequence. Based on this sequence, the first type of statistical parameters regarding the observed pseudorange residuals are calculated. It should be noted that since this observed pseudorange residual is calculated based on the terminal positioning result obtained from the positioning calculation, it can also be called the post-hoc observed pseudorange residual.

[0101] It should be noted that if the terminal receives satellite signals from n satellites in the current epoch, during multiple iterations of the positioning calculation, the terminal can remove satellites with poor satellite observation data quality. Therefore, the terminal positioning result obtained in the final iteration may be based on the calculation of q (q < n) satellites out of the n satellites. Subsequent steps, such as determining the satellite observation pseudorange residuals based on the calculated terminal positioning results, calculating the first type of statistical parameters related to the observation pseudorange residuals, and step 306, are all based on the satellite observation data of these q satellites. In other words, during the iteration process, low-quality satellite observation data may be removed, and the satellite observation data of the remaining satellites, along with the terminal positioning result, are used to calculate the first and second types of statistical parameters, further improving the accuracy of the positioning quality indicators. However, for ease of explanation, the following text will use the example of obtaining the terminal positioning result in the final iteration based on the satellite observation data of n satellites.

[0102] For example, the observation pseudorange residual v of satellite i i It can be calculated using the following formula:

[0103]

[0104] in, This represents the observation pseudorange of satellite i. This represents the positioning distance of satellite i, where, r represents the calculated terminal position. i Indicates the satellite position of satellite i. dt represents the calculated terminal clock error. i ζ represents the satellite clock bias of satellite i. i Let be the error correction for satellite i. If the terminal receives satellite signals from n satellites in the current epoch, the terminal can calculate the observation pseudorange residuals for each of the n satellites according to the above formula, forming an observation pseudorange residual sequence V:

[0105]

[0106] The first type of statistical parameters are mathematical statistical parameters concerning the observation pseudorange residuals of each satellite, including at least the unit weighted mean square error δ, root mean square γ, and absolute median τ of the observation pseudorange residual sequence. The essence of the observation pseudorange residual sequence is the difference between the observed pseudorange and the positioning distance. If the residual is near zero and the jitter is small, the observation pseudorange quality is good; conversely, if the residual is large, it indicates poor observation pseudorange quality. The first type of statistical parameters obtained based on the observation pseudorange residual sequence can reflect the quality of satellite observation data, including the observed pseudorange, and thus indirectly reflect the positioning quality of the terminal positioning result.

[0107] Step 306: Based on the satellite observation pseudorange and the terminal positioning results, determine the single-difference observation pseudorange residual between satellites, and calculate the second type of statistical parameters for the single-difference observation pseudorange residual.

[0108] In one embodiment, the terminal can determine the single-difference observation pseudorange residual based on the satellite's observed pseudorange and the positioning distance determined based on the terminal's positioning result. Then, based on the apologetic observation pseudorange variance matrix and the single-difference observation pseudorange residual, it can calculate a second type of statistical parameter regarding the single-difference observation pseudorange residual. That is, after obtaining the terminal positioning result through a single-epoch positioning solution, the terminal can determine the single-difference observation pseudorange residual and calculate a second type of statistical parameter regarding it based on the apologetic observation pseudorange variance matrix.

[0109] Specifically, the terminal can combine the posterior error model with the unit weighted mean square error of the observation pseudorange residual in the first type of statistical parameters to calculate the posterior observation pseudorange variance of the satellite and construct the posterior observation pseudorange variance matrix S.

[0110] The terminal can select a reference satellite from the n satellites that have received satellite signals at the current epoch. The other satellites in these n satellites form a pair with the reference satellite, and a total of n-1 pairs of satellites can be formed. For any pair of satellites, the single-difference observation pseudorange residuals of this pair of satellites are calculated. Based on the single-difference observation pseudorange residuals of each pair of satellites, the single-difference observation pseudorange residual sequence M can be obtained.

[0111] The second type of statistical parameters are mathematical statistical parameters concerning the single-difference observation pseudorange residual sequence, including at least the unit weighted mean square error Δδ, root mean square Δγ, and absolute median Δτ. The essence of the single-difference observation pseudorange residual sequence is the difference between the observation single difference and the positioning single difference of a pair of satellites. If the residual is near zero and the jitter is small, it indicates that the observation single difference can effectively eliminate errors caused by various influences, and the observation pseudorange quality is relatively good. Based on the single-difference observation pseudorange residual sequence M and the subsequent observation pseudorange variance matrix S, the obtained second type of statistical parameters can reflect the quality of satellite observation data, including the observation pseudorange, and thus indirectly reflect the positioning quality of the terminal positioning result.

[0112] Step 308: Evaluate the positioning quality of the current epoch terminal positioning result based on the first type of statistical parameters and the second type of statistical parameters.

[0113] As mentioned earlier, both the first and second types of statistical parameters can reflect the quality of satellite observation data. The terminal positioning result is obtained by calculating the positioning based on the satellite observation data. If the quality of the satellite observation data is low, the positioning quality of the terminal positioning result obtained will obviously be poor. Therefore, the first and second types of statistical parameters can reflect the positioning quality of the terminal positioning result to a certain extent.

[0114] The terminal can evaluate the quality of the terminal positioning results based on the first type of statistical parameters and the second type of statistical parameters, determine whether the terminal positioning results are of good or bad quality, and thus determine whether to filter out the terminal positioning results.

[0115] In one embodiment, the terminal can also evaluate the positioning quality of the current epoch based on the relative changes between the first type of statistical parameters and the relative changes between the second type of statistical parameters corresponding to each adjacent epoch.

[0116] In one embodiment, the terminal may also evaluate the positioning quality of the current epoch positioning result using at least one of the following: a first type of statistical parameter, a second type of statistical parameter, the relative change between the first type of statistical parameter corresponding to the current epoch and the previous epoch, and the relative change between the second type of statistical parameter corresponding to the current epoch and the previous epoch.

[0117] In one embodiment, the terminal can also determine the smoothness of the positioning trajectory in the current epoch based on the relative changes between the first type of statistical parameters and the relative changes between the second type of statistical parameters in adjacent epochs, and evaluate the positioning quality of the terminal positioning result in the current epoch based on the smoothness of the positioning trajectory.

[0118] In one embodiment, the terminal can also determine the scene in which it is located based on the unit weighted mean square error of the observation pseudorange residual and the unit weighted mean square error of the single-difference observation pseudorange residual; and evaluate the positioning quality of the current epoch terminal positioning result based on the scene in which the terminal is located.

[0119] In one embodiment, the terminal can also calculate the statistical parameters of the signal-to-noise ratio (SNR) of the observed pseudorange, and output statistical information about the SNR based on the statistical parameters of the SNR, including the maximum value, minimum value, standard deviation, and absolute median difference of the SNR of the observed pseudorange of each satellite.

[0120] As mentioned above, the terminal can perform a positioning quality assessment based on one or more of the previous statistical parameters. It can also combine one or more of the above statistical parameters, the relative change of statistical parameters, the smoothness of the positioning trajectory, and the terminal scenario to perform a positioning quality assessment, thereby obtaining a quality assessment result of the terminal positioning result output by the single-epoch least squares solution module, and filtering out positioning results with poor positioning indicators or obvious deviations.

[0121] The aforementioned positioning quality assessment method acquires satellite observation data for the current epoch of the terminal, calculates the terminal positioning result based on the observation pseudorange in the satellite observation data, and then determines the observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, calculating a first type of statistical parameter regarding the observation pseudorange residual. Furthermore, using the satellite observation pseudorange and the aforementioned calculated terminal positioning result, the single-difference observation pseudorange residual is determined, and a second type of statistical parameter regarding the single-difference observation pseudorange residual can be calculated. Therefore, combining the first and second type of statistical parameters as positioning quality assessment indicators can accurately evaluate the positioning quality of the terminal positioning result for the current epoch, thereby improving the accuracy of the terminal positioning result.

[0122] like Figure 5 The diagram shown illustrates a process for solving the pseudorange observation equation based on satellite observation data in one embodiment. (Refer to...) Figure 5 This step includes:

[0123] Step 502: In the current iteration of the least squares solution, obtain the estimated parameters of the previous iteration, including the estimated position and the estimated clock difference; obtain the target satellites selected after the previous iteration.

[0124] The parameters to be estimated in the least squares solution include the estimated terminal location and the estimated terminal clock error:

[0125]

[0126] Assuming the previous iteration was the k-th iteration and the current iteration is the (k+1)-th iteration, after the current iteration begins, the terminal obtains the estimated parameters from the previous iteration. Where, r u,k Indicates the terminal estimated position and dt of the previous iteration. r,k This represents the terminal's estimated clock bias from the previous iteration. During the first iteration, the terminal obtains preset initial estimation parameters and begins iteration from these initial estimation parameters.

[0127] In addition, each iteration may remove some satellites of poor quality from the n satellites. The terminal also obtains the target satellites selected after the kth iteration. For example, if the target satellites are m satellites out of n satellites, the (k+1)th iteration is performed based on the satellite observation data of these m satellites, where m is less than or equal to n and greater than 4.

[0128] Step 504: Calculate the observation pseudorange variance of the target satellite based on the signal-to-noise ratio of the observation pseudorange of the target satellite and the elevation angle of the satellite determined based on the satellite position corresponding to the target satellite and the terminal estimated position of the previous iteration; construct the observation pseudorange variance matrix based on the observation pseudorange variance of each target satellite.

[0129] The terminal obtains the estimated terminal position r in the k-th iteration. u,k Based on the satellite position corresponding to each target satellite and the terminal's estimated position r u,k Calculate the elevation angle el corresponding to each target satellite. i k Then, the terminal calculates the variance of the target satellite's observed pseudorange based on the signal-to-noise ratio of the pseudorange and the elevation angle.

[0130]

[0131] in, CNO i Let el be the signal-to-noise ratio of the pseudorange observed by satellite i (i = 1, 2, ..., m). i k Let represent the elevation angle of satellite i in the k-th iteration.

[0132] Based on the observation pseudorange variances of each target satellite determined in the previous iteration, the terminal constructs the observation pseudorange variance matrix for the k-th iteration:

[0133]

[0134] Step 506: Based on the satellite observation data of the target satellite, determine the pseudorange observation equation for the current iteration.

[0135] Based on satellite observation data from m target satellites, the terminal determines the pseudorange observation equation for the current iteration according to the following formula:

[0136]

[0137] Step 508: Obtain the differential matrix of the pseudorange observation equation for the parameters to be estimated in the current iteration. The parameters to be estimated include the terminal position and the terminal clock error to be estimated. Based on the differential matrix, the observation pseudorange variance matrix, and the pseudorange observation residual of the target satellite in the current iteration, determine the correction amount of the estimated parameters for the current iteration.

[0138] The terminal calculates the partial derivatives of the parameters to be estimated with respect to the pseudorange observation equation of the current iteration, and obtains the differential matrix of the parameters to be estimated:

[0139]

[0140] In the formula, Represents the unit vector from the terminal to satellite i;

[0141] The terminal calculates the pseudorange observation residuals of the m target satellites after the previous iteration based on the terminal estimated position, terminal estimated clock error, and the satellite position and clock error corresponding to each target satellite from the previous iteration:

[0142]

[0143] The terminal then determines the correction amount of the estimated parameters for the current iteration based on the differential matrix, the observation pseudorange variance matrix, and the pseudorange observation residuals of the target satellite.

[0144]

[0145] Step 510: Correct the estimated parameters of the previous iteration based on the correction amount of the estimated parameters of the current iteration to obtain the estimated parameters of the current iteration.

[0146] x u,k+1 =x u,k +Δx ρ,k ;

[0147] in,

[0148] In one embodiment, the solution process may further include:

[0149] If the correction amount of the estimated parameters in the current iteration is greater than the first preset threshold, the iteration process continues. If the correction amount of the estimated parameters in the current iteration is less than the first preset threshold, based on the estimated parameters of the current iteration, the post-observation pseudorange residual sequence and the post-observation pseudorange variance matrix are determined. The chi-square test statistic is calculated based on the post-observation pseudorange variance matrix and the post-observation pseudorange residual sequence. If the chi-square test statistic is less than the second preset threshold, the iteration stops, and the terminal's positioning result is obtained based on the estimated parameters of the current iteration. If the chi-square test statistic is greater than the second preset threshold, a normality test is performed on the post-observation pseudorange residual sequence based on the post-observation pseudorange residual covariance matrix of the current iteration. After removing the observed pseudoranges that fail the normality test, the iteration process continues using the observed pseudoranges of the selected target satellite.

[0150] Specifically, if the correction amount of the estimated parameters in the current iteration is greater than the first preset threshold, the iteration process continues. In other words, if the correction amount of the estimated parameters in the current iteration is greater than the first preset threshold, the next iteration process will continue based on the terminal estimated parameters obtained in the current iteration.

[0151] When the estimated parameter correction amount in the current iteration is less than the first preset threshold, the chi-square test statistic is calculated. Specifically, for the m satellites, the formula for calculating the posterior observation pseudorange residual sequence in the current iteration is as follows:

[0152]

[0153] In addition, the terminal obtains the estimated terminal position for the current iteration, calculates the elevation angle corresponding to each target satellite based on the satellite position corresponding to each target satellite and the estimated terminal position for the current iteration, and then calculates the posterior observation pseudorange variance for the current iteration based on the signal-to-noise ratio of the observed pseudorange of the target satellite and the elevation angle.

[0154]

[0155] in, CNO i Let el be the signal-to-noise ratio of the pseudorange observed by satellite i (i = 1, 2, ..., m). k+1 i This represents the elevation angle of satellite i calculated based on the terminal estimated position in the (k+1)th iteration.

[0156] The terminal constructs the posterior observation pseudorange variance matrix based on the posterior observation pseudorange variance of the current iteration:

[0157]

[0158] Based on the post-test observed pseudorange variance matrix and the post-test observed pseudorange residual sequence, the chi-square test statistic s is calculated using the following formula:

[0159]

[0160] When the chi-square test statistic exceeds the second preset threshold, the terminal can further perform a normality test on the post-observation pseudorange residual sequence based on the covariance matrix of the post-observation pseudorange residuals from the current iteration. From the observed pseudoranges of m satellites, pseudoranges that fail the normality test are removed, and q satellites are selected. Based on the terminal's estimated parameters obtained in this iteration, the next iteration, i.e., the (k+2)th iteration, is then performed. When the chi-square test statistic is less than the second preset threshold, the iteration stops, and the terminal's positioning result is obtained based on the estimated parameters from the current iteration.

[0161] At the end of the entire iteration, output the terminal localization result obtained from the localization solution:

[0162]

[0163] in Indicates the terminal location. This indicates the terminal clock bias.

[0164] In one embodiment, the solution process may further include: when constructing the pseudorange observation equation for the current iteration, performing gross error removal on the observed pseudoranges of each satellite, and using the gross error-removed observed pseudoranges to construct the pseudorange observation equation for the current iteration's positioning solution, thereby improving positioning quality. Specifically, this includes: during multiple iterations of least-squares solution of the pseudorange observation equation based on satellite observation data, calculating the estimated distance between the terminal and the satellite based on the satellite position corresponding to the current epoch, the satellite clock error, the estimated terminal position value obtained from the previous iteration, and the estimated terminal clock error value; calculating the residual between the observed pseudoranges of each satellite and the estimated distance, performing gross error removal on the residuals based on quartiles, selecting the target observed pseudorange from the observed pseudoranges of each satellite, and using the pseudorange observation equation formed by the target observed pseudorange to perform least-squares solution for the current iteration.

[0165] It should be noted that if, in the previous iteration, m target satellites were selected from the n satellites observed by the terminal by removing the observed pseudoranges that failed the normality test, then the gross error removal mentioned in this embodiment is to further remove gross errors from the selected m target satellites to filter out the observed pseudoranges with larger gross errors, resulting in p target satellites. The pseudorange observation equation is then constructed using these p satellites for the least squares solution of the current iteration.

[0166] like Figure 6 The diagram shown illustrates the iterative process of least squares solution in one embodiment. (Refer to...) Figure 6Starting from the (k+1)th iteration, based on satellite observation data and the estimated parameters of the kth iteration, an observation pseudorange residual sequence is constructed. Gross errors are removed from the constructed observation pseudorange residual sequence based on quartiles. Based on the satellite observation data after gross error removal, a pseudorange observation equation is constructed, and the differential matrix of this pseudorange observation equation with respect to the estimated parameters is calculated. Based on the satellite position and the terminal estimated position of the kth iteration, an observation pseudorange variance matrix is ​​constructed. Based on the differential matrix, the observation pseudorange variance matrix, and the observation pseudorange residual sequence, the estimated parameter correction is calculated, and the estimated parameters are updated according to this correction to obtain the estimated parameters for the (k+1)th iteration. Finally, it is determined whether the estimated parameter correction is correct. If the value is less than the first threshold, then the (k+2)th iteration begins. If the value is greater than the second threshold, then the post-observation pseudorange variance matrix and the post-observation pseudorange residual sequence are calculated based on the estimated parameters of the (k+1)th iteration. The chi-square test statistic is then calculated based on the post-observation pseudorange variance matrix and the post-observation pseudorange residual sequence. The chi-square test statistic is then determined to be greater than the second threshold. If the value is greater than the second threshold, then the post-observation pseudorange residual covariance matrix is ​​calculated. The post-observation pseudorange residual sequence is then subjected to a normality test based on the post-observation pseudorange residual covariance matrix. After removing the observed pseudoranges that fail the normality test, the (k+2)th iteration begins. If the value is less than the first threshold, then the entire solution process ends, and the terminal positioning result is output.

[0167] The following describes the specific calculation process for the first type of statistical parameter:

[0168] In one embodiment, such as Figure 7 As shown, the observed pseudorange residuals are determined based on the terminal positioning results obtained from the solution, and the first type of statistical parameters about the observed pseudorange residuals are calculated, including:

[0169] Step 702: Calculate the positioning distance between the terminal and the satellite based on the satellite position and clock bias corresponding to the current epoch satellite, as well as the calculated terminal position and clock bias.

[0170] The positioning distance of each satellite can be calculated based on its satellite position, clock bias, error correction, and the terminal position and clock bias from the terminal positioning result. For example, the positioning distance of satellite i can be calculated using the following formula:

[0171]

[0172] in Indicates the terminal location. This indicates the terminal clock bias.

[0173] Step 704: Calculate the residual between the observation pseudorange of each satellite and the corresponding positioning distance to obtain the observation pseudorange residual sequence.

[0174] For example, the observation pseudorange residual v of satellite i iIt can be calculated using the following formula:

[0175]

[0176] in, This represents the observation pseudorange of satellite i. This represents the positioning distance of satellite i, where, r represents the calculated terminal position. i Indicates the satellite position of satellite i. dt represents the calculated terminal clock error. i ζ represents the satellite clock bias of satellite i. i Let be the error correction for satellite i. If the terminal receives satellite signals from n satellites in the current epoch, then the terminal can calculate the observation pseudorange residuals for each of the n satellites according to the above formula, forming an observation pseudorange residual sequence V = {V1, V2, ..., Vn}:

[0177]

[0178] Step 706: Calculate the root mean square, absolute median, and unit weighted mean error of the observed pseudorange residual sequence.

