Real-time dynamic test method for navigation type GNSS receiver

Abstract

The invention relates to the technical field of GNSS (Global Navigation Satellite System) testing, in particular to a real-time dynamic testing method for a navigation type GNSS receiver, which comprises the following steps of: collecting GNSS signal data, carrying out time window sample segmentation on a received GNSS signal, analyzing signal intensity fluctuation and loss events of each sample, recording geometric coverage of each segment, calculating the number of coverage points and generating fractal dimension data. According to the invention, by collecting and analyzing the signal samples in different time windows, the fluctuation and loss events of the signal intensity can be evaluated more accurately, and the instant identification capability of signal loss and interference is enhanced. Through fractal dimension calculation and difference analysis, abnormal changes in a signal mode are effectively identified, and the understanding of navigation accuracy in a complex signal environment is improved. Besides, the signal complexity is analyzed and combined with climate and geographical environment data, so that the attribution of the signal change is more accurate, and the adaptability of the receiver under different environment conditions is optimized.

Description

Technical Field

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

[0002] The field of GNSS testing technology is the methods and tools for evaluating the performance and accuracy of global navigation satellite system (GNSS) equipment. The field includes tests in different environments and application scenarios, such as static tests, dynamic tests, and simulated environment tests. This simulates the actual working conditions of the equipment in a mobile state, such as applications in vehicle navigation and mobile mapping. Common indicators in the test process include positioning accuracy, time accuracy, signal reception capability, and anti-interference performance.

[0003] Among them, the real-time dynamic test method for navigation GNSS receivers is a technical method used to evaluate and verify the performance and accuracy of navigation GNSS receivers in real-time and dynamic environments. The method monitors the performance of the receiver in actual motion, such as capturing and processing satellite signals, and maintaining navigation accuracy under changing environmental conditions. This evaluates the performance of the device in actual applications and ensures its reliability and accuracy.

[0004] Existing technologies mainly evaluate the performance of receivers by monitoring their performance in actual motion. It is difficult to accurately reflect the actual performance of receivers under rapidly changing environmental conditions and complex signal interference. In urban environments, multipath effects and building shielding may cause signal loss, resulting in limited signal processing efficiency. In addition, existing technologies are difficult to deeply analyze the impact of environmental factors in real-time dynamic testing, which limits their adaptