A navigation-type GNSS receiver real-time kinematic test method

Abstract

The present application relates to the field of satellite navigation technology, specifically to a navigation type GNSS receiver real-time dynamic test method, comprising the following steps: reconstructing the positioning point trajectory by using GNSS receiver positioning data, comparing the actual trajectory to calculate the error, analyzing the influence of motion state parameters on error change, combining environmental data to analyze the influence of environmental factors on receiver performance, evaluating data based on error fluctuation and stability, predicting the performance change trend of the receiver, and obtaining the receiver performance evaluation result. In the present application, the positioning trajectory of the receiver is reconstructed by obtaining the positioning data of the GNSS receiver, the error of each data point is accurately calculated, the influence of motion state parameters on error change is analyzed, the performance of the receiver in various dynamic environments is accurately identified, the environmental data and motion state data are fused, the performance change trend of the receiver under various environmental conditions is predicted, the accuracy performance data is combined, and the performance of the receiver is accurately predicted and evaluated.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and in particular to a real-time dynamic testing method for navigation GNSS receivers. Background Technology

[0002] The field of satellite navigation technology encompasses technologies for positioning, navigation, and timing using Global Navigation Satellite Systems (GNSS). Its core components involve GNSS signal reception, processing, and analysis, focusing on solving problems such as accurate satellite signal tracking, optimization of positioning algorithms, correction of error sources, and improvement of system performance. Satellite navigation technology is widely used in aviation, aerospace, automotive, military, surveying, and transportation, providing high-precision positioning and navigation services. With the diversification of navigation needs and the increasing complexity of application scenarios, satellite navigation systems need to continuously adapt to changes in different environments and dynamic conditions. This places higher demands on the performance of GNSS receivers, especially their stability and accuracy in complex and dynamic environments.

[0003] One of the methods is a real-time dynamic testing method for navigation GNSS receivers, designed to evaluate the performance of navigation GNSS receivers in motion. This method aims to address the challenge of testing key performance aspects of GNSS receivers, such as positioning accuracy, tracking capability, and data latency, in real-time dynamic environments. The method constructs a high-precision trajectory reference system and combines it with a dynamic simulator or actual motion platform to acquire and compare the GNSS receiver's output data with the reference trajectory in real time. By employing inertial navigation system and GNSS data fusion, the method improves the accuracy and reliability of the test, comprehensively and accurately evaluating the dynamic performance of navigation GNSS receivers in complex environments.

[0004] Traditional real-time dynamic testing techniques for navigation GNSS receivers focus on calculating and analyzing static errors when evaluating GNSS receiver performance, neglecting the receiver's performance in real-world dynamic environments. They only consider simple error comparisons and lack a comprehensive assessment of the receiver under various motion states and environmental factors. This can lead to the receiver failing to accurately reflect its performance fluctuations and dynamic behavior in complex environments. Relying on laboratory environments for testing cannot effectively simulate interference sources and dynamic changes in real-world environments, resulting in test results that do not fully reflect the receiver's stability and reliability in practical applications. Static testing modes prevent GNSS receiver performance evaluation from meeting the needs of different application scenarios and fail to fully consider the impact of external environments and complex dynamic conditions on receiver performance, thus affecting the authenticity and operability of the test results. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a real-time dynamic testing method for navigation GNSS receivers. The technical solution is as follows:

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a real-time dynamic testing method for a navigation GNSS receiver, comprising the following steps:

[0007] S1: Acquire GNSS receiver positioning data, including longitude, latitude, velocity and acceleration; collect external environmental data, including radio interference, weather changes and signal blockage; reconstruct the receiver's positioning point trajectory based on the time information of multiple data points; and output the reconstructed positioning point trajectory.

[0008] S2: Reconstruct the trajectory based on the positioning points, compare it point by point with the actual motion trajectory of the receiver, calculate the deviation of position, velocity and acceleration of each data point, evaluate the degree of error of each data point, analyze the accuracy performance of the receiver, and obtain the error dataset;

[0009] S3: Based on the error dataset, the influence of various motion state parameters on error changes is calculated, including acceleration, velocity, and turning angle. The receiver's performance under various motion states is analyzed, the error change trend under each motion state is evaluated, and motion state influence data is generated.

[0010] S4: Call the error dataset and motion state impact data, call the environmental data, analyze the positioning error performance under various external environmental conditions, analyze the impact of environmental factors on receiver performance, predict the performance of the receiver under various environmental conditions, and generate environmental impact analysis data.

[0011] As a further aspect of the present invention, the reconstructed trajectory of the positioning point specifically includes reconstructing the positioning point trajectory, external environmental information, and data point timestamps. The error dataset includes position error calculation results, velocity error analysis results, and receiver accuracy scores. The motion state influence data includes acceleration influence data, velocity influence data, and turning angle influence data. The environmental influence analysis data includes weather influence data, signal obstruction influence data, and electromagnetic interference influence data.

[0012] As a further aspect of the present invention, the steps of acquiring GNSS receiver positioning data, including longitude, latitude, velocity, and acceleration, collecting external environmental data, including radio interference, weather changes, and signal obstruction, reconstructing the receiver's positioning point trajectory based on the time information of multiple data points, and outputting the reconstructed positioning point trajectory are as follows:

[0013] S101: Acquire GNSS receiver positioning data, including longitude, latitude, velocity and acceleration, and simultaneously collect external environmental data, including radio interference, weather changes and signal blockage, to generate the raw positioning dataset;

[0014] S102: Based on the original positioning dataset, the positioning data and external environment data are matched according to the timestamp to generate a time synchronization dataset;

[0015] S103: Using the time synchronization dataset, based on the receiver's positioning data and external environment data, reconstruct the receiver's positioning point trajectory and generate the reconstructed positioning point trajectory.

