Real-time dynamic test method for navigation type GNSS receiver

Abstract

The invention relates to the technical field of satellite navigation, in particular to a real-time dynamic test method for a navigation type GNSS receiver, which comprises the following steps of: reconstructing a positioning point track by using positioning data of the GNSS receiver, calculating an error by comparing an actual track, and analyzing the influence of a motion state parameter on error change; and analyzing the influence of the environmental factors on the receiver performance in combination with the environmental data, predicting the receiver performance change trend based on the error fluctuation and the stability evaluation data, and obtaining a receiver performance evaluation result. According to the method, the positioning track of the receiver is reconstructed by acquiring the positioning data of the GNSS receiver, the error of each data point is accurately calculated, the influence of the motion state parameters on the error change is analyzed, the performance of the receiver in various dynamic environments is accurately identified, and the performance of the receiver in various dynamic environments is accurately identified through the fusion of the environment data and the motion state data. And predicting the performance change trend of the receiver under various environmental conditions, and realizing accurate prediction and evaluation of the performance of the receiver in combination with the precision performance data.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation, and particularly to a real-time dynamic testing method for a navigation-type GNSS receiver. Background Art

[0002] The technical field of satellite navigation includes technologies for positioning, navigation, and timing using global navigation satellite systems. The core content of this technical field involves GNSS signal reception, processing, and analysis, and focuses on solving problems such as precise tracking of satellite signals, optimization of positioning algorithms, correction of error sources, and improvement of system performance. Satellite navigation technology is widely used in multiple fields such as aviation, aerospace, automotive, military, surveying and mapping, and transportation to provide high-precision positioning and navigation services. With the diversification of navigation requirements and the complexity of application scenarios, satellite navigation systems need to continuously adapt to changes in different environments and dynamic conditions, which poses higher requirements for the performance of GNSS receivers, especially the stability and accuracy of receivers in complex and dynamic environments.

[0003] Among them, a real-time dynamic testing method for a navigation-type GNSS receiver refers to a testing method used to evaluate the performance of a navigation-type GNSS receiver in a moving state, aiming to solve the problem of testing the key performances such as positioning accuracy, tracking ability, and data delay of a GNSS receiver in a real dynamic environment. The method constructs a high-precision trajectory reference system and combines it with a dynamic simulator or an actual motion platform to obtain the output data of the GNSS receiver in real time and compare and analyze it with the reference trajectory. By using the method of fusing inertial navigation system and GNSS data, the accuracy and reliability of the test are improved, and the dynamic performance of the navigation-type GNSS receiver in a complex environment is comprehensively and accurately evaluated.

[0004] When traditional real-time dynamic testing technologies for navigation-type GNSS receivers handle the performance evaluation of GNSS receivers, they focus on the calculation and analysis of static errors, ignore the performance of receivers in actual dynamic environments, only consider simple error comparisons, and lack a comprehensive evaluation of receivers under the influence of various motion states and environmental factors. As a result, receivers may not accurately reflect their performance fluctuations and dynamic performances in complex environments. Relying on laboratory environments for testing cannot effectively simulate interference sources and dynamic changes in real environments, resulting in test results that cannot fully reflect the stability and reliability of receivers in actual applications. The static testing mode makes the performance evaluation of GNSS receivers unable to meet the requirements of different application scenarios, cannot fully consider the influence of external environments and complex dynamic conditions on the performance of receivers, and affects the authenticity and operability of test results. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, an embodiment of the present invention provides a real-time dynamic testing method for a navigation-type GNSS receiver. The technical solution is as follows: To achieve the above object, the present invention adopts the following technical solution. A real-time dynamic testing method for a navigation-type GNSS receiver includes the following steps: S1: Obtain the positioning data of the GNSS receiver, including longitude, latitude, speed, and acceleration, collect the external environment data, including radio interference, weather changes, and signal occlusion, reconstruct the positioning point trajectory of the receiver according to the time information of multiple data points, and output the reconstructed positioning point trajectory; S2: According to the reconstructed positioning point trajectory, calculate the deviations of the position, speed, and acceleration of each data point by comparing point by point with the actual movement trajectory of the receiver, evaluate the error degree of each data point, analyze the accuracy performance of the receiver, and obtain an error data set; S3: Based on the error data set, analyze the performance of the receiver in various motion states by calculating the influence of various motion state parameters on the error change, including acceleration, speed, and turning angle, evaluate the error change trend in each motion state, and generate motion state influence data; S4: Call the error data set and the motion state influence data, call the environmental data, analyze the influence of environmental factors on the performance of the receiver by analyzing the positioning error performance in various external environmental states, predict the performance of the receiver under various environmental conditions, and generate environmental influence analysis data.

[0006] As a further solution of the present invention, the reconstructed positioning point trajectory is specifically the reconstructed positioning point trajectory, external environment information, and data point timestamps. The error data set includes the position error calculation result, speed error analysis result, and receiver accuracy score. The motion state influence data includes acceleration influence data, speed influence data, and turning angle influence data. The environmental influence analysis data includes weather influence data, signal occlusion influence data, and electromagnetic interference influence data.