[0179] The root mean square (RMS) is calculated by summing the squares of all values ​​in a numerical sequence, taking the mean, and then taking the square root. The formula for calculating the RMS of the observation pseudorange residual sequence is as follows:

[0180]

[0181] The root mean square (RMS) of the observed pseudorange residual sequence reflects the degree of agreement between the observed pseudorange and the positioning distance, and to a certain extent, reflects the quality of the observed pseudorange. A smaller RMS indicates higher quality, and therefore, higher positioning quality based on the observed pseudorange. For example, a terminal can assess the quality of its positioning results based on the RMS value. When the RMS is less than or equal to a preset threshold, the positioning quality assessment result is considered correct; when the RMS is greater than a preset error threshold, the positioning quality assessment result is considered incorrect. The error thresholds set in different accuracy assessment methods can vary, and these thresholds can be manually set according to specific needs.

[0182] The formula for calculating the absolute median of the observed pseudorange residual sequence is as follows:

[0183] τ=1.4826·Median({V1-Median(V),V2-Median(V),...,V n -Median(V)});

[0184] The absolute median is calculated by first determining the median of a numerical sequence, then calculating the difference between each data point and the median, and finally calculating the median of these differences. The absolute median reflects the degree of fluctuation in the numerical sequence. Similarly, the absolute median of the observed pseudorange residual sequence reflects the degree of fluctuation in the observed pseudorange. A smaller absolute median indicates less fluctuation in the observed pseudorange, higher quality pseudorange, and therefore higher positioning quality based on the observed pseudorange.

[0185] In one embodiment, the satellite observation data further includes the signal-to-noise ratio of the observed pseudorange, determining the observed pseudorange residuals based on the calculated terminal positioning results, and calculating a first type of statistical parameter for the observed pseudorange residuals. This may further include: calculating the observed pseudorange variance of the satellite based on the signal-to-noise ratio of the observed pseudorange and the satellite elevation angle determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position; constructing an observed pseudorange variance matrix based on the observed pseudorange variances of each satellite; and calculating the unit weighted mean square error of the observed pseudorange residual sequence based on the observed pseudorange residual sequence and the observed pseudorange variance matrix.

[0186] The formula for calculating the variance of satellite observation pseudorange is as follows:

[0187]

[0188] Among them, CNO i Let el be the signal-to-noise ratio of the pseudorange observed by satellite i (i = 1, 2, ..., m). i This represents the elevation angle of satellite i, determined based on the satellite position and the terminal position.

[0189] The terminal constructs an observation pseudorange variance matrix based on the observation pseudorange variance of each satellite:

[0190]

[0191] The unit weighted mean square error of the observation pseudorange residuals is calculated based on the observation pseudorange variance matrix and the observation pseudorange residuals:

[0192]

[0193] The unit weighted mean square error is the mean square error of the values ​​with a weight of 1 in the numerical sequence. Mean square error is an indicator of observation accuracy; therefore, the unit weighted mean square error reflects the accuracy of the observed pseudorange. The smaller the value, the higher the accuracy of the observed pseudorange and the higher the positioning quality. The terminal can evaluate the quality of its positioning results based on the magnitude of the unit weighted mean square error.

[0194] like Figure 8The diagram shown is a flowchart illustrating the calculation of the first type of statistical information in one embodiment. (Refer to...) Figure 8 After obtaining the terminal positioning result through positioning calculation, the observation pseudorange variance matrix can be calculated based on the satellite position, terminal position, and signal-to-noise ratio of satellite observation pseudorange. The observation pseudorange residual sequence can be calculated based on the satellite observation pseudorange, satellite position, satellite clock error, terminal position, and terminal clock error. The root mean square γ and absolute median τ can be output based on the observation pseudorange residual sequence. The unit weighted mean error δ can be output based on the observation pseudorange variance matrix and the observation pseudorange residual sequence.

[0195] The following describes the specific calculation process for the second type of statistical information:

[0196] In one embodiment, such as Figure 9 As shown, based on the satellite's observed pseudorange and the terminal positioning results, the single-difference observed pseudorange residuals between satellites are determined, including:

[0197] Step 902: Determine the reference satellite from the observation satellites of the current epoch terminal.

[0198] The terminal can select a reference satellite from n satellites that have received satellite signals at the current epoch. Each of the other n satellites forms a pair with the reference satellite, for a total of n-1 pairs. The reference satellite can be the one with the largest elevation angle among the n satellites. The satellite elevation angle represents the angle between the line connecting the satellite's position and the terminal's position and the horizontal plane where the terminal is located, and can be calculated based on the satellite's position and the terminal's position.

[0199] Step 904: For each non-reference satellite among the observation satellites, calculate the observation pseudorange difference between the non-reference satellite and the reference satellite, and calculate the positioning pseudorange difference between the corresponding positioning distance of the non-reference satellite and the corresponding positioning distance of the reference satellite.

[0200] For any pair of satellites, the terminal can calculate the difference in the observation pseudoranges of the pair of satellites to obtain the observation single difference of the pair of satellites; and calculate the difference in the positioning distances of the pair of satellites to obtain the positioning single difference of the pair of satellites.

[0201] The positioning distance of each satellite can be calculated based on its satellite position, clock bias, error correction, and the terminal position and clock bias in the positioning results. For example, the positioning distance of satellite i can be calculated using the following formula:

[0202]

[0203] Step 906: Determine the pseudorange residual of single-difference observations based on the residuals between the single observation difference and the single positioning difference corresponding to each non-reference satellite.

[0204] The terminal then calculates the residuals between the observation single difference and the positioning single difference for this pair of satellites, obtaining the single-difference observation pseudorange residuals for this pair of satellites. Based on the single-difference observation pseudorange residuals for each pair of satellites, a sequence of single-difference observation pseudorange residuals can be obtained.

[0205] Taking satellite 1 as the reference satellite, the residual m of the single-difference observation pseudorange between satellite 2 and satellite 1 is... 2,1 It can be calculated using the following formula:

[0206]

[0207] The terminal can calculate the pseudorange residuals of single-difference observations for n-1 pairs of satellites according to the above formula, forming a sequence of single-difference observation pseudorange residuals M = {M1, M2, ..., Mn-1}:

[0208]

[0209] In one embodiment, based on the a priori observation pseudorange variance matrix and the single-difference observation pseudorange residuals, a second type of statistical parameters for the single-difference observation pseudorange residuals are calculated, including: calculating the root mean square and absolute median of the single-difference observation pseudorange residual sequence formed by the single-difference observation pseudorange residuals of each non-reference satellite.

[0210] The formula for calculating the root mean square Δγ of the pseudorange residual sequence of single-difference observations is as follows:

[0211]

[0212] The formula for calculating the absolute median Δτ of the pseudorange residual sequence of single-difference observations is as follows:

[0213] Δτ=1.4826·Median({M1-Median(M),M2-Median(M),...,M n-1 -Median(M)});

[0214] In one embodiment, based on the post-hoc observation pseudorange variance matrix and the single-difference observation pseudorange residuals, a second type of statistical parameter is calculated regarding the single-difference observation pseudorange residuals. This includes: calculating the post-hoc observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange, the satellite elevation angle determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position, and the unit weighted mean square error of the observation pseudorange residuals in the first type of statistical parameter; constructing the post-hoc observation pseudorange variance matrix based on the post-hoc observation pseudorange variance matrix of each satellite; and calculating the unit weighted mean square error of the single-difference observation pseudorange residual sequence based on the post-hoc observation pseudorange variance matrix and the single-difference observation pseudorange residual sequence.

[0215] The formula for calculating the variance of the satellite's post-hoc observation pseudorange is as follows:

[0216]

[0217] in, CNO i Let el be the signal-to-noise ratio of the pseudorange observed by satellite i (i = 1, 2, ..., m). i This represents the elevation angle of satellite i, determined based on the satellite position and the terminal position.

[0218] The terminal constructs the post-hoc observation pseudorange variance matrix S based on the post-hoc observation pseudorange variance of each satellite:

[0219]

[0220] Based on the verified observed pseudorange variance matrix S and the single-difference observed pseudorange residual sequence M, calculate the unit weighted mean square error of the single-difference observed pseudorange residual sequence:

[0221]

[0222] like Figure 10 The diagram shown illustrates the flowchart for calculating the second type of statistical information in one embodiment. (Refer to...) Figure 10 After obtaining the terminal positioning result through positioning calculation, the post-tested observation pseudorange variance matrix is ​​calculated based on the terminal position and the unit weighted mean error in the first type of statistical parameters. A reference satellite is selected, and a single-difference observation pseudorange residual sequence is constructed based on the satellite's single-difference observation pseudorange residual. The root mean square Δγ and absolute median Δτ are calculated based on the single-difference observation pseudorange residual sequence, and the unit weighted mean error Δδ is calculated based on the single-difference observation pseudorange residual sequence and the post-tested observation pseudorange variance matrix.

[0223] In one embodiment, the terminal can calculate the posterior parameter covariance matrix of the current epoch and the posterior parameter covariance matrix of the previous epoch; and calculate the relative change in posterior variance based on the posterior parameter covariance matrices of adjacent epochs.