[0016] As a further aspect of the present invention, the steps of reconstructing the trajectory based on the positioning points, comparing it point-by-point with the actual motion trajectory of the receiver, calculating the deviation of the position, velocity, and acceleration of each data point, evaluating the degree of error at each data point, analyzing the accuracy performance of the receiver, and obtaining the error dataset are as follows:

[0017] S201: Reconstruct the trajectory based on the positioning points, obtain the actual motion trajectory of the receiver, and compare the difference between the reconstructed trajectory and the actual trajectory based on the time information, calculate the position deviation of each data point, and generate position deviation data;

[0018] S202: Based on the position deviation data, by comparing the actual velocity and acceleration at each time point, calculate the velocity and acceleration deviation of each data point to generate velocity and acceleration deviation data;

[0019] S203: Based on the velocity acceleration deviation data, and based on the error of each data point, evaluate the positioning accuracy of the receiver in each time period, analyze the accuracy performance of the receiver, and generate an error dataset.

[0020] As a further aspect of the present invention, the specific formula for evaluating the positioning accuracy of the receiver in each time period is as follows:

[0021] ;

[0022] Calculate the receiver positioning accuracy score;

[0023] in, Indicates the first Positioning accuracy score over a given time period This indicates the total number of data points within that time period. Indicates the first Within the time period, the first The actual positioning distance of each data point Indicates the first Within the time period, the first Reference positioning distance for each data point Indicates the first Within the time period, the first The velocity value of each data point Indicates the first Within the time period, the first The error of each data point affects the weight. Index representing a time period Indicates the first [number]th ... Index of data points.

[0024] As a further aspect of the present invention, based on the error dataset, the steps of calculating the influence of various motion state parameters on error changes, including acceleration, velocity, and turning angle, analyzing the receiver's performance under various motion states, evaluating the error change trend under each motion state, and generating motion state influence data are as follows:

[0025] S301: Based on the error dataset, call the motion state parameters of each positioning data point of the receiver, including acceleration, velocity and turning angle. Based on the motion state and error performance of each data point, analyze the influence of various motion state parameters on the change of receiver error and generate motion state parameter influence data.

[0026] S302: Based on the motion state parameter influence data, evaluate the error change trend of the receiver under various motion states, analyze the error change trend under each motion state, and generate change trend data;

[0027] S303: Based on the aforementioned trend data, analyze and predict the receiver's accuracy performance under various motion states, and generate motion state impact data.

[0028] As a further aspect of the present invention, the steps of calling the error dataset and motion state influence data, calling environmental data, analyzing the positioning error performance under various external environmental conditions, analyzing the impact of environmental factors on receiver performance, predicting the receiver performance under various environmental conditions, and generating environmental impact analysis data are as follows:

[0029] S401: Call the error dataset and motion state influence data, call external environmental data, including weather changes, radio interference, and signal blockage, calculate the changes in receiver positioning error under various environmental conditions, and generate error change data;

[0030] S402: Based on the error change data, according to the changes in receiver positioning error under various external environmental conditions, assess the impact of each environmental condition on receiver performance and generate environmental impact assessment data;

[0031] S403: Combining the aforementioned environmental impact assessment data, and based on the relationship between error data and environmental factors, predict the receiver's performance under various environmental conditions, and generate environmental impact analysis data.

[0032] As a further aspect of the present invention, the method further includes:

[0033] S5: Based on the motion state influence data and environmental influence analysis data, according to the error change rate and error fluctuation time characteristics, identify the fluctuation characteristics of the receiver error data, evaluate the stability of the receiver, and combine the accuracy performance data to analyze the performance of the receiver under various environmental and motion state changes, and obtain the receiver performance evaluation results.

[0034] The receiver performance evaluation results include the fluctuation characteristics of error data, stability evaluation results, and performance change trend prediction results.

[0035] As a further aspect of the present invention, based on the motion state influence data and environmental influence analysis data, and according to the time characteristics of error change rate and error fluctuation, the fluctuation characteristics of receiver error data are identified, the stability of the receiver is evaluated, and the receiver performance is analyzed under various environmental and motion state changes in combination with accuracy performance data to obtain the receiver performance evaluation results. The specific steps are as follows:

[0036] S501: Based on the motion state influence data and environmental influence analysis data, obtain the rate of change of error data and the time characteristics of error fluctuation, calculate the error fluctuation trend of each data point, identify the fluctuation characteristics of receiver error data by analyzing the fluctuation of error in each time period, and generate error fluctuation characteristic data.

[0037] S502: Call the error fluctuation characteristic data, evaluate the stability of the receiver based on the fluctuation characteristics of the error data, and obtain stability evaluation data;

[0038] S503: Using the stability assessment data and the receiver's accuracy performance data, calculate the receiver's performance score, predict the receiver's performance under various environmental and motion state changes, and generate the receiver performance assessment result.

[0039] As a further aspect of the present invention, the specific formula for calculating the performance score of the receiver is as follows:

[0040] ;

[0041] in, The receiver's performance score. The weighting coefficients for the stability score. The weighting coefficients for accuracy scoring. The weighting coefficients for the volatility score. The receiver's stability is rated. To score the accuracy of the receiver. To score the error fluctuation of the receiver, The rate of change of error, This is the error correction factor.