[0007] As a further solution of the present invention, the steps of obtaining the positioning data of the GNSS receiver, including longitude, latitude, speed, and acceleration, collecting the external environment data, including radio interference, weather changes, and signal occlusion, reconstructing the positioning point trajectory of the receiver according to the time information of multiple data points, and outputting the reconstructed positioning point trajectory are specifically as follows: S101: Obtain the positioning data of the GNSS receiver, including longitude, latitude, speed, and acceleration, synchronously collect the external environment data, including radio interference, weather changes, and signal occlusion, and generate an original positioning data set; S102: Based on the original positioning dataset, match the positioning data and external environment data according to the time stamp to generate a time synchronization dataset; S103: Utilize the time synchronization dataset to reconstruct the positioning point trajectory of the receiver based on the positioning data and external environment data of the receiver, and generate a reconstructed positioning point trajectory.

[0008] As a further solution of the present invention, the steps of obtaining an error dataset by comparing the reconstructed positioning point trajectory with the actual movement trajectory of the receiver point by point, calculating the deviations 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 are specifically as follows: S201: According to the reconstructed positioning point trajectory, obtain the actual movement 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 the position of each data point, and generate position deviation data; S202: Based on the position deviation data, calculate the speed and acceleration deviations 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 an error dataset.

[0009] As a further solution of the present invention, the specific formula for evaluating the positioning accuracy of the receiver in each time period is: ; Calculate the positioning accuracy score of the receiver; where, represents the positioning accuracy score in the th time period, represents the total number of data points in this time period, represents the actual positioning distance of the th data point in the th time period, represents the reference positioning distance of the th data point in the th time period, represents the speed value of the th data point in the th time period, represents the error influence weight of the th data point in the th time period, represents the index of the time period, represents the th data point index in this time period.

[0010] As a further aspect of the present invention, based on the error data set, by calculating the influence of various motion state parameters on the error change, including acceleration, speed, and turning angle, analyzing the performance of the receiver in various motion states, and evaluating the error change trend in each motion state, the steps of generating motion state influence data are specifically 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. According to the motion state and error performance of each data point, analyze the influence of various motion state parameters on the error change of the receiver, and generate motion state parameter influence data; S302: Based on the motion state parameter influence data, evaluate the error change trend of the receiver in various motion states, analyze the error change trend in each motion state, and generate change trend data; S303: According to the change trend data, analyze and predict the accuracy performance of the receiver in various motion states, and generate motion state influence data.

[0011] As a further aspect of the present invention, call the error data set and the motion state influence data, call the environmental data, and by analyzing the positioning error performance in various external environmental states, analyze the influence of environmental factors on the performance of the receiver, predict the performance of the receiver under various environmental conditions, and the steps of generating environmental influence analysis data are specifically as follows: S401: Call the error data set and the motion state influence data, call the external environmental data, including weather changes, radio interference, and signal occlusion, calculate the change of the receiver positioning error under various environmental conditions, and generate error change data; S402: Based on the error change data, according to the change of the receiver positioning error under various external environmental conditions, evaluate the influence of each environmental condition on the performance of the receiver, and generate environmental influence evaluation data; S403: Combine the environmental influence evaluation data, according to the relationship between the error data and the environmental factors, predict the performance of the receiver under various environmental conditions, and generate environmental influence analysis data.

[0012] As a further aspect of the present invention, the method further includes: S5: Based on the motion state influence data and the environmental influence analysis data, according to the time characteristics of the error change rate and the error fluctuation, 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 result; The receiver performance evaluation result includes the fluctuation characteristics of the error data, the stability evaluation result, and the performance change trend prediction result.

[0013] As a further solution of the present invention, based on the motion state influence data and the environmental influence analysis data, according to the error change rate and the time characteristics of the error fluctuation, 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 the steps of obtaining the receiver performance evaluation result are specifically as follows: S501: Based on the motion state influence data and the environmental influence analysis data, obtain the change rate of the error data and the time characteristics of the error fluctuation, calculate the error fluctuation trend of each data point, and by analyzing the fluctuation of the error in each time period, identify the fluctuation characteristics of the receiver error data and generate error fluctuation characteristic data; S502: Invoke the error fluctuation characteristic data, evaluate the stability of the receiver according to the fluctuation characteristics of the error data, and obtain stability evaluation data; S503: Use the stability evaluation data, combine with the accuracy performance data of the receiver, calculate the performance score of the receiver, predict the performance of the receiver under various environmental and motion state changes, and generate the receiver performance evaluation result.

[0014] As a further solution of the present invention, the specific formula for calculating the performance score of the receiver is: ; Wherein, is the performance score of the receiver, is the weight coefficient of the stability score, is the weight coefficient of the accuracy score, is the weight coefficient of the fluctuation score, is the stability score of the receiver, is the accuracy score of the receiver, is the error fluctuation score of the receiver, is the error change rate, is the error correction coefficient.