[0224] Based on the previous explanation of epochs, each epoch corresponds to different satellite observation data, and each epoch yields a different terminal positioning result. The posterior parameter covariance matrix of the current epoch can be denoted as P. j+1 The posterior parameter covariance matrix of the previous epoch can be denoted as P. j The terminal can calculate the relative change in posterior variance using the following formula.

[0225]

[0226]

[0227]

[0228]

[0229] In one embodiment, calculating the posterior parameter covariance matrix for the current epoch includes: calculating the observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observed pseudorange at the current epoch and the satellite elevation angle determined based on the satellite position corresponding to the satellite at the current epoch and the calculated terminal position; constructing the observation pseudorange variance matrix based on the observation pseudorange variance of each satellite; obtaining the differential matrix of the pseudorange observation equation with respect to the parameters to be estimated, the parameters to be estimated including the terminal position to be estimated and the terminal clock error to be estimated; and calculating the posterior parameter covariance matrix for the current epoch based on the observation pseudorange variance matrix, the differential matrix, and the unit weighted mean square error of the observation pseudorange residual at the current epoch.

[0230] The formula for calculating the variance of satellite observation pseudorange is as follows:

[0231]

[0232] Among them, CNO i Let el be the signal-to-noise ratio of the pseudorange observed by satellite i (i = 1, 2, ..., m). i This represents the elevation angle of satellite i, determined based on the satellite position and the terminal position.

[0233] The terminal constructs an observation pseudorange variance matrix based on the observation pseudorange variance of each satellite:

[0234]

[0235] The observation pseudorange variance matrices of the current epoch and the previous epoch are denoted as w, respectively. ρ (j+1), w ρ (j).

[0236] The terminal constructs the pseudorange observation equation for the current epoch based on the satellite observation data of the current epoch, and obtains the differential matrix of the pseudorange observation equation with respect to the parameter to be estimated:

[0237]

[0238] The terminal constructs the pseudorange observation equation for the previous epoch based on the satellite observation data from the previous epoch, and obtains the differential matrix of the pseudorange observation equation with respect to the parameter to be estimated:

[0239]

[0240] The posterior parameter covariance matrix P of the current epoch j+1 The calculation formula is:

[0241] P j+1 =δ j+1 (G ρT (j+1)W(j+1)G ρ (j+1) T );

[0242] The posterior parameter covariance matrix P of the previous epoch j The calculation formula is:

[0243] P j =δ j (G ρ T (j)W(j)G ρ (j) T ).

[0244] In one embodiment, the terminal can statistically analyze the relative changes in the unit weighted mean square error, the root mean square, and the absolute median of the observed pseudorange residuals between the current epoch and the previous epoch; statistically analyze the relative changes in the unit weighted mean square error, the root mean square, and the absolute median of the observed pseudorange residuals between the current epoch and the previous epoch; and evaluate the positioning quality of the terminal positioning result at the current epoch based on the statistically analyzed relative changes between the first type of statistical parameters and the second type of statistical parameters corresponding to the current epoch and the previous epoch.

[0245] The formulas for calculating the relative changes in the unit weighted mean error, the relative change in the root mean square, and the relative change in the absolute median of the observed pseudorange residuals between the current epoch and the previous epoch are as follows:

[0246]

[0247]

[0248]

[0249] The formulas for calculating the relative changes in unit weighted mean error, root mean square, and absolute median of the pseudorange residuals of single-difference observations between the current epoch and the previous epoch are as follows:

[0250]

[0251]

[0252]

[0253] Furthermore, the terminal can also determine the maximum value among the relative changes in posterior variance, the relative changes in the unit weighted mean square error of the observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median, the relative changes in the unit weighted mean square error of the single-difference observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median; compare the determined maximum value with the threshold corresponding to the smoothness of each level of positioning trajectory to determine the smoothness of the positioning trajectory corresponding to the current epoch; and evaluate the positioning quality of the terminal positioning results at the current epoch based on the smoothness of the positioning trajectory.

[0254] Specifically, refer to Figure 11 This is a schematic diagram illustrating the determination of the smoothness of the current positioning trajectory of a terminal in one embodiment. (Refer to...) Figure 11 Based on the terminal positioning results calculated using the least squares method for a single epoch, the terminal can output the unit weighted mean square error, root mean square, and absolute median of the observation pseudorange residual for that single epoch. This allows us to obtain the relative changes in the unit weighted mean square error, root mean square, and absolute median of the observation pseudorange residual between adjacent epochs. Based on the single-difference observation pseudorange residuals for each epoch, we can statistically analyze the relative changes in unit weighted mean error, root mean square, and absolute median of the single-difference observation pseudorange residuals between adjacent epochs. The terminal can base its calculations on the aforementioned relative changes and the relative changes in posterior variance. Determine the smoothness of the terminal's current positioning trajectory.

[0255] Assumption and If the maximum value in the range is ε, then when ε is greater than 1.5, the smoothness of the positioning trajectory of the current epoch terminal is determined to be non-tolerable jump, meaning the positioning result may have a large deviation; when ε is greater than 1.0, the smoothness of the positioning trajectory of the current epoch terminal is determined to be tolerant of jump, meaning the positioning result has an acceptable deviation; when ε is less than 1.0, the smoothness of the positioning trajectory of the current epoch terminal is determined to be a smooth trajectory, meaning the positioning quality of the positioning result is good. In other words, and By comparing the results with a pre-set threshold, the current positioning quality of the terminal can be determined, and positioning results with poor accuracy or significant deviation can be filtered out.

[0256] In one embodiment, the terminal can also determine the scene in which it is located based on the unit weighted mean square error of the observation pseudorange residual and the unit weighted mean square error of the single-difference observation pseudorange residual; and evaluate the positioning quality of the current epoch terminal positioning result based on the scene in which the terminal is located.

[0257] Reference Figure 12 This is a schematic diagram illustrating the scenario in which the terminal is located, as shown in one embodiment. (Refer to...) Figure 12The terminal can also be based on the relative change in posterior variance. The scene in which the terminal is located is determined by the unit weighted mean square error δ of the observed pseudorange residual and the unit weighted mean square error Δδ of the single-difference observed pseudorange residual.

[0258] Specifically, when δ < 1 and Δδ < 1, the terminal is in an open scene; when 1 < δ < 1.2 and 1 < Δδ < 1.2, the terminal is in a partially occluded scene; when 1.2 < δ < 1.5 and 1.2 < Δδ < 1.5, the terminal is in an occluded scene; when δ > 1.5 and Δδ > 1.5, the terminal is in a high-rise street scene; when... At that time, the terminal is in a tunnel entry / exit scenario. Other terminal scenarios can also be determined based on... The values ​​of δ and Δδ are adapted.

[0259] In one embodiment, the terminal can also obtain the signal-to-noise ratio (SNR) of the observation pseudoranges of each satellite for which the terminal received the satellite transmission signal from the satellite observation data; and output statistical information about the SNR based on the maximum value, minimum value, standard deviation, and absolute median difference of the SNR of each satellite observation pseudorange.

[0260] Specifically, the terminal receives signals transmitted by n satellites, and the signal-to-noise ratio of the signals from the n satellites can be obtained by the terminal as T = {CN01, CN02, ..., CN0}. n};

[0261] The maximum and minimum values ​​are: CN0 max =MAX(T); CN0 min (MIN(T);

[0262] The standard deviation and variance are:

[0263]

[0264] σ 2 CN0 =(σ CN0 ) 2 ;

[0265] The absolute median difference is:

[0266] τ CN0 =1.4826·Median({CN01-Median(T),CN02-Median(T),...,CN0 n -Median(T)}).

[0267] It's understandable that a higher signal-to-noise ratio (SNR) indicates better data quality. For example, a terminal can compare the maximum SNR value with a preset threshold (min) and the minimum SNR value with a preset threshold (max). If the maximum SNR is less than the preset threshold (min), the data quality is considered poor; conversely, if the minimum SNR is greater than the preset threshold (max), the data quality is considered excellent. The comparison results can be used to make judgments based on actual needs. Judgments can also be made based on the SNR variance and standard deviation. In this way, SNR statistics can reflect the quality of positioning results to a certain extent, enabling positioning quality assessment and control based on SNR statistics.

[0268] Based on this, the terminal can output positioning quality assessment indicators such as statistical parameters of signal-to-noise ratio, first-type statistical parameters, second-type statistical parameters, relative changes of first-type statistical parameters, relative changes of second-type statistical parameters, smoothness of terminal positioning trajectory, and terminal scene. One or more of these indicators can be used to accurately evaluate the terminal's positioning quality, perform quality control on the terminal's output positioning results, and thus improve the accuracy of the terminal's position assessment. These indicators can also be used in conjunction with sensors to perform fusion positioning of the terminal, obtaining more accurate positioning results.

[0269] In one specific embodiment, the location quality assessment method includes the following steps:

[0270] 1. Obtain satellite observation data at the current epoch of the terminal. The satellite observation data includes the observation pseudorange between the terminal and the satellite and the signal-to-noise ratio of the observation pseudorange.

[0271] 2. Based on the observed pseudorange between the terminal and the satellite at the current epoch and the reception time of the satellite signal received by the terminal, calculate the transmission time of the satellite signal, query the real-time navigation ephemeris of the satellite based on the transmission time, and obtain the satellite position and satellite clock error corresponding to the current epoch.

[0272] 3. Based on the observed pseudorange, the estimated terminal position, the estimated terminal clock error, the satellite position, and the satellite clock error, construct the pseudorange observation equation between the terminal and the satellite; perform least squares solution on the pseudorange observation equation based on satellite observation data to obtain the terminal positioning result, which includes the terminal position and the terminal clock error.

[0273] 4. Based on the terminal position, terminal clock error, satellite position, and satellite clock error, determine the positioning distance between the satellite and the terminal. Based on the difference between the satellite's observed pseudorange and the positioning distance, determine the single-difference observed pseudorange residual sequence; calculate the root mean square and absolute median of the observed pseudorange residual sequence.

[0274] 5. Calculate the observation pseudorange variance of the satellites based on the signal-to-noise ratio of the observed pseudorange and the satellite elevation angle determined by the satellite position corresponding to the current epoch satellite and the terminal position obtained by the solution; construct the observation pseudorange variance matrix based on the observation pseudorange variance of each satellite; calculate the unit weighted mean square error of the observation pseudorange residual sequence based on the observation pseudorange residual sequence and the observation pseudorange variance matrix.

[0275] 6. Determine the reference satellite from the observation satellites of the current epoch terminal; for each non-reference satellite among the observation satellites, calculate the observation pseudorange difference between the non-reference satellite and the reference satellite, and calculate the positioning pseudorange difference between the non-reference satellite and the reference satellite.

[0276] 7. Based on the residuals between the observation single difference and the positioning single difference corresponding to each non-reference satellite, determine the single-difference observation pseudorange residual sequence; calculate the root mean square and absolute median of the single-difference observation pseudorange residual sequence;

[0277] 8. Based on the signal-to-noise ratio of the observed pseudorange, the satellite elevation angle determined by the satellite position corresponding to the current epoch satellite and the calculated terminal position, and the unit weighted mean square error of the observed pseudorange residual in the first type of statistical parameters, calculate the posterior observed pseudorange variance of the satellite; construct the posterior observed pseudorange variance matrix based on the posterior observed pseudorange variance matrix of each satellite; calculate the unit weighted mean square error of the single-difference observed pseudorange residual sequence based on the posterior observed pseudorange variance matrix and the single-difference observed pseudorange residual sequence.

[0278] 9. Obtain the differential matrix of the pseudorange observation equation with respect to the parameters to be estimated, including the terminal position and the terminal clock error to be estimated; calculate the posterior parameter covariance matrix of the current epoch based on the observation pseudorange variance matrix, differential matrix, and the unit weighted mean square error of the observation pseudorange residual in step 5; calculate the relative change of posterior variance based on the posterior parameter covariance matrix of the current epoch and the posterior parameter covariance matrix of the previous epoch.

[0279] 10. Calculate the relative changes in the unit weighted mean error, root mean square, and absolute median of the observed pseudorange residuals between the current epoch and the previous epoch.

[0280] 11. Determine the maximum value among the relative changes in post-hoc variance, the relative changes in the unit weighted mean square error of the observed pseudorange residuals, the relative changes in the root mean square, the relative changes in the absolute median, the relative changes in the unit weighted mean square error of the single-difference observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median; compare the determined maximum value with the threshold corresponding to the smoothness of the positioning trajectory at each level to determine the smoothness of the positioning trajectory corresponding to the current epoch;

[0281] 12. Determine the scenario in which the terminal is located based on the relative change of the post-hoc variance, the unit weighted mean square error of the observed pseudorange residual, and the unit weighted mean square error of the single-difference observed pseudorange residual.

[0282] 13. Obtain the signal-to-noise ratio (SNR) of the pseudoranges of each satellite whose transmitted signals are received by the terminal from the satellite observation data; output statistical information about the SNR based on the maximum value, minimum value, standard deviation, and absolute median deviation of the SNR of each satellite's pseudoranges.

[0283] 14. Evaluate the positioning quality of the terminal positioning results based on one or more of the following: the root mean square, absolute median, and unit weighted mean error of the observed pseudorange residual sequence; the root mean square, absolute median, and unit weighted mean error of the single-difference observed pseudorange residual sequence; the relative change in posterior variance between adjacent epochs; the relative change in unit weighted mean error, root mean square, and absolute median of the observed pseudorange residual between the current epoch and the previous epoch; the relative change in unit weighted mean error, root mean square, and absolute median of the single-difference observed pseudorange residual between the current epoch and the previous epoch; the smoothness of the positioning trajectory corresponding to the current epoch; the scene in which the terminal is located; and the statistical information of the signal-to-noise ratio.

[0284] The aforementioned positioning quality assessment method acquires satellite observation data for the current epoch of the terminal, constructs a pseudorange observation equation between the terminal and the satellite based on the pseudorange in the satellite observation data, and then solves this pseudorange observation equation based on the satellite observation data. Based on the terminal positioning result obtained from the solution, the pseudorange residual can be determined, and a first-type statistical parameter related to the pseudorange residual can be calculated. Furthermore, using the satellite's pseudorange and the aforementioned terminal positioning result, a single-difference pseudorange residual is determined. Then, based on the post-hoc pseudorange variance matrix and this single-difference pseudorange residual, a second-type statistical parameter related to the single-difference pseudorange residual can be calculated. Thus, by combining the relative changes between the first-type statistical parameters corresponding to the current epoch and the relative changes between the corresponding second-type statistical parameters, these relative changes serve as positioning quality assessment indicators, accurately evaluating the positioning quality of the terminal positioning result at the current epoch, thereby improving the accuracy of the terminal positioning result.

[0285] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0286] Based on the same inventive concept, this application also provides a positioning quality assessment device for implementing the positioning quality assessment method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more positioning quality assessment device embodiments provided below can be found in the limitations of the positioning quality assessment method described above, and will not be repeated here.

[0287] In one embodiment, such as Figure 13 As shown, a positioning quality assessment device 1300 is provided, including: an acquisition module 1302, a first statistical module 1304, a second statistical module 1306, and a quality assessment module 1308, wherein:

[0288] The acquisition module 1302 is used to acquire satellite observation data of the terminal at the current epoch, the satellite observation data including the observation pseudorange between the terminal and the satellite; and to acquire the terminal positioning result calculated based on the satellite observation data.

[0289] The first statistical module 1304 is used to determine the satellite observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, and to calculate the first type of statistical parameters about the observation pseudorange residual.

[0290] The second statistical module 1306 is used to determine the single-difference observation pseudorange residual between satellites based on the satellite observation pseudorange and the terminal positioning result, and to calculate the second type of statistical parameters about the single-difference observation pseudorange residual.

[0291] The quality assessment module 1308 is used to assess the positioning quality of the terminal positioning result in the current epoch based on the first type of statistics and the second type of statistical parameters.

[0292] In one embodiment, the apparatus further includes: a pseudorange observation equation construction module, used to calculate the transmission time of the satellite signal based on the observed pseudorange between the terminal and the satellite at the current epoch and the reception time of the satellite signal received by the terminal; query the real-time navigation ephemeris of the satellite based on the transmission time to obtain the satellite position and satellite clock bias corresponding to the satellite at the current epoch; construct a pseudorange observation equation between the terminal and the satellite based on the observed pseudorange, satellite position, satellite clock bias of each satellite, as well as the estimated terminal position and estimated terminal clock bias; and solve the pseudorange observation equation based on the satellite observation data to obtain the terminal positioning result.

[0293] In one embodiment, the first statistical module 1304 includes a positioning solution unit, which is used to calculate the estimated distance between the terminal and the satellite in the multiple iterations of the least squares solution of the pseudorange observation equation based on satellite observation data, according to the satellite position corresponding to the current epoch satellite, the satellite clock error, the terminal position estimate obtained in the previous iteration, and the terminal clock error estimate; calculate the residual between the observed pseudorange of each satellite and the estimated distance, perform gross error elimination on the residual based on quartiles, select the target observed pseudorange from the observed pseudoranges of each satellite, and perform the least squares solution of the pseudorange observation equation formed by the target observed pseudorange in the current iteration.

[0294] In one embodiment, the satellite observation data further includes the signal-to-noise ratio of the observed pseudorange. The first statistical module 1304 includes a positioning solution unit, used to: obtain the estimated parameters of the previous iteration during the current iteration of the least squares solution, the estimated parameters including the estimated position and the estimated clock error; obtain the target satellites selected after the previous iteration; calculate the observed pseudorange variance of the target satellites based on the signal-to-noise ratio of the observed pseudorange of the target satellites and the satellite elevation angle determined based on the satellite position corresponding to the target satellite and the terminal estimated position of the previous iteration; construct the observed pseudorange variance matrix based on the observed pseudorange variance of each target satellite; determine the pseudorange observation equation for the current iteration based on the satellite observation data of the target satellites; obtain the differential matrix of the pseudorange observation equation for the current iteration with respect to the parameters to be estimated, the parameters to be estimated including the terminal position to be estimated and the terminal clock error to be estimated; determine the estimated parameter correction amount for the current iteration based on the differential matrix, the observed pseudorange variance matrix, and the pseudorange observation residual of the target satellites; and correct the estimated parameters of the previous iteration based on the estimated parameter correction amount for the current iteration to obtain the estimated parameters for the current iteration.

[0295] In one embodiment, the positioning calculation unit is further configured to continue the iteration process when the estimated parameter correction amount of the current iteration is greater than a first preset threshold; when the estimated parameter correction amount of the current iteration is less than the first preset threshold, based on the estimated parameters of the current iteration, determine the post-observation pseudorange residual sequence and the post-observation pseudorange variance matrix of the current iteration, and calculate the chi-square test statistic based on the post-observation pseudorange variance matrix and the post-observation pseudorange residual sequence; when the chi-square test statistic is less than a second preset threshold, the iteration stops, and the positioning result of the terminal is obtained based on the estimated parameters of the current iteration; when the chi-square test statistic is greater than the second preset threshold, perform a normality test on the post-observation pseudorange residual sequence based on the post-observation pseudorange residual covariance matrix of the current iteration, remove the observed pseudoranges that fail the normality test, and continue the iteration process using the observed pseudoranges of the selected target satellite.

[0296] In one embodiment, the first statistical module 1304 includes a residual parameter statistical unit, used to calculate the positioning distance between the terminal and the satellite based on the satellite position and satellite clock error corresponding to the current epoch satellite, as well as the calculated terminal position and terminal clock error; calculate the residual between the observation pseudorange of each satellite and the corresponding positioning distance to obtain the observation pseudorange residual sequence; and calculate the root mean square and absolute median of the observation pseudorange residual sequence.

[0297] In one embodiment, the residual parameter statistics unit is further configured to calculate the observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observed pseudorange and the elevation angle of the satellite determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position; construct an observation pseudorange variance matrix based on the observation pseudorange variance of each satellite; calculate the unit weighted mean square error of the observation pseudorange residual sequence based on the observation pseudorange residual sequence and the observation pseudorange variance matrix; and output the observation pseudorange residual sequence, as well as the unit weighted mean square error, root mean square, and absolute median of the observation pseudorange residuals.

[0298] In one embodiment, the second statistics module 1306 includes a single-difference residual construction unit, used to determine a reference satellite from the observed satellites of the current epoch terminal; for each non-reference satellite among the observed satellites, calculate the single observation difference between the observed pseudorange of the non-reference satellite and the observed pseudorange of the reference satellite, calculate the single positioning difference between the positioning distance corresponding to the non-reference satellite and the positioning distance corresponding to the reference satellite; and determine the single-difference observed pseudorange residual sequence based on the residual between the single observation difference and the positioning single difference corresponding to each non-reference satellite.

[0299] In one embodiment, the second statistical module 1306 includes a single-difference residual parameter statistical unit, used to calculate the root mean square and absolute median of the single-difference observation pseudorange residual sequence based on the single-difference observation pseudorange residual sequence formed by the single-difference observation pseudorange residuals of each non-reference satellite; calculate the posterior observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange, the satellite elevation angle determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position, and the unit weighted mean error of the observation pseudorange residual in the first type of statistical parameters; construct the posterior observation pseudorange variance matrix based on the posterior observation pseudorange variance of each satellite; calculate the unit weighted mean error of the single-difference observation pseudorange residual sequence based on the posterior observation pseudorange variance matrix and the single-difference observation pseudorange residual sequence; and output the unit weighted mean error, root mean square, and absolute median of the single-difference observation pseudorange residual.

[0300] In one embodiment, the first type of statistical parameters includes the unit weighted mean square error of the observed pseudorange residual; the second type of statistical parameters includes the unit weighted mean square error of the single-difference observed pseudorange residual; the device further includes a terminal scene determination module, used to determine the scene in which the terminal is located based on the unit weighted mean square error of the observed pseudorange residual and the unit weighted mean square error of the single-difference observed pseudorange residual; the quality assessment module is further used to assess the positioning quality of the terminal positioning result in the current epoch based on the scene in which the terminal is located.

[0301] In one embodiment, the terminal scene determination module is further configured to calculate the posterior parameter covariance matrix of the current epoch and the posterior parameter covariance matrix of the previous epoch; calculate the relative change in posterior variance based on the posterior parameter covariance matrices of adjacent epochs; and determine the scene in which the terminal is located based on the relative change in posterior variance, the unit weighted mean square error of the observed pseudorange residual, and the unit weighted mean square error of the single-difference observed pseudorange residual.

[0302] In one embodiment, the first type of statistical parameters includes the unit weighted mean square error of the observation pseudorange residuals; the terminal scene decision module is further configured to calculate the observation pseudorange variance of the satellites based on the signal-to-noise ratio of the observation pseudorange at the current epoch and the elevation angle of the satellites determined based on the satellite positions corresponding to the satellites at the current epoch and the calculated terminal positions; construct the observation pseudorange variance matrix based on the observation pseudorange variances of each satellite; obtain the differential matrix of the pseudorange observation equation for the parameters to be estimated, the parameters to be estimated including the terminal positions to be estimated and the terminal clock errors to be estimated; and calculate the posterior parameter covariance matrix for the current epoch based on the observation pseudorange variance matrix, the differential matrix, and the unit weighted mean square error of the observation pseudorange residuals at the current epoch.

[0303] In one embodiment, the first type of statistical parameters includes the unit weighted mean error, root mean square, and absolute median of the observed pseudorange residuals; the second type of statistical parameters includes the unit weighted mean error, root mean square, and absolute median of the single-difference observed pseudorange residuals.

[0304] The quality assessment module is also used to statistically analyze the relative changes in the unit weighted mean square error, root mean square error, and absolute median error of the observed pseudorange residuals between the current epoch and the previous epoch; to statistically analyze the relative changes in the unit weighted mean square error, root mean square error, and absolute median error of the observed pseudorange residuals between the current epoch and the previous epoch; and to evaluate the positioning quality of the terminal positioning result in the current epoch based on the statistically analyzed relative changes between the first type of statistical parameters and the second type of statistical parameters corresponding to the current epoch and the previous epoch.

[0305] In one embodiment, the quality assessment module 1308 is further configured to determine the maximum value among the relative changes in post-hoc variance, the relative changes in the unit weighted mean square error of the observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median, the relative changes in the unit weighted mean square error of the single-difference observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median; compare the determined maximum value with the threshold corresponding to the smoothness of each level of positioning trajectory to determine the smoothness of the positioning trajectory corresponding to the current epoch; and evaluate the positioning quality of the terminal positioning result at the current epoch based on the smoothness of the positioning trajectory.

[0306] In one embodiment, the satellite observation data also includes the signal-to-noise ratio of the observed pseudorange. The positioning quality assessment device 1300 further includes: a signal-to-noise ratio information statistics module, used to obtain the signal-to-noise ratio of the observed pseudorange corresponding to each satellite for which the terminal receives the satellite transmission signal from the satellite observation data; and output statistical information about the signal-to-noise ratio based on the maximum value, minimum value, standard deviation and absolute median difference of the signal-to-noise ratio of the observed pseudorange of each satellite.

[0307] The quality assessment module 1308 is also used to assess the positioning quality of the current epoch terminal positioning results based on statistical information about the signal-to-noise ratio.

[0308] The aforementioned positioning quality assessment device 1300 acquires satellite observation data for the current epoch of the terminal, calculates the terminal positioning result based on the observation pseudorange in the satellite observation data, and then determines the observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, calculating a first type of statistical parameter regarding the observation pseudorange residual. Furthermore, based on the satellite observation pseudorange and the terminal positioning result obtained above, it determines the single-difference observation pseudorange residual, and then calculates a second type of statistical parameter regarding the single-difference observation pseudorange residual. Thus, by combining the first type of statistical parameter and the second type of statistical parameter as a positioning quality assessment index, the positioning quality of the terminal positioning result for the current epoch can be accurately assessed, thereby improving the accuracy of the terminal positioning result.

[0309] Each module in the aforementioned positioning quality assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0310] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 14 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a positioning quality assessment method. The display unit of the computer device is used to form a visually visible image. It can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0311] Those skilled in the art will understand that Figure 14The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0312] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the positioning quality assessment method provided in any one or more of the above embodiments.

[0313] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the positioning quality assessment method provided in any one or more of the above embodiments.

[0314] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the positioning quality assessment method provided in any one or more of the above embodiments. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0315] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0316] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0317] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing positioning quality, characterized in that, The method includes: Obtain satellite observation data for the current epoch of the terminal, the satellite observation data including the observation pseudorange between the terminal and the satellite; obtain the terminal positioning result calculated based on the satellite observation data; Based on the satellite's observed pseudorange and the terminal's positioning results, the satellite's observed pseudorange residual is determined, and a first type of statistical parameter is calculated regarding the observed pseudorange residual. The first type of statistical parameter includes the unit weighted mean square error of the observed pseudorange residual. Based on the satellite observation pseudorange and the terminal positioning result, the single-difference observation pseudorange residual between the satellites is determined, and a second type of statistical parameter is calculated for the single-difference observation pseudorange residual. The second type of statistical parameter includes the unit weighted mean square error of the single-difference observation pseudorange residual. Based on the first type of statistical parameters and the second type of statistical parameters, the scene in which the terminal is located is determined, and the positioning quality of the terminal positioning result in the current epoch is evaluated based on the scene in which the terminal is located.

2. The method according to claim 1, characterized in that, The step of obtaining the terminal positioning result calculated based on the satellite observation data includes: The transmission time of the satellite signal is calculated based on the observation pseudorange between the current epoch terminal and the satellite, and the reception time of the satellite signal received by the terminal. Based on the launch time, query the satellite's real-time navigation ephemeris to obtain the satellite position and satellite clock difference corresponding to the current epoch. Based on the pseudorange of each satellite, the satellite position, the satellite clock error, the position of the terminal to be estimated, and the clock error of the terminal to be estimated, the pseudorange observation equation between the terminal and the satellite is constructed. The pseudorange observation equation is solved based on the satellite observation data to obtain the terminal positioning result.

3. The method according to claim 2, characterized in that, The method further includes: During the multiple iterations of least-squares solution of the pseudorange observation equation based on the satellite observation data, the estimated distance between the terminal and the satellite is calculated according to the satellite position corresponding to the current epoch, the satellite clock error, the terminal position estimate obtained in the previous iteration, and the terminal clock error estimate. The residuals between the observed pseudoranges and estimated distances of each satellite are calculated. Gross errors are removed from the residuals based on quartiles. The target observed pseudorange is selected from the observed pseudoranges of each satellite. The pseudorange observation equation formed by the target observed pseudorange is used to perform the least squares solution for the current iteration.

4. The method according to claim 2, characterized in that, The satellite observation data also includes the signal-to-noise ratio of the observed pseudorange, and the method further includes: In the current iteration of the least squares solution, the estimated parameters of the previous iteration are obtained, including the estimated position and the estimated clock error; the target satellites selected after the previous iteration are obtained. Based on the signal-to-noise ratio of the observed pseudorange of the target satellite and the elevation angle of the satellite determined by the satellite position corresponding to the target satellite and the terminal estimated position of the previous iteration, the observed pseudorange variance of the target satellite is calculated; based on the observed pseudorange variance of each target satellite, an observed pseudorange variance matrix is ​​constructed. Based on the satellite observation data of the target satellite, determine the pseudorange observation equation for the current iteration; Obtain the differential matrix of the pseudorange observation equation for the current iteration with respect to the parameters to be estimated, the parameters to be estimated include the terminal position to be estimated and the terminal clock error to be estimated; determine the correction amount of the estimated parameters for the current iteration based on the differential matrix, the observation pseudorange variance matrix and the pseudorange observation residual of the target satellite for the current iteration. The estimated parameters of the previous iteration are corrected based on the correction amount of the estimated parameters of the current iteration to obtain the estimated parameters of the current iteration.

5. The method according to claim 4, characterized in that, The method further includes: When the correction amount of the estimated parameters in the current iteration is greater than the first preset threshold, the iteration process continues; When the correction amount of the estimated parameters in the current iteration is less than the first preset threshold, based on the estimated parameters in the current iteration, the posterior observation pseudorange residual sequence and the posterior observation pseudorange variance matrix of the current iteration are determined, and the chi-square test statistic is calculated based on the posterior observation pseudorange variance matrix and the posterior observation pseudorange residual sequence. When the chi-square test statistic is less than the second preset threshold, the iteration stops, and the terminal's positioning result is obtained based on the estimated parameters of the current iteration. When the chi-square test statistic is greater than the second preset threshold, the post-observation pseudorange residual sequence is subjected to a normal distribution test based on the covariance matrix of the post-observation pseudorange residuals of the current iteration. After removing the observation pseudoranges that fail the normal distribution test, the iterative process continues using the observation pseudoranges of the selected target satellite.

6. The method according to claim 1, characterized in that, The step of determining the satellite's observed pseudorange residual based on the satellite's observed pseudorange and the terminal positioning result, and calculating a first type of statistical parameter regarding the observed pseudorange residual, includes: Based on the satellite position and clock bias corresponding to the current epoch satellite, as well as the calculated terminal position and clock bias, the positioning distance between the terminal and the satellite is calculated. Calculate the residuals between the observation pseudoranges of each satellite and the corresponding positioning distances to obtain the observation pseudorange residual sequence; Calculate the root mean square and absolute median of the observed pseudorange residual sequence.

7. The method according to claim 6, characterized in that, The satellite observation data also includes the signal-to-noise ratio of the observed pseudorange, and the method further includes: Based on the signal-to-noise ratio of the observed pseudorange and the elevation angle of the satellite determined by the satellite position corresponding to the current epoch satellite and the calculated terminal position, the observed pseudorange variance of the satellite is calculated; based on the observed pseudorange variance of each satellite, an observed pseudorange variance matrix is ​​constructed. Calculate the unit weighted mean square error of the observed pseudorange residual sequence based on the observed pseudorange residual sequence and the observed pseudorange variance matrix; Output the observed pseudorange residual sequence, as well as the unit weighted mean square error, root mean square, and absolute median of the observed pseudorange residual.

8. The method according to claim 1, characterized in that, The step of determining the single-difference observation pseudorange residual between satellites based on the satellite's observed pseudorange and the terminal positioning result includes: Determine the reference satellite from the observation satellites of the terminal described in the current epoch; For each non-reference satellite among the observed satellites, calculate the observation pseudorange difference between the non-reference satellite and the observation pseudorange of the reference satellite, and calculate the positioning pseudorange difference between the positioning distance of the non-reference satellite and the positioning distance of the reference satellite. Based on the residuals between the observation single difference corresponding to each non-reference satellite and the positioning single difference, the single difference observation pseudorange residual sequence is determined.

9. The method according to claim 8, characterized in that, The calculation of the second type of statistical parameters with respect to the pseudorange residuals of the single-difference observations includes: Based on the single-difference observation pseudorange residual sequence formed by the single-difference observation pseudorange residuals of each non-reference satellite, calculate the root mean square and absolute median of the single-difference observation pseudorange residual sequence. Based on the signal-to-noise ratio of the observed pseudorange, the satellite elevation angle determined by the satellite position corresponding to the current epoch satellite and the calculated terminal position, and the unit weighted mean square error of the observed pseudorange residual in the first type of statistical parameters, the posterior observed pseudorange variance of the satellite is calculated; based on the posterior observed pseudorange variance of each satellite, a posterior observed pseudorange variance matrix is ​​constructed; based on the posterior observed pseudorange variance matrix and the single-difference observed pseudorange residual sequence, the unit weighted mean square error of the single-difference observed pseudorange residual sequence is calculated; Output the unit weighted mean square error, root mean square, and absolute median of the single-difference observation pseudorange residual.

10. The method according to claim 1, characterized in that, Determining the scenario of the terminal based on the first type of statistical parameters and the second type of statistical parameters includes: Calculate the posterior parameter covariance matrix of the current epoch and the posterior parameter covariance matrix of the previous epoch; The relative change in posterior variance is calculated based on the covariance matrix of the posterior parameters in adjacent epochs. The scene in which the terminal is located is determined based on the relative change of the post-hoc variance, the unit weighted mean square error of the observed pseudorange residual, and the unit weighted mean square error of the single-difference observed pseudorange residual.

11. The method according to claim 10, characterized in that, The calculation of the posterior parameter covariance matrix for the current epoch includes: Based on the signal-to-noise ratio of the observed pseudorange at the current epoch and the elevation angle of the satellite determined by the satellite position corresponding to the satellite at the current epoch and the terminal position obtained by the solution, the observed pseudorange variance of the satellite is calculated; based on the observed pseudorange variance of each satellite, the observed pseudorange variance matrix is ​​constructed. Obtain the differential matrix of the pseudorange observation equation with respect to the parameters to be estimated, the parameters to be estimated including the terminal position and the terminal clock error to be estimated; The posterior parameter covariance matrix of the current epoch is calculated based on the observed pseudorange variance matrix, the differential matrix, and the unit weighted mean square error of the observed pseudorange residual at the current epoch.

12. The method according to claim 1, characterized in that, The first type of statistical parameters also includes the root mean square and absolute median of the observed pseudorange residuals; the second type of statistical parameters includes the root mean square and absolute median of the single-difference observed pseudorange residuals. The method further includes: The relative changes in unit weighted mean error, root mean square, and absolute median of the observed pseudorange residuals between the current epoch and the previous epoch are statistically analyzed. The relative changes in unit weighted mean error, root mean square, and absolute median of the pseudorange residuals of the single-difference observations between the current epoch and the previous epoch are statistically analyzed. The positioning quality of the terminal positioning result in the current epoch is evaluated based on the relative changes between the first type of statistical parameters corresponding to the current epoch and the previous epoch, and the relative changes between the second type of statistical parameters corresponding to each epoch.

13. The method according to claim 12, characterized in that, The step of evaluating the positioning quality of the terminal positioning result in the current epoch based on the relative changes between the first type of statistical parameters corresponding to the current epoch and the previous epoch, and the relative changes between the second type of statistical parameters corresponding to each epoch, includes: Determine the maximum value among the relative changes in posterior variance, the relative changes in the unit weighted mean square error of the observed pseudorange residuals, the relative changes in the root mean square, the relative changes in the absolute median, the relative changes in the unit weighted mean square error of the single-difference observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median. The determined maximum value is compared with the threshold corresponding to the smoothness of the positioning trajectory at each level to determine the smoothness of the positioning trajectory corresponding to the current epoch. The positioning quality of the terminal positioning result in the current epoch is evaluated based on the smoothness of the positioning trajectory.

14. The method according to any one of claims 1 to 13, characterized in that, The satellite observation data also includes the signal-to-noise ratio of the observed pseudorange, and the method further includes: The signal-to-noise ratio of the observation pseudorange corresponding to each satellite whose satellite transmission signal was received by the terminal is obtained from the satellite observation data. Based on the maximum, minimum, standard deviation, and absolute median of the signal-to-noise ratio of the pseudoranges observed by each satellite, output statistical information about the signal-to-noise ratio. The positioning quality of the terminal positioning result in the current epoch is evaluated based on the statistical information regarding the signal-to-noise ratio.

15. A positioning quality assessment device, characterized in that, The device includes: The acquisition module is used to acquire satellite observation data of the terminal at the current epoch, the satellite observation data including the observation pseudorange between the terminal and the satellite; and to acquire the terminal positioning result calculated based on the satellite observation data. The first statistical module is used to determine the satellite observation pseudorange residual based on the satellite observation pseudorange and the terminal positioning result, and to calculate a first type of statistical parameters about the observation pseudorange residual. The first type of statistical parameters includes the unit weighted mean square error about the observation pseudorange residual. The second statistical module is used to determine the single-difference observation pseudorange residual between satellites based on the satellite observation pseudorange and the terminal positioning result, and to calculate a second type of statistical parameters for the single-difference observation pseudorange residual. The second type of statistical parameters includes the unit weighted mean square error of the single-difference observation pseudorange residual. The quality assessment module is used to determine the scene in which the terminal is located based on the first type of statistical parameters and the second type of statistical parameters, and to assess the positioning quality of the terminal positioning result in the current epoch based on the scene in which the terminal is located.

16. The apparatus according to claim 15, characterized in that, The device further includes: The pseudorange observation equation construction module is used to calculate the transmission time of the satellite signal based on the observed pseudorange between the terminal and the satellite at the current epoch and the reception time of the satellite signal received by the terminal; query the real-time navigation ephemeris of the satellite based on the transmission time to obtain the satellite position and satellite clock error corresponding to the satellite at the current epoch; construct the pseudorange observation equation between the terminal and the satellite based on the observed pseudorange, satellite position, satellite clock error of each satellite, as well as the estimated terminal position and estimated terminal clock error; and solve the pseudorange observation equation based on the satellite observation data to obtain the terminal positioning result.

17. The apparatus according to claim 16, characterized in that, The first statistics module includes: The positioning and calculation unit is used to calculate the estimated distance between the terminal and the satellite during multiple iterations of least-squares solution of the pseudorange observation equation based on the satellite observation data. This is done by considering the satellite position corresponding to the current epoch satellite, the satellite clock error, the estimated terminal position value obtained in the previous iteration, and the estimated terminal clock error value. The unit also calculates the residual between the observed pseudorange of each satellite and the estimated distance, performs outlier elimination on the residual based on quartiles, selects the target observed pseudorange from the observed pseudoranges of each satellite, and performs least-squares solution of the pseudorange observation equation formed by the target observed pseudorange in the current iteration.

18. The apparatus according to claim 16, characterized in that, The satellite observation data also includes the signal-to-noise ratio of the observed pseudorange, and the first statistical module includes: The positioning and solving unit is used to, during the current iteration of the least squares solution, obtain the estimated parameters from the previous iteration, including the estimated position and estimated clock error; obtain the target satellites selected after the previous iteration; calculate the observation pseudorange variance of the target satellites based on the signal-to-noise ratio of the observed pseudorange of the target satellites and the satellite elevation angle determined based on the satellite position corresponding to the target satellite and the terminal estimated position of the previous iteration; construct an observation pseudorange variance matrix based on the observation pseudorange variance of each target satellite; determine the pseudorange observation equation for the current iteration based on the satellite observation data of the target satellites; obtain the differential matrix of the pseudorange observation equation for the current iteration with respect to the parameters to be estimated, including the terminal position to be estimated and the terminal clock error to be estimated; determine the estimation parameter correction amount for the current iteration based on the differential matrix, the observation pseudorange variance matrix, and the pseudorange observation residual of the target satellites; and correct the estimated parameters of the previous iteration based on the estimation parameter correction amount for the current iteration to obtain the estimated parameters for the current iteration.

19. The apparatus according to claim 18, characterized in that, The positioning and solving unit is also used to continue the iteration process when the estimated parameter correction amount of the current iteration is greater than the first preset threshold; when the estimated parameter correction amount of the current iteration is less than the first preset threshold, based on the estimated parameters of the current iteration, determine the post-observation pseudorange residual sequence and the post-observation pseudorange variance matrix of the current iteration, and calculate the chi-square test statistic based on the post-observation pseudorange variance matrix and the post-observation pseudorange residual sequence. When the chi-square test statistic is less than the second preset threshold, the iteration stops, and the positioning result of the terminal is obtained based on the estimated parameters of the current iteration. When the chi-square test statistic is greater than the second preset threshold, the post-observation pseudorange residual sequence is subjected to a normal distribution test based on the covariance matrix of the post-observation pseudorange residuals of the current iteration. After removing the observation pseudoranges that fail the normal distribution test, the iteration process continues using the observation pseudoranges of the selected target satellite.

20. The apparatus according to claim 15, characterized in that, The first statistics module includes: The residual parameter statistics unit is used to calculate the positioning distance between the terminal and the satellite based on the satellite position and satellite clock error corresponding to the current epoch satellite, as well as the terminal position and terminal clock error obtained by the solution; calculate the residual between the observation pseudorange of each satellite and the corresponding positioning distance to obtain the observation pseudorange residual sequence; and calculate the root mean square and absolute median of the observation pseudorange residual sequence.

21. The apparatus according to claim 20, characterized in that, The satellite observation data also includes the signal-to-noise ratio of the observation pseudorange. The residual parameter statistics unit is also used to calculate the observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange and the satellite elevation angle determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position; and to construct the observation pseudorange variance matrix based on the observation pseudorange variance of each satellite. Based on the observed pseudorange residual sequence and the observed pseudorange variance matrix, calculate the unit weighted mean square error of the observed pseudorange residual sequence; output the observed pseudorange residual sequence, as well as the unit weighted mean square error, root mean square, and absolute median of the observed pseudorange residuals.

22. The apparatus according to claim 15, characterized in that, The second statistics module includes: A single-difference residual construction unit is used to determine a reference satellite from the observation satellites of the terminal at the current epoch; for each non-reference satellite among the observation satellites, the unit calculates the single observation difference between the observation pseudorange of the non-reference satellite and the observation pseudorange of the reference satellite, and calculates the single positioning difference between the positioning distance corresponding to the non-reference satellite and the positioning distance corresponding to the reference satellite; and determines the single-difference observation pseudorange residual sequence based on the residual between the observation single difference corresponding to each non-reference satellite and the positioning single difference.

23. The apparatus according to claim 22, characterized in that, The second statistics module includes: The single-difference residual parameter statistical unit is used to calculate the root mean square and absolute median of the single-difference observation pseudorange residual sequence formed by the single-difference observation pseudorange residuals of each non-reference satellite; calculate the posterior observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange, the satellite elevation angle determined based on the satellite position corresponding to the current epoch satellite and the calculated terminal position, and the unit weighted mean error of the observation pseudorange residual in the first type of statistical parameters; construct the posterior observation pseudorange variance matrix based on the posterior observation pseudorange variance matrix of each satellite; calculate the unit weighted mean error of the single-difference observation pseudorange residual sequence based on the posterior observation pseudorange variance matrix and the single-difference observation pseudorange residual sequence; and output the unit weighted mean square, root mean square, and absolute median of the single-difference observation pseudorange residual.

24. The apparatus according to claim 15, characterized in that, The device further includes: The terminal scene determination module is used to calculate the posterior parameter covariance matrix of the current epoch and the posterior parameter covariance matrix of the previous epoch; calculate the relative change in posterior variance based on the posterior parameter covariance matrices of adjacent epochs; and determine the scene in which the terminal is located based on the relative change in posterior variance, the unit weighted mean square error of the observed pseudorange residual, and the unit weighted mean square error of the single-difference observed pseudorange residual.

25. The apparatus according to claim 24, characterized in that, The terminal scene decision module is further configured to calculate the observation pseudorange variance of the satellite based on the signal-to-noise ratio of the observation pseudorange at the current epoch and the satellite elevation angle determined by the satellite position corresponding to the satellite at the current epoch and the terminal position obtained by the solution; construct the observation pseudorange variance matrix based on the observation pseudorange variance of each satellite; and obtain the differential matrix of the pseudorange observation equation for the parameters to be estimated, wherein the parameters to be estimated include the terminal position to be estimated and the terminal clock error to be estimated. The posterior parameter covariance matrix of the current epoch is calculated based on the observed pseudorange variance matrix, the differential matrix, and the unit weighted mean square error of the observed pseudorange residual at the current epoch.

26. The apparatus according to claim 15, characterized in that, The first type of statistical parameters also includes the root mean square and absolute median of the observed pseudorange residuals; the second type of statistical parameters includes the root mean square and absolute median of the single-difference observed pseudorange residuals. The quality assessment module is further configured to: statistically analyze the relative changes in the unit weighted mean square error, the root mean square, and the absolute median of the observed pseudorange residuals between the current epoch and the previous epoch; statistically analyze the relative changes in the unit weighted mean square error, the root mean square, and the absolute median of the observed pseudorange residuals between the current epoch and the previous epoch; and evaluate the positioning quality of the terminal positioning result in the current epoch based on the statistically analyzed relative changes between the first type of statistical parameters and the second type of statistical parameters corresponding to the current epoch and the previous epoch.

27. The apparatus according to claim 26, characterized in that, The quality assessment module is further configured to determine the maximum value among the relative changes in post-hoc variance, the relative changes in the unit weighted mean square error of the observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median, the relative changes in the unit weighted mean square error of the single-difference observed pseudorange residuals, the relative changes in the root mean square, and the relative changes in the absolute median; compare the determined maximum value with the threshold corresponding to the smoothness of each level of positioning trajectory to determine the smoothness of the positioning trajectory corresponding to the current epoch; and evaluate the positioning quality of the terminal positioning result in the current epoch based on the smoothness of the positioning trajectory.

28. The apparatus according to any one of claims 15 to 27, characterized in that, The satellite observation data also includes the signal-to-noise ratio of the observation pseudorange, and the device further includes: The signal-to-noise ratio (SNR) information statistics module is used to obtain the SNR of the observed pseudoranges of each satellite for which the terminal receives satellite transmission signals from the satellite observation data; and output statistical information about the SNR based on the maximum value, minimum value, standard deviation, and absolute median difference of the SNR of each satellite's observed pseudoranges. The quality assessment module is also used to assess the positioning quality of the terminal positioning result in the current epoch based on statistical information about the signal-to-noise ratio.

29. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 14.

30. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 14.

31. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 14.

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