[0042] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0043] By acquiring positioning data from a GNSS receiver, the receiver's positioning trajectory can be reconstructed, the error of each data point can be accurately calculated, the impact of motion state parameters on error changes can be analyzed, and the receiver's performance in various dynamic environments can be accurately identified. The fusion of environmental data and motion state data can predict the performance change trend of the receiver under various environmental conditions, realizing dynamic monitoring of error fluctuations. Combined with accuracy performance data, the receiver's performance can be more accurately predicted and evaluated, and the receiver's stability in dynamic environments can be fed back in real time. The accuracy and stability of the receiver can be comprehensively and deeply evaluated, providing more detailed data support for performance optimization and improving test accuracy and reliability. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the workflow of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0047] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0048] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0049] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0050] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0051] Please see Figure 1 This invention provides a technical solution: a real-time dynamic testing method for a navigation GNSS receiver, comprising the following steps:

[0052] S1: Acquire GNSS receiver positioning data, including longitude, latitude, velocity and acceleration; collect external environmental data, including radio interference, weather changes and signal blockage; reconstruct the receiver's positioning point trajectory based on the time information of multiple data points; and output the reconstructed positioning point trajectory.

[0053] S2: Reconstruct the trajectory based on the positioning points, compare it point by point with the actual motion trajectory of the receiver, calculate the deviation of position, velocity and acceleration of each data point, evaluate the degree of error of each data point, analyze the accuracy performance of the receiver, and obtain the error dataset;

[0054] S3: Based on the error dataset, the receiver's performance under various motion states is analyzed by calculating the impact of multiple motion state parameters on error changes, including acceleration, velocity, and turning angle. The error change trend under each motion state is evaluated, and motion state impact data is generated.

[0055] S4: Call the error dataset and motion state impact data, call the environmental data, analyze the positioning error performance under various external environmental conditions, analyze the impact of environmental factors on receiver performance, predict the performance of the receiver under various environmental conditions, and generate environmental impact analysis data.

[0056] S5: Based on motion state impact data and environmental impact analysis data, and according to the error change rate and error fluctuation time characteristics, identify the fluctuation characteristics of receiver error data, evaluate the stability of the receiver, and combine with accuracy performance data to analyze the receiver's performance under various environmental and motion state changes, and obtain receiver performance evaluation results.

[0057] The positioning point reconstruction trajectory specifically includes reconstructing the positioning point trajectory, external environmental information, and data point timestamps. The error dataset includes position error calculation results, velocity error analysis results, and receiver accuracy score. Motion state impact data includes acceleration impact data, velocity impact data, and turning angle impact data. Environmental impact analysis data includes weather impact data, signal obstruction impact data, and electromagnetic interference impact data. Receiver performance evaluation results include the fluctuation characteristics of error data, stability evaluation results, and performance change trend prediction results.

[0058] The specific steps for acquiring GNSS receiver positioning data, including longitude, latitude, velocity, and acceleration, collecting external environmental data, including radio interference, weather changes, and signal obstruction, reconstructing the receiver's positioning trajectory based on the time information of multiple data points, and outputting the reconstructed positioning trajectory are as follows:

[0059] S101: Acquire GNSS receiver positioning data, including longitude, latitude, velocity and acceleration, and simultaneously collect external environmental data, including radio interference, weather changes and signal blockage, to generate the raw positioning dataset;

[0060] First, the system collects real-time positioning data from the receiver, including longitude, latitude, velocity, and acceleration, while simultaneously acquiring external environmental data such as radio interference, weather changes, and signal obstruction. These data points are timestamped, and the system synchronizes all types of data based on these timestamped values ​​to ensure that the data for each positioning point matches the environmental data at the corresponding time. The system analyzes each set of collected data to obtain a raw dataset synchronized with the time, including positioning information and environmental status information. For external environmental data, such as radio interference data, the system periodically records it using sensors or other devices and aligns it with the positioning data time, ensuring that the positioning information for each data point matches the environmental influencing factors, and outputs the raw positioning dataset.

[0061] S102: Based on the original location dataset, the location data and external environment data are matched according to the timestamp to generate a time synchronization dataset;

[0062] Next, the system uses the synchronized location dataset and external environmental data to perform time alignment based on the timestamp of each data point, generating a time-synchronized dataset. The key to this step is unifying the timestamps of different data sources, ensuring an accurate match between the timestamps of each location data point and the environmental data. For example, the receiver's location data updates every second, while environmental data, such as weather changes, might be recorded every 10 seconds. The system uses timestamp difference comparisons to align or interpolate incompletely matching data, ensuring that the location information and environmental information of each data point are matched to an accurate time. Formula:

[0063] ;

[0064] Calculate the time difference between location data and environmental data, where, For timestamps of environmental data, This refers to the timestamp of the location data. Assuming the timestamp of the environmental data is 12:00:03 and the timestamp of the location data is 12:00:00, calculate the time difference:

[0065] ;

[0066] Calculations show that there is a 3-second time difference between environmental data and location data. The system will correct this time difference to ensure that all data points are synchronized. The resulting time-synchronized dataset will provide an accurate data source for subsequent trajectory reconstruction.