[0015] The beneficial effects brought by the technical solutions provided by the embodiments of the present invention at least include: By obtaining the positioning data of the GNSS receiver, the positioning trajectory of the receiver is reconstructed, the error of each data point is accurately calculated, the influence of motion state parameters on the 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 dynamic monitoring of error fluctuations is realized, the performance of the receiver is more accurately predicted and evaluated in combination with the accuracy performance data, the stability of the receiver in the dynamic environment is feedback in real time, the accuracy and stability of the receiver are comprehensively and deeply evaluated, more detailed data support is provided for performance optimization, and the test accuracy and reliability are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0017] Figure 1 It is a schematic diagram of the working process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions in the present invention will be described below with reference to the drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "example" in the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0021] In the embodiments of the present invention, sometimes the subscript such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0022] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] Please refer to Figure 1 , the present invention provides a technical solution, a real-time dynamic testing method for a navigation-type GNSS receiver, including the following steps: S1: Obtain the positioning data of the GNSS receiver, including longitude, latitude, speed, and acceleration, collect the external environment data, including radio interference, weather changes, signal occlusion, and reconstruct the positioning point trajectory of the receiver according to the time information of multiple data points, and output the reconstructed positioning point trajectory; S2: According to the reconstructed positioning point trajectory, calculate the deviations of the position, speed, and acceleration of each data point by comparing with the actual movement trajectory of the receiver point by point, evaluate the error degree of each data point, analyze the accuracy performance of the receiver, and obtain the error data set; S3: Based on the error data set, analyze the performance of the receiver in various motion states by calculating the influence of various motion state parameters on the error change, including acceleration, speed, and turning angle, evaluate the error change trend in each motion state, and generate motion state influence data; S4: Call the error data set and the 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 environmental influence analysis data; S5: Based on the motion state influence data and the environmental influence analysis data, identify the fluctuation characteristics of the receiver error data according to the error change rate and the time characteristics of the error fluctuation, 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 result.

[0024] The reconstructed positioning point trajectory specifically refers to the reconstructed positioning point trajectory, external environment information, and data point timestamps. The error data set includes the calculation results of position errors, the analysis results of speed errors, and the accuracy scores of the receiver. The motion state influence data includes acceleration influence data, speed influence data, and turning angle influence data. The environmental influence analysis data includes weather influence data, signal occlusion influence data, and electromagnetic interference influence data. The receiver performance evaluation result includes the fluctuation characteristics of the error data, the stability evaluation result, and the prediction result of the performance change trend.

[0025] The steps of obtaining the positioning data of the GNSS receiver, including longitude, latitude, speed, and acceleration, collecting the external environment data, including radio interference, weather changes, signal occlusion, and reconstructing the positioning point trajectory of the receiver according to the time information of multiple data points, and outputting the reconstructed positioning point trajectory are specifically as follows: S101: Obtain the positioning data of the GNSS receiver, including longitude, latitude, speed, and acceleration, and synchronously collect the external environment data, including radio interference, weather changes, and signal occlusion, to generate the original positioning data set; First, the system collects the real-time positioning data of the receiver, including longitude, latitude, speed, and acceleration, etc., and simultaneously obtains the external environment data, such as radio interference, weather changes, signal occlusion and other information. These data points will carry timestamps, and the system will synchronize various types of data based on the timestamps to ensure that the data of each positioning point matches the environmental data at the corresponding time. The system analyzes each set of collected data to obtain the original data set synchronized with time, which includes positioning information and environmental status information. For external environment data, such as radio interference data, the system regularly records it through sensors or other devices and aligns its time with the positioning data to ensure that the positioning information and environmental impact factors of each data point match, and outputs the original positioning data set.

[0026] S102: Based on the original positioning data set, match the positioning data and the external environment data according to the timestamps to generate the time-synchronized data set; Next, the system uses the synchronized positioning data set and the external environment data, and according to the timestamps of each data point, performs time alignment to generate the time-synchronized data set. The key to this step is to unify the time of different data sources to ensure that the timestamps of each positioning data point and the environmental data are exactly matched. For example, the positioning data of the receiver is updated once per second, while the recording frequency of environmental data such as meteorological changes may be once every 10 seconds. The system will use the difference comparison of timestamps to align or interpolate the incompletely matched data, so that the positioning information and environmental information of each data point can be matched to the accurate time. Formula: ; Calculate the time difference between the positioning data and the environmental data, where, is the timestamp of the environmental data, is the timestamp of the positioning data. Assume that the timestamp of the environmental data is 12:00:03 and the timestamp of the positioning data is 12:00:00. Calculate the time difference: ; The calculation result shows that there is a 3-second time difference between the environmental data and the positioning data, and the system will correct this time difference so that all data points can be synchronized. The finally generated time-synchronized data set will provide an accurate data source for the subsequent trajectory reconstruction.