[0067] S103: Using the time synchronization dataset, based on the receiver's positioning data and external environment data, reconstruct the receiver's positioning point trajectory and generate the reconstructed positioning point trajectory;

[0068] Utilizing a time-synchronized dataset, the system combines the receiver's positioning data with environmental data. Based on the data information at each time point, it calculates the receiver's positional changes at different time points using kinematic methods, thereby reconstructing the receiver's positioning trajectory. Specifically, the system uses forward and backward position data along with velocity, acceleration, and other information to calculate the receiver's trajectory within a specific time interval using time differences. For example, the system calculates the receiver's trajectory based on its velocity and acceleration data, combined with the corresponding time difference. Formula:

[0069] ;

[0070] Calculate the new position of the receiver, where, This is the initial position of the receiver. For the receiver speed, For the time difference, Let the acceleration be 1. Assume the receiver's initial position is 45.0°, its velocity is 10 m / s, its acceleration is 2 m / s², and the time difference is 1 second. Substitute these values ​​into the calculation:

[0071] ;

[0072] Calculation results show that the receiver's longitude increases from 45.0° to 45.011° within 1 second, and the reconstructed trajectory of the generated positioning points provides crucial data for subsequent error analysis. By continuously reconstructing the trajectories of all data points, the complete dynamic path of the receiver is finally obtained.

[0073] Based on the reconstructed trajectory from the positioning points, the deviation of position, velocity, and acceleration at each data point is calculated by comparing it point-by-point with the actual motion trajectory of the receiver. The degree of error at each data point is evaluated, and the accuracy performance of the receiver is analyzed. The specific steps for obtaining the error dataset are as follows:

[0074] S201: Reconstruct the trajectory based on the positioning points, obtain the actual motion trajectory of the receiver, and compare the difference between the reconstructed trajectory and the actual trajectory based on the time information, calculate the position deviation of each data point, and generate position deviation data;

[0075] First, the receiver's latitude, longitude, velocity, and acceleration data are extracted from the reconstructed trajectory based on the positioning points. Then, the receiver's actual motion trajectory is acquired, typically directly via GNSS signals. The system compares the two trajectories one by one according to their timestamps to ensure time synchronization and calculates the positional difference between the two trajectories at each time point. For each data point, the system obtains the positional deviation data by calculating its latitude and longitude deviation. For example, assuming at time point... The receiver reconstructs a trajectory with a longitude of 45.0°, while the actual trajectory has a longitude of 45.01°. Therefore, the position deviation is 0.01°, which the system records as the position error. Using the formula:

[0076] ;

[0077] in, For positional deviation, and The latitude and longitude of the actual trajectory. and To reconstruct the latitude and longitude of the trajectory. Assume at time point... The actual trajectory longitude is 45.01°, the reconstructed trajectory longitude is 45.0°, the actual trajectory latitude is 12.01°, and the reconstructed trajectory latitude is 12.0°. Substituting these values ​​into the calculation:

[0078] ;

[0079] The calculated deviation was 0.0141 km, and the generated position deviation data was used for subsequent accuracy analysis.

[0080] S202: Based on position deviation data, by comparing the actual velocity and acceleration at each time point, calculate the velocity and acceleration deviation of each data point to generate velocity-acceleration deviation data;

[0081] The system utilizes position deviation data and further calculates the velocity and acceleration deviation for each data point by comparing the actual velocity and acceleration data at each time point. First, the system extracts the actual velocity and acceleration data from the receiver, typically obtained through velocity calculations and accelerometers in the GNSS received signal. Next, the system calculates the deviation between the velocity and acceleration at each time point and the corresponding data point in the reconstructed trajectory. Assuming at time point... The receiver's actual speed is 10 m / s, and the reconstructed trajectory speed is 9.8 m / s, therefore the speed deviation is 0.2 m / s. Using the formula:

[0082] ;

[0083] in, For speed deviation, For actual speed, Let be the velocity of the reconstructed trajectory. Assuming the actual velocity is 10 m / s and the reconstructed velocity is 9.8 m / s, substitute these values ​​into the calculation:

[0084] ;

[0085] Similarly, using acceleration data, the acceleration deviation is calculated by comparing the difference between the actual acceleration and the reconstructed acceleration. Through the above process, the system generates velocity-acceleration deviation data, which is used for subsequent accuracy evaluation.

[0086] S203: Based on the velocity acceleration deviation data and the error of each data point, evaluate the positioning accuracy of the receiver in each time period, analyze the accuracy performance of the receiver, and generate an error dataset;

[0087] The specific formula for evaluating the positioning accuracy of the receiver in each time period is as follows:

[0088] ;

[0089] Calculate the receiver positioning accuracy score;

[0090] in, Indicates the first Positioning accuracy score over a given time period This indicates the total number of data points within that time period. Indicates the first Within the time period, the first The actual positioning distance of each data point Indicates the first Within the time period, the first Reference positioning distance for each data point Indicates the first Within the time period, the first The velocity value of each data point Indicates the first Within the time period, the first The error of each data point affects the weight. Index representing a time period Indicates the first [number]th ... Index of data points.

[0091] formula:

[0092] ;

[0093] Detailed explanation of the formula and its calculation derivation:

[0094] The formula is used to calculate the positioning accuracy score of the receiver in each time period;

[0095] Parameter meanings and settings:

[0096] For the first The positioning accuracy score for each time period reflects the positioning accuracy performance of the receiver within that time period. The calculated score provides a basis for subsequent optimization and evaluation.

[0097] The total number of data points within that time period represents the number of data points in the first period. The number of location data points collected within a time period is set to 5, meaning there are 5 data points in each time period.

[0098] For the first Within the time period, the first The actual positioning distance of each data point represents the actual measured positioning value. The set values ​​are 500, 520, 530, 510, and 515 meters, reflecting the actual position error of each data point.

[0099] For the first Within the time period, the first The reference positioning distance for each data point represents the ideal positioning value of that point, with set values ​​of 485, 510, 520, 495, and 500 meters.

[0100] For the first Within the time period, the first The error influence weight of each data point reflects the degree of influence of the error on the positioning accuracy score. The set value is 0.1, which means that the points with smaller errors have a smaller influence weight.

[0101] For the first Within the time period, the first The speed value of each data point represents the moving speed of that data point. The set values ​​are 15, 14, 16, 13, and 14 meters per second, which represent the speed of that location point.

[0102] Substitute the parameters into the formula to calculate:

[0103] ;

[0104] ;

[0105] The calculation results show that This result indicates that the receiver is at the 1st epoch. The positioning accuracy score over a given time period reflects that the receiver's positioning error was relatively large during that period, and that speed variations and errors had a strong impact. This indicates that the positioning accuracy may be affected by external environmental factors and requires further optimization and adjustment.

[0106] Based on the error dataset, the specific steps for generating motion state influence data are as follows: By calculating the impact of various motion state parameters on error changes, including acceleration, velocity, and turning angle, the receiver's performance under various motion states is analyzed, and the error change trend under each motion state is evaluated.

[0107] S301: Based on the error dataset, call the motion state parameters of each positioning data point of the receiver, including acceleration, velocity and turning angle. Based on the motion state and error performance of each data point, analyze the influence of various motion state parameters on the change of receiver error and generate motion state parameter influence data.

[0108] Based on the error dataset, the system first collects the receiver's positioning data during the test, including longitude, latitude, speed, and acceleration, and records the corresponding timestamps. This data is then compared with the receiver's actual trajectory. By collecting the receiver's actual motion parameters, including speed, acceleration, and turning angle, the system compares the motion state of each data point with its position deviation to calculate the position error for each data point. For each data point, the system calculates the relationship between position error and motion state, using numerical calculation methods to determine the degree of deviation for each data point. For example, in the test, suppose the receiver measures a speed of [missing value] at a data point. The actual speed is Therefore, the speed difference can be used as part of the error, and this difference will affect the position error. In this process, we assume the influence coefficient of the turning angle on the error is... (Assuming that a 1-degree turn has an error impact of 0.05m), the relationship between the turning angle and the position deviation can be calculated using the following formula:

[0109] ;

[0110] in, For positional deviation, The influence coefficient of the turning angle. For the turning angle difference, set to Substituting this into the formula, we get:

[0111] ;

[0112] This process can calculate the change in error at each time point and ultimately generate data on the impact of motion state parameters.

[0113] S302: Based on the motion state parameter influence data, evaluate the error change trend of the receiver under various motion states, analyze the error change trend under each motion state, and generate change trend data;

[0114] Based on the influence data of motion state parameters, the system analyzes the error variation trend of the receiver under different motion states. During the analysis, the error fluctuations within each time period are first calculated based on the receiver's motion state data (including acceleration, velocity, and turning angle), particularly the impact of acceleration changes on the error. The relationship between the acceleration deviation and the error at each data point is further quantified, assuming the measured acceleration by the receiver is... The actual acceleration is The difference between the two is:

[0115] ;

[0116] in, The acceleration measured by the receiver. For actual acceleration, For acceleration deviation, i.e.:

[0117] ;

[0118] in, The acceleration deviation coefficient, This relates to the effect of acceleration deviation on the error. The influence of the acceleration deviation value on the error can be seen through the acceleration coefficient. After calibration, substituting into the formula, we get:

[0119] ;

[0120] This deviation value is added to the position error calculation, thus affecting the error change trend. The system will calculate the error change under each motion state (such as uniform motion, acceleration, and turning) according to this method, and generate error change trend data for each state.

[0121] S303: Based on the trend data, analyze and predict the receiver's accuracy performance under various motion states, and generate motion state impact data;

[0122] Based on the trend data, the system further analyzes the receiver's accuracy performance under various motion states. During the analysis, the system clusters the error trends under different states to identify periods of poor receiver accuracy. The analysis first calculates the standard deviation and mean of each data point to assess the receiver's accuracy fluctuations under different states. The mean error under each state is set to... Sum of standard deviation The larger the standard deviation, the greater the fluctuation in accuracy. The fluctuation coefficient is calculated using the following formula:

[0123] ;

[0124] in, The standard deviation of the error. The mean of the error. Let be the volatility coefficient. Substituting the assumed values, we get:

[0125] ;

[0126] Based on this fluctuation coefficient, the system analyzes the receiver's accuracy under various motion conditions. If the fluctuation coefficient exceeds a set threshold... A high value indicates poor receiver accuracy, while a high value indicates good accuracy. By further analyzing the error data under different motion states, the system can predict the receiver's accuracy performance under various motion states, ultimately generating motion state impact data. This data reflects the receiver's performance in various dynamic environments and provides a reference for optimization.

[0127] The steps for generating environmental impact analysis data are as follows: This involves calling error datasets and motion state impact data, accessing environmental data, analyzing positioning error performance under various external environmental conditions, analyzing the impact of environmental factors on receiver performance, predicting receiver performance under various environmental conditions, and generating environmental impact analysis data.