[0027] S103: Utilize the time-synchronized data set, and based on the positioning data of the receiver and the external environment data, reconstruct the positioning point trajectory of the receiver to generate the positioning point reconstruction trajectory; Using the time-synchronized dataset, the system combines the positioning data of the receiver with the environmental data. Based on the data information at each time point, it calculates the position changes of the receiver at different time points through kinematic methods, and then reconstructs the positioning point trajectory of the receiver. Specifically, the system uses the front and rear position data and information such as speed and acceleration, and calculates the movement trajectory of the receiver within a specific time interval through the time difference. For example, the system calculates the movement trajectory of the receiver based on the speed and acceleration data of the receiver, combined with the corresponding time difference. Formula: ; Calculate the new position of the receiver, where is the initial position of the receiver, is the speed of the receiver, is the time difference, is the acceleration. Assume that the initial position of the receiver is 45.0°, the speed is 10 m / s, the acceleration is 2 m / s², and the time difference is 1 second. Substitute into the calculation: ; The calculation results show that within 1 second, the longitude of the receiver increases from 45.0° to 45.011°. The generated positioning point reconstruction trajectory provides key data for subsequent error analysis. By continuously reconstructing the trajectories of all data points, the complete dynamic path of the receiver is finally obtained.

[0028] According to the positioning point reconstruction trajectory, by comparing it point by point with the actual movement trajectory of the receiver, calculate the deviations of the position, speed, and acceleration of each data point, evaluate the error degree of each data point, analyze the accuracy performance of the receiver, and the steps to obtain the error dataset are specifically as follows: S201: According to the positioning point reconstruction trajectory, obtain the actual movement trajectory of the receiver, and according to the time information, compare the differences between the reconstruction trajectory and the actual trajectory, calculate the deviation of the position of each data point, and generate position deviation data; First, extract the longitude, latitude, speed, and acceleration data of the receiver from the positioning point reconstruction trajectory, and then obtain the actual movement trajectory of the receiver, usually directly obtained through GNSS signals. The system compares the two one by one according to the time stamp to ensure time synchronization, and calculates the position differences between the two trajectories at each time point. For each data point, the system obtains the position deviation data by calculating the deviation on its longitude and latitude. For example, assume that at the time point , the longitude of the reconstruction trajectory of the receiver is 45.0°, and the longitude of the actual trajectory is 45.01°, then the position deviation is 0.01°. The system records this deviation as the position error. Using the formula: ; where is the position deviation, and are the longitude and latitude of the actual trajectory, and are the longitude and latitude of the reconstructed trajectory. Assume that at time point , the longitude of the actual trajectory is 45.01°, the longitude of the reconstructed trajectory is 45.0°, the latitude of the actual trajectory is 12.01°, and the latitude of the reconstructed trajectory is 12.0°. Substitute into the calculation: ; The calculated deviation is 0.0141 km, and the generated position deviation data is used for subsequent accuracy analysis.

[0029] S202: Based on the position deviation data, by comparing the actual speed and acceleration at each time point, calculate the speed and acceleration deviation of each data point, and generate speed and acceleration deviation data; The system uses the position deviation data to further calculate the speed and acceleration deviation of each data point by comparing the actual speed and acceleration data at each time point. First, the system extracts the actual speed and acceleration data of the receiver, usually obtained through speed calculation in the GNSS received signal and the acceleration sensor. Then, the system calculates the deviation between the speed and acceleration at each time point and the corresponding data points in the reconstructed trajectory. Assume that at time point , the actual speed of the receiver is 10 m / s, and the speed of the reconstructed trajectory is 9.8 m / s, then the speed deviation is 0.2 m / s. Use the formula: ; where is the speed deviation, is the actual speed, is the speed of the reconstructed trajectory. Assume the actual speed is 10 m / s and the reconstructed speed is 9.8 m / s, substitute into the calculation: ; Similarly, using the acceleration data, calculate the acceleration deviation by comparing the difference between the actual acceleration and the reconstructed acceleration. Through the above process, the system generates speed and acceleration deviation data, which is used for subsequent accuracy evaluation.

[0030] S203: According to the speed and acceleration deviation data, evaluate the positioning accuracy of the receiver in each time period based on the error of each data point, analyze the accuracy performance of the receiver, and generate an error data set; The specific formula for evaluating the positioning accuracy of the receiver in each time period is: ; Calculate the positioning accuracy score of the receiver; where represents the positioning accuracy score in the th time period, Indicates the total number of data points within that time period, Indicates the actual positioning distance of the th data point within the Indicates the reference positioning distance of the th data point within the Indicates the speed value of the th data point within the Indicates the error influence weight of the th data point within the Indicates the index of the time period, Indicates the th data point index within that time period.

[0031] Formula: ; Detailed explanation of the formula and the derivation process of formula calculation: The formula is used to calculate the positioning accuracy score of the receiver within each time period; Meaning and setting values of parameters: is the positioning accuracy score of the th time period, reflecting the positioning accuracy performance of the receiver within that time period. The calculated score provides a basis for subsequent optimization and evaluation; is the total number of data points within that time period, indicating the number of positioning data points collected within the th time period. The setting value is 5, indicating that there are 5 data points within each time period; is the actual positioning distance of the th data point within the is the reference positioning distance of the th data point within the is the error influence weight of the th data point within the th time period, reflecting the degree of influence of the error on the positioning accuracy score. The setting value is 0.1, indicating that the influence weight of points with smaller errors is smaller; For the th time period, the th speed value of the data point, representing the moving speed of the data point, with the set values being 15, 14, 16, 13, 14 m / s, representing the speed at this position point; Substitute the parameters into the formula for calculation: ; ; The calculation results show that , this result represents the positioning accuracy score of the receiver in the th time period, reflecting that within this time period, the positioning error of the receiver is relatively large, the influence of speed change and error is relatively strong, indicating that the positioning accuracy may be affected by external environmental factors and needs further optimization and adjustment.

[0032] Based on the error data set, by calculating the influence of various motion state parameters on the error change, including acceleration, speed, and turning angle, analyzing the performance of the receiver in various motion states, and evaluating the error change trend in each motion state, the steps to generate the motion state influence data are specifically 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. According to the motion state and error performance of each data point, analyze the influence of various motion state parameters on the error change of the receiver, and generate the motion state parameter influence data; According to the error data set, the system first collects the positioning data of the receiver during the test, including longitude, latitude, speed, and acceleration, and records the corresponding timestamps, and then compares them with the actual motion trajectory of the receiver. By collecting the actual motion state parameters of the receiver, including speed, acceleration, and turning angle, comparing the motion state of each data point with the position deviation, and calculating the position error of each data point. For each data point, the system calculates the relationship between the position error and the motion state, and uses numerical calculation methods to obtain the deviation degree of each data point. For example, during the test, assume that the speed measured by the receiver for a data point is , while the actual speed is , then the difference in speed can be used as part of the error, and this difference value will affect the position error. During this process, assume that the influence coefficient of the turning angle on the error is (assuming that a 1-degree turn has an influence on the error of 0.05 m), then the relationship between the turning angle and the position deviation can be calculated by the following formula: ; Among them, is the position deviation, is the influence coefficient of the turning angle, is the turning angle difference, set as , then substituting into the formula gives: ; This process can calculate the change of the error at each time point and finally generate the data of the influence of the motion state parameters.

[0033] S302: Based on the data of the influence of the motion state parameters, evaluate the error change trend of the receiver in multiple motion states, analyze the error change trend in each motion state, and generate the change trend data; Based on the data of the influence of the motion state parameters, the system analyzes the error change trend of the receiver in different motion states. During the analysis process, first, according to the motion state data of the receiver (including acceleration, speed, and turning angle), calculate the error fluctuation in each time period, especially the influence of the acceleration change on the error. The relationship between the acceleration deviation of each data point and the error is further quantified. Set the acceleration measured by the receiver as , and the actual acceleration is , and the difference between the two is: ; Among them, is the acceleration measured by the receiver, is the actual acceleration, is the acceleration deviation, that is: ; Among them, is the acceleration deviation coefficient, is the influence of the acceleration deviation on the error. The influence of the acceleration deviation value on the error relationship can be calibrated by the acceleration coefficient , substituting into the formula gives: ; This deviation value will be added to the position error calculation, thus affecting the error change trend. The system will calculate the error change in each motion state (such as uniform motion, acceleration, turning) according to this method and generate the error change trend data for each state.

[0034] S303: According to the change trend data, analyze and predict the accuracy performance of the receiver in multiple motion states, and generate the motion state influence data; Based on the trend data of changes, the system further analyzes the accuracy performance of the receiver under various motion states. During the analysis process, the system clusters the error trend of different states to find the time periods with poor accuracy performance of the receiver. When analyzing, first calculate the standard deviation and mean of each data point to evaluate the accuracy fluctuation of the receiver under different states. The error mean under the set state is and the standard deviation is . The larger the standard deviation, the greater the accuracy fluctuation. Use the following formula to calculate the fluctuation coefficient: ; where is the standard deviation of the error, is the mean of the error, is the fluctuation coefficient. Substitute the assumed values to get: ; According to this fluctuation coefficient, the system analyzes the accuracy of the receiver under various motion states. If the fluctuation coefficient is greater than the set threshold , it indicates that the accuracy of the receiver is poor, otherwise it is good. By further analyzing the error data under different motion states, the system can predict the accuracy performance of the receiver under various motion states, and finally generate motion state impact data, which will reflect the performance of the receiver in various dynamic environments and provide a reference for optimization.

[0035] The steps of calling the error data set and motion state impact data, calling the environmental data, and analyzing the impact of environmental factors on the receiver performance by analyzing the positioning error performance under various external environmental states and predicting the performance of the receiver under various environmental conditions to generate environmental impact analysis data are as follows: S401: Call the error data set and motion state impact data, call the external environmental data, including weather changes, radio interference, signal occlusion, calculate the changes in the positioning error of the receiver under various environmental conditions, and generate error change data; In this step, the system first extracts the motion state parameters of the receiver, including acceleration, speed, and turning angle. The motion state at each time point affects the positioning accuracy. Especially in the cases of high-speed driving and sharp turns, the error of the receiver may increase significantly. The system performs correlation analysis on these motion state parameters and the error data. For example, when the receiver has a high acceleration, its position error may be larger, and the change in the turning angle may also lead to an increase in the position error. Specifically, the system analyzes each data point one by one, calculates the error change under different motion states, and then generates the data on the influence of motion state parameters on the error change. For example, assume that when the receiver is accelerating rapidly, the error value is large. The system will calculate the relationship between the acceleration and the error, and obtain that the error increase is 0.1 km when the acceleration is 2 m / s². Using the formula: ; where, is the comprehensive error, is the position deviation, is the speed deviation, is the acceleration deviation, is the weight coefficient. Assume that the position deviation is 0.01 km, the speed deviation is 0.2 m / s, the acceleration deviation is 0.1 m / s², and the weight coefficient , , . Substitute into the calculation: ; The calculation result shows that the comprehensive error of the receiver is 0.085 km. The generated error change data can provide an analysis basis for the error performance of the receiver under different motion states.

[0036] S402: Based on the error change data, evaluate the influence of each environmental condition on the receiver performance according to the changes in the positioning error of the receiver under various external environmental conditions, and generate environmental impact assessment data; In this step, the system evaluates the influence of each environmental factor (such as weather changes, radio interference, signal occlusion, etc.) on the positioning accuracy by analyzing the influence of external environmental conditions on the error change of the receiver. For example, the system will calculate the error change of the receiver under these conditions according to the influence of different weather conditions (such as heavy rain, fog) on signal propagation. By comparing the error fluctuations under different environmental conditions, the system evaluates the specific influence of each environmental factor on the receiver performance. Assume that in an environment with strong radio interference, the error of the receiver increases by 0.2 km, and in the signal occlusion area, the error increases by 0.5 km. The system can generate environmental impact assessment data by analyzing these environmental change data. By calculating the relationship between the environmental factor and the error, the system can provide data support for the performance of the receiver in complex environments. For example, the error change formula in the signal occlusion area: ; Among them, is the error change caused by environmental factors, is the basic error, is the additional error caused by environmental factors. Assuming the basic error is 0.1 km and the error caused by environmental factors is 0.3 km, substitute into the calculation: ; 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.

[0037] S403: Combine the environmental impact assessment data, and according to the relationship between the error data and environmental factors, predict the performance of the receiver under various environmental conditions to generate environmental impact analysis data; In this step, the system combines the relationship between the environmental impact assessment data and the error data to predict the performance of the receiver under various environmental conditions. The system evaluates the possible error fluctuations of the receiver in the future by analyzing the error change trend of the receiver under different environmental conditions. For example, the system predicts the accuracy performance of the receiver in rainy days, snowy days or interference environments according to historical error data and the influence of different environmental factors. If the error fluctuates greatly in rainy days, the system will generate corresponding environmental impact analysis data to provide a prediction basis for receiver performance optimization. Assuming that in an environment with strong interference, the predicted error of the receiver is 0.5 km, while in a clear environment it is 0.2 km, the generated environmental impact analysis data can be used to predict the accuracy change of the receiver in the future environment. For example, assuming the environmental impact data is and the error prediction coefficient is , the formula is: ; Among them, is the predicted error, is the error caused by environmental factors, is the influence coefficient of environmental factors. Assuming the environmental error is 0.4 km and the influence coefficient , substitute into the calculation: ; Finally, the calculated result shows that the predicted error of the receiver in this environment is 0.6 km, and the generated environmental impact analysis data can provide effective support for the future performance prediction of the receiver.

[0038] Based on the motion state impact data and environmental impact analysis data, according to the error change rate and the time characteristics of error fluctuations, identify the fluctuation characteristics of the receiver error data, evaluate the stability of the receiver, and combine with the accuracy performance data to analyze the performance of the receiver under various environmental and motion state changes, and the steps to obtain the receiver performance evaluation results are specifically as follows: S501: Based on the motion state impact data and environmental impact analysis data, obtain the change rate of the error data and the time characteristics of error fluctuations, calculate the error fluctuation trend of each data point, and identify the fluctuation characteristics of the receiver error data by analyzing the error fluctuations in each time period, and generate error fluctuation characteristic data; In this step, the system first calculates the error volatility of each data point by analyzing the error data obtained from the motion state impact data and environmental impact analysis data. The system extracts the error change rate of each data point and tracks its fluctuation trend over time. For example, the system compares the positioning errors of the receiver at different time periods and obtains the fluctuation trend by calculating the change rate of the error in the time series. If the error fluctuates greatly during the acceleration phase of the receiver, the system will mark this fluctuation characteristic. The error fluctuation characteristics can be used to judge the accuracy performance of the receiver under specific environments and motion states. The error may be larger when the receiver is turning and smaller during straight-line motion. Formula: ; Among them, is the error change rate, is the error at the current time point, is the error at the previous time point, is the time interval. Assume that the error at time point is 0.1 km, the error at time point is 0.05 km, and the time interval is 1 second. Substitute into the calculation: ; The calculation result shows that the error change rate is 0.05 km / s. By statistically analyzing the error fluctuation trends of all data points, the generated error fluctuation characteristic data can reveal the accuracy performance of the receiver in different time periods.

[0039] S502: Call the error fluctuation characteristic data, evaluate the stability of the receiver according to the fluctuation characteristics of the error data, and obtain the stability evaluation data; In this step, the system evaluates the stability of the receiver by using the fluctuations obtained from the error fluctuation characteristic data. Through the analysis of the error fluctuation characteristics, the system determines the performance stability of the receiver under different environments and motion states. For example, if the error fluctuation of the receiver is very large within a certain period of time, it can be determined that its stability is poor, and vice versa. The system classifies the error fluctuations for each time period, determines the stability of each time period, and finally aggregates them into stability evaluation data. For example, if the error fluctuation of the receiver exceeds 0.1 km under the high-speed motion state, it is considered that the stability of the receiver in this state is poor, and the system will give a lower stability score. The stability is quantified by calculating the standard deviation of the error fluctuation: ; where, is the standard deviation of the error fluctuation, is the error of the th data point, is the average error of all data points, is the number of data points. Assume , , , the average error , substitute into the calculation: ; The calculated standard deviation is 0.041 km, and the generated stability evaluation data provides a quantitative basis for the performance stability of the receiver.

[0040] S503: Use the stability evaluation data, combined with the accuracy performance data of the receiver, to calculate the performance score of the receiver, predict the performance of the receiver under various environmental and motion state changes, and generate the receiver performance evaluation result; The specific formula for calculating the performance score of the receiver is: ; where, is the performance score of the receiver, is the weight coefficient of the stability score, is the weight coefficient of the accuracy score, is the weight coefficient of the fluctuation score, is the stability score of the receiver, is the accuracy score of the receiver, is the error fluctuation score of the receiver, is the error change rate, is the error correction coefficient.

[0041] Formula: ; Detailed Explanation of the Formula and the Derivation Process of Formula Calculation: The formula is used to calculate the comprehensive performance score of the receiver and predict the performance of the receiver under different environmental and motion state changes; Parameter Meanings and Set Values: is the comprehensive performance score of the receiver, reflecting the comprehensive performance of the receiver under different environments and motion states; is the weight coefficient of the stability score, with a set value of 0.4, reflecting the relatively high influence of stability on the receiver performance; is the weight coefficient of the accuracy score, with a set value of 0.35, reflecting the importance of accuracy in the performance; is the weight coefficient of the fluctuation score, with a set value of 0.25, reflecting the influence of error fluctuation on the performance; is the stability score of the receiver, with a set value of 0.8, representing the performance of the receiver under stable conditions; is the accuracy score of the receiver, with a set value of 0.9; is the error fluctuation score of the receiver, with a set value of 0.7; is the error change rate, with a set value of 0.05, indicating the rate of change of the receiver error over time; is the error correction coefficient, with a set value of 0.1, reflecting the degree of correction of the error change to the performance score; Substitute the parameters into the formula for calculation: ; ; ; ; The result 0.804 indicates the comprehensive performance of the receiver, reflecting its overall performance in terms of stability, accuracy, and error fluctuation under various environments and motion states. The higher the performance score, the more stable and accurate the receiver is under various conditions.

[0042] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The 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, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0043] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0044] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0045] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0046] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0047] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0048] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be electrical, mechanical, or other forms.

[0049] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0050] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0051] When the above-mentioned functions are implemented in the form of 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 the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0052] As described above, the above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A real-time dynamic test method for a navigation GNSS receiver, characterized in that: The method comprises: S1: Obtain GNSS receiver positioning data, including longitude, latitude, speed and acceleration, collect external environment data, including radio interference, weather changes, signal shielding, reconstruct the receiver's positioning point trajectory based on the time information of multiple data points, and output the positioning point reconstruction trajectory; S2: Reconstruct the trajectory according to the positioning point, compare it point by point with the actual motion trajectory of the receiver, calculate the deviation of the position, velocity and acceleration of each data point, evaluate the error degree of each data point, analyze the accuracy performance of the receiver, and obtain the error data set; S3: Based on the error data set, by calculating the influence of various motion state parameters on the error change, including acceleration, speed, and turning angle, analyzing the performance of the receiver in various motion states, evaluating the error change trend in each motion state, and generating motion state influence data; S4: Call the error data set 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.

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

3. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: Obtain GNSS receiver positioning data, including longitude, latitude, speed and acceleration, collect external environment data, including radio interference, weather changes, signal blocking, and reconstruct the receiver's positioning point trajectory based on the time information of multiple data points. The steps for outputting the positioning point reconstruction trajectory are as follows: S101: Acquire GNSS receiver positioning data, including longitude, latitude, speed and acceleration, and simultaneously collect external environment data, including radio interference, weather changes, and signal shielding, to generate an original positioning data set; S102: Based on the original positioning data set, matching the positioning data and the external environment data according to the timestamp to generate a time synchronization data set; S103: Reconstructing the positioning point trajectory of the receiver according to the positioning data of the receiver and the external environment data using the time synchronization data set to generate a positioning point reconstruction trajectory.