[0128] S401: Call the error dataset and motion state impact data, call external environmental data, including weather changes, radio interference, and signal blockage, calculate the changes in receiver positioning error under various environmental conditions, and generate error change data;

[0129] In this step, the system first extracts the receiver's motion state parameters, including acceleration, velocity, and turning angle. The motion state at each time point affects positioning accuracy, especially during high-speed travel and sharp turns, where receiver errors can increase significantly. The system performs correlation analysis between these motion state parameters and error data. For example, when the receiver's acceleration is high, its position error may be larger, and changes in the turning angle may also lead to an increase in position error. Specifically, the system analyzes each data point individually, calculating the error changes under different motion states, and thus generating data on the impact of motion state parameters on error changes. For example, assuming the receiver has a large error value during rapid acceleration, the system calculates the relationship between acceleration and error, finding that the error increase is 0.1 km when the acceleration is 2 m / s². The formula used is:

[0130] ;

[0131] in, For the comprehensive error, For positional deviation, For speed deviation, For acceleration deviation, These are the weighting coefficients. Assuming a position deviation of 0.01 km, a velocity deviation of 0.2 m / s, and an acceleration deviation of 0.1 m / s², the weighting coefficients are... , , Substitute into the calculation:

[0132] ;

[0133] The calculation results show that the overall error of the receiver is 0.085 km, and the generated error variation data can provide an analytical basis for the error performance of the receiver under different motion states.

[0134] S402: Based on error variation data, assess the impact of each environmental condition on receiver performance according to the changes in receiver positioning error under various external environmental conditions, and generate environmental impact assessment data.

[0135] In this step, the system analyzes the impact of external environmental conditions on receiver error variations, assessing the influence of each environmental factor (such as weather changes, radio interference, signal obstruction, etc.) on positioning accuracy. For example, the system calculates the receiver error variations under different weather conditions (such as heavy rain and dense fog) and their effects on signal propagation. By comparing error fluctuations under different environmental conditions, the system evaluates the specific impact of each environmental factor on receiver performance. Assuming that the receiver error increases by 0.2 km in environments with strong radio interference, and by 0.5 km in signal-obstructed areas, the system can generate environmental impact assessment data by analyzing these environmental change data. By calculating the relationship between environmental factors and error, the system can provide data support for the receiver's performance in complex environments. For example, the error variation formula in signal-obstructed areas is:

[0136] ;

[0137] in, Error variations caused by environmental factors Based on the basic error, This includes additional errors caused by environmental factors. Assuming a baseline error of 0.1 km and an error of 0.3 km due to environmental factors, the calculation is as follows:

[0138] ;

[0139] The calculation results show that the additional error caused by environmental factors is 0.4 km, and the generated environmental impact assessment data can provide accurate environmental impact assessment results for subsequent analysis.

[0140] S403: Combining environmental impact assessment data with the relationship between error data and environmental factors, predict the receiver's performance under various environmental conditions and generate environmental impact analysis data;

[0141] In this step, the system combines environmental impact assessment data with error data to predict the receiver's performance under various environmental conditions. By analyzing the error variation trend of the receiver under different environmental conditions, the system assesses potential future error fluctuations. For example, based on historical error data and the influence of different environmental factors, the system predicts the receiver's accuracy performance in rainy, snowy, or interference-prone environments. If the error fluctuation is significant in rainy weather, the system will generate corresponding environmental impact analysis data, providing a predictive basis for receiver performance optimization. Assuming the receiver's prediction error is 0.5km in a strong interference environment and 0.2km in a clear environment, the generated environmental impact analysis data can be used to predict future accuracy changes in different environments. For example, assuming the environmental impact data is... The sum of the error prediction coefficients is The formula is:

[0142] ;

[0143] in, For the prediction error, Errors caused by environmental factors This represents the influence coefficient of environmental factors. Assuming an environmental error of 0.4 km, the influence coefficient... Substitute into the calculation:

[0144] ;

[0145] The final calculation showed that the receiver's prediction error under this environment was 0.6 km, and the generated environmental impact analysis data can provide effective support for predicting the receiver's future performance.

[0146] Based on motion state impact data and environmental impact analysis data, and according to the time characteristics of error change rate and error fluctuation, the fluctuation characteristics of receiver error data are identified, the stability of the receiver is evaluated, and combined with accuracy performance data, the performance of the receiver under various environmental and motion state changes is analyzed. The specific steps to obtain the receiver performance evaluation results are as follows:

[0147] S501: Based on motion state impact data and environmental impact analysis data, obtain the rate of change of error data and the time characteristics of error fluctuation, calculate the error fluctuation trend of each data point, identify the fluctuation characteristics of receiver error data by analyzing the fluctuation of error in each time period, and generate error fluctuation characteristic data.

[0148] In this step, the system first analyzes error data obtained from motion state impact data and environmental impact analysis data, calculating the error fluctuation rate for each data point. The system extracts the error change rate for each data point and tracks its fluctuation trend over time. For example, the system compares the receiver's positioning error at different time periods, obtaining the fluctuation trend by calculating the error change rate in the time series. If the receiver experiences significant error fluctuations during acceleration, the system will flag this fluctuation characteristic. Error fluctuation characteristics can be used to determine the receiver's accuracy performance under specific environmental and motion states; the receiver's error may be larger when turning, while fluctuations are smaller during straight-line motion. Formula:

[0149] ;

[0150] in, The rate of change of error, The error at the current time point. This is the error from the previous time point. For time intervals. Assume at a given time point. The error was 0.1 km at the time point. The time error is 0.05km, and the time interval is... Assuming a time interval of 1 second, substitute the values ​​into the calculation:

[0151] ;

[0152] The calculation results show that the error change rate is 0.05 km / s. By statistically analyzing the error fluctuation trend of all data points, the generated error fluctuation characteristic data can reveal the accuracy performance of the receiver in different time periods.

[0153] S502: Call the error fluctuation characteristic data, evaluate the stability of the receiver based on the fluctuation characteristics of the error data, and obtain stability evaluation data;

[0154] In this step, the system uses the fluctuation data obtained from error fluctuation feature data to evaluate the receiver's stability. By analyzing the error fluctuation characteristics, the system determines the receiver's stability under different environments and motion conditions. For example, if the receiver's error fluctuation is very large over a certain period, its stability can be judged as poor, and vice versa. The system categorizes the error fluctuations for each time period, determines the stability of each time period, and finally summarizes them into stability evaluation data. For example, if the receiver's error fluctuation exceeds 0.1 km under high-speed motion, the receiver is considered to have poor stability under that condition, and the system will give it a low stability score. Stability is quantified by calculating the standard deviation of the error fluctuations.

[0155] ;

[0156] in, The standard deviation of the error fluctuation. For the first Error per data point The average error across all data points. This represents the number of data points. Assume... , , average error Substitute into the calculation:

[0157] ;

[0158] The calculated standard deviation is 0.041 km, and the generated stability assessment data provides a quantitative basis for the performance stability of the receiver.

[0159] S503: Using stability assessment data and receiver accuracy performance data, calculate the receiver's performance score, predict the receiver's performance under various environmental and motion state changes, and generate receiver performance assessment results.

[0160] The specific formula for calculating the receiver's performance score is as follows:

[0161] ;

[0162] in, The receiver's performance score. The weighting coefficients for the stability score. The weighting coefficients for accuracy scoring. The weighting coefficients for the volatility score. The receiver's stability is rated. To score the accuracy of the receiver. To score the error fluctuation of the receiver, The rate of change of error, This is the error correction factor.

[0163] formula:

[0164] ;

[0165] Detailed explanation of the formula and its calculation derivation:

[0166] The formula is used to calculate the receiver's overall performance score and predict the receiver's performance under different environmental and motion conditions.

[0167] Parameter meanings and settings:

[0168] The overall performance score of the receiver reflects its comprehensive performance under different environments and motion conditions;

[0169] The weighting coefficient for stability scoring is set to 0.4, reflecting the significant impact of stability on receiver performance.

[0170] The weighting coefficient for accuracy scoring is set to 0.35, reflecting the importance of accuracy in performance.

[0171] The weighting coefficient for the fluctuation score is set to 0.25, reflecting the impact of error fluctuation on performance;

[0172] The receiver's stability score is set to 0.8, representing the receiver's performance under stable conditions.

[0173] The receiver's accuracy is rated with a value of 0.9.

[0174] The receiver's error fluctuation score is set to 0.7.

[0175] The error change rate is set to 0.05, representing the rate at which the receiver error changes over time.

[0176] This is the error correction factor, set to 0.1, which reflects the degree to which error changes correct for the performance score;

[0177] Substitute the parameters into the formula to calculate:

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] The result of 0.804 indicates the overall performance of the receiver, reflecting its stability, accuracy, and error fluctuation under various environmental and motion conditions. The higher the performance score, the more stable and accurate the receiver is under various conditions.

[0183] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0184] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0185] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0186] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0187] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0189] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0190] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0192] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of navigating GNSS receiver real-time kinematic testing, characterized in that, The method comprises: S1: acquiring GNSS receiver positioning data, including longitude, latitude, speed and acceleration, collecting external environment data, including radio interference, weather changes, signal shielding, reconstructing the positioning point trajectory of the receiver according to the time information of multiple data points, and outputting the reconstructed trajectory of the positioning point; S2: according to the reconstructed trajectory of the positioning point, by comparing each data point with the actual motion trajectory of the receiver, calculating the deviation of the position, speed and acceleration of each data point, evaluating the error degree of each data point, analyzing the accuracy performance of the receiver, and obtaining an error data set; S3: based on the error data set, by calculating the influence of multiple motion state parameters on error changes, including acceleration, speed, turning angle, analyzing the performance of the receiver under multiple motion states, evaluating the error change trend under each motion state, and generating motion state influence data; S4: calling the error data set and motion state influence data, calling the environment data, analyzing the positioning error performance under multiple external environment states, analyzing the influence of environmental factors on the performance of the receiver, predicting the performance of the receiver under multiple environmental conditions, and generating environment influence analysis data; S5: based on the motion state influence data and environment influence analysis data, according to the error change rate and the time characteristics of error fluctuation, identifying the fluctuation characteristics of the receiver error data, evaluating the stability of the receiver, combining the accuracy performance data, analyzing the performance of the receiver under multiple environmental and motion state changes, and obtaining the performance evaluation result of the receiver.

2. The navigation-type GNSS receiver real-time kinematic test method according to claim 1, characterized in that, The reconstructed trajectory of the positioning point is specifically a reconstructed positioning point trajectory, external environment information, and a data point timestamp. The error data set includes position error calculation results, speed error analysis results, and receiver accuracy scores. The motion state influence data includes acceleration influence data, speed influence data, and turning angle influence data. The environment influence analysis data includes weather influence data, signal shielding influence data, and electromagnetic interference influence data.

3. The navigation-type GNSS receiver real-time kinematic test method according to claim 1, characterized in that, The steps of acquiring GNSS receiver positioning data, including longitude, latitude, speed and acceleration, collecting external environment data, including radio interference, weather changes, signal shielding, reconstructing the positioning point trajectory of the receiver according to the time information of multiple data points, and outputting the reconstructed trajectory of the positioning point are specifically: S101: acquiring GNSS receiver positioning data, including longitude, latitude, speed and acceleration, synchronously collecting external environment data, including radio interference, weather changes, signal shielding, and generating an original positioning data set; S102: based on the original positioning data set, matching the positioning data and external environment data according to the timestamp to generate a time synchronization data set; S103: using the time synchronization data set, reconstructing the positioning point trajectory of the receiver according to the positioning data and external environment data of the receiver, and generating a positioning point reconstruction trajectory.

4. The navigation-type GNSS receiver real-time kinematic test method according to claim 3, characterized in that, The steps of comparing each data point with the actual motion trajectory of the receiver, calculating the deviation of the position, speed and acceleration of each data point, evaluating the error degree of each data point, analyzing the accuracy performance of the receiver, and obtaining an error data set according to the reconstructed trajectory of the positioning point are specifically: S201: Reconstruct the trajectory according to the positioning points, obtain the actual motion trajectory of the receiver, and compare the differences between the reconstructed trajectory and the actual trajectory according to the time information, calculate the deviation of each data point position, and generate position deviation data; S202: Based on the position deviation data, calculate the speed and acceleration deviation of each data point by comparing the actual speed and acceleration at each time point, and generate speed and acceleration deviation data; S203: According to the speed and acceleration deviation data, evaluate the positioning accuracy of the receiver in each time period according to the error of each data point, analyze the accuracy performance of the receiver, and generate error data set.

5. The navigation-type GNSS receiver real-time kinematic test method according to claim 4, characterized in that, The specific formula for evaluating the positioning accuracy of the receiver in each time period is: ； Calculate the positioning accuracy score of the receiver; wherein, represents a positioning accuracy score for a th time period, represents a total number of data points in the time period, represents an actual positioning distance for a th data point in a th time period, represents a reference positioning distance for a th data point in a th time period, represents a speed value for a th data point in a th time period, represents an error influence weight for a th data point in a th time period, represents an index of a time period, represents a th data point index in the time period.

6. The navigation-type GNSS receiver real-time kinematic test method according to claim 4, characterized in that, Based on the error data set, by calculating the influence of various motion state parameters on error change, including acceleration, speed, turning angle, analyze the performance of the receiver under various motion states, evaluate the error change trend under each motion state, and generate motion state influence data. The steps are as follows: S301: According to the error data set, call the motion state parameters of each positioning data of the receiver, including acceleration, speed and turning angle, analyze the influence of various motion state parameters on the error change of the receiver according to the motion state and error performance of each data point, and generate motion state parameter influence data; S302: Based on the motion state parameter influence data, evaluate the error change trend of the receiver under various motion states, analyze the error change trend under each motion state, and generate change trend data; S303: According to the change trend data, analyze and predict the accuracy performance of the receiver under various motion states, and generate motion state influence data.

7. The navigation-type GNSS receiver real-time kinematic test method according to claim 6, characterized in that, Call the error data set and motion state influence data, call the environment data, analyze the positioning error performance under various external environment states, analyze the influence of environmental factors on the performance of the receiver, predict the performance of the receiver under various environmental conditions, and generate environment influence analysis data. The steps are as follows: S401: Call the error data set and motion state influence data, call external environment data, including weather changes, radio interference, signal shielding, calculate the change of receiver positioning error under various environmental conditions, and generate error change data; S402: Based on the error change data, according to the change of receiver positioning error under various external environment conditions, evaluate the influence of each environment condition on the performance of the receiver, and generate environment influence evaluation data; S403: Combine the environment influence evaluation data, according to the relationship between error data and environmental factors, predict the performance of the receiver under various environmental conditions, and generate environment influence analysis data.

8. The navigation-type GNSS receiver real-time kinematic test method according to claim 1, characterized by, The method further comprises: The receiver performance evaluation result includes the fluctuation characteristics of the error data, the stability evaluation result, and the performance change trend prediction result.

9. The navigation-type GNSS receiver real-time kinematic test method according to claim 8, characterized in that, Based on the motion state influence data and the environment influence analysis data, the fluctuation characteristics of the receiver error data are identified according to the error change rate and the time characteristics of error fluctuation, the stability of the receiver is evaluated, the performance of the receiver under various environments and motion state changes is analyzed in combination with the precision performance data, and the performance evaluation result of the receiver is obtained. S501: Based on the motion state influence data and the environment influence analysis data, the change rate of error data and the time characteristics of error fluctuation are obtained, the error fluctuation trend of each data point is calculated, the fluctuation characteristics of the receiver error data are identified by analyzing the fluctuation of error in each time period, and error fluctuation characteristic data is generated; S502: The error fluctuation characteristic data is called, the stability of the receiver is evaluated according to the fluctuation characteristics of the error data, and stability evaluation data is obtained; S503: The stability evaluation data is used to calculate the performance score of the receiver in combination with the precision performance data of the receiver, the performance of the receiver under various environments and motion state changes is predicted, and the performance evaluation result of the receiver is generated.

10. The navigation-type GNSS receiver real-time kinematic test method according to claim 9, characterized in that, The specific formula for calculating the performance score of the receiver is: ； wherein, is a performance score of the receiver, is a weight coefficient of the stability score, is a weight coefficient of the accuracy score, is a weight coefficient of the fluctuation score, is a stability score of the receiver, is an accuracy score of the receiver, is an error fluctuation score of the receiver, is an error change rate, is an error correction coefficient.

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