4. The real-time dynamic testing method for a navigation-type GNSS receiver according to claim 3, characterized in that: The trajectory is reconstructed based on the positioning points, and the deviation of the position, velocity and acceleration of each data point is calculated by comparing it point by point with the actual motion trajectory of the receiver, the error degree of each data point is evaluated, and the accuracy performance of the receiver is analyzed. The specific steps for obtaining the error data set are as follows: S201: reconstructing a trajectory according to the positioning point, obtaining the actual motion trajectory of the receiver, and comparing the difference between the reconstructed trajectory and the actual trajectory according to the time information, calculating the deviation of the position of each data point, and generating position deviation data; S202: Based on the position deviation data, by comparing the actual speed and acceleration at each time point, calculating the speed and acceleration deviation of each data point, and generating speed acceleration deviation data; S203: According to the velocity acceleration deviation data and the error of each data point, the positioning accuracy of the receiver in each time period is evaluated, the accuracy performance of the receiver is analyzed, and an error data set is generated.

5. The real-time dynamic testing method for a navigation-type GNSS receiver 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 receiver positioning accuracy score; in, Indicates Positioning accuracy score within a time period, Indicates the total number of data points in this time period, Indicates In the time period The actual positioning distance of the data points, Indicates In the time period The reference positioning distance of the data points, Indicates In the time period The speed value of the data point, Indicates In the time period The error of each data point affects the weight, The index of the time period. Indicates the first The index of the data point.

6. The real-time dynamic testing method for a navigation-type GNSS receiver according to claim 4, characterized in that: Based on the error data set, by calculating the influence of various motion state parameters on the error change, including acceleration, speed, and turning angle, analyzing the performance of the receiver in various motion states, and evaluating the error change trend in each motion state, the steps of generating motion state influence data are specifically as follows: S301: According to the error data set, the motion state parameters of each positioning data of the receiver are called, including acceleration, speed and turning angle, and according to the motion state and error performance of each data point, the influence of multiple motion state parameters on the receiver error change is analyzed to generate motion state parameter influence data; S302: Based on the motion state parameter influence data, evaluating the error change trend of the receiver under multiple motion states, analyzing the error change trend under each motion state, and generating change trend data; S303: Analyze and predict the accuracy performance of the receiver under various motion states based on the change trend data, and generate motion state impact data.

7. The real-time dynamic testing method for a navigation-type GNSS receiver according to claim 6, characterized in that: The steps of calling the error data set and motion state impact 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 performance of the receiver under various environmental conditions, and generating environmental impact analysis data are as follows: S401: calling the error data set and motion state impact data, calling external environment data, including weather changes, radio interference, and signal shielding, calculating changes in receiver positioning errors under various environmental conditions, and generating error change data; S402: Based on the error change data, according to the change of the positioning error of the receiver under various external environmental conditions, evaluate the impact of each environmental condition on the performance of the receiver, and generate environmental impact assessment data; S403: In combination with the environmental impact assessment data, according to the relationship between the error data and environmental factors, the performance of the receiver under various environmental conditions is predicted to generate environmental impact analysis data.

8. The real-time dynamic testing method for a navigation-type GNSS receiver according to claim 1, characterized in that: The method further comprises: S5: Based on the motion state impact data and the environmental impact analysis data, according to the error change rate and the time characteristics of the error fluctuation, the fluctuation characteristics of the receiver error data are identified, the stability of the receiver is evaluated, and the performance of the receiver under various environments and motion state changes is analyzed in combination with the accuracy performance data to obtain the receiver performance evaluation result; The receiver performance evaluation results include fluctuation characteristics of error data, stability evaluation results, and performance change trend prediction results.

9. The real-time dynamic testing method for a navigation GNSS receiver according to claim 8, characterized in that: Based on the motion state impact data and the environmental impact analysis data, according to the error change rate and the time characteristics of the error fluctuation, the fluctuation characteristics of the receiver error data are identified, the stability of the receiver is evaluated, and the performance of the receiver under various environments and motion state changes is analyzed in combination with the accuracy performance data. The steps of obtaining the receiver performance evaluation results are specifically as follows: S501: Based on the motion state impact data and the environmental impact analysis data, the change rate of the error data and the time characteristics of the error fluctuation are obtained, the error fluctuation trend of each data point is calculated, and the fluctuation characteristics of the receiver error data are identified by analyzing the fluctuation of the error in each time period, and the error fluctuation characteristic data is generated; S502: calling the error fluctuation characteristic data, evaluating the stability of the receiver according to the fluctuation characteristics of the error data, and obtaining stability evaluation data; S503: Utilize the stability evaluation data and combine it with the accuracy performance data of the receiver to calculate the performance score of the receiver, predict the performance of the receiver under various environments and motion state changes, and generate a receiver performance evaluation result.

10. The real-time dynamic testing method for a navigation GNSS receiver according to claim 9, characterized in that: The specific formula for calculating the performance score of the receiver is: ； in, is the performance score of the receiver, is the weight coefficient of stability score, is the weight coefficient of the accuracy score, is the weight coefficient of volatility score, Score the stability of the receiver, Score the accuracy of the receiver, Score the receiver's error fluctuations, is the error change rate, is the error correction factor.

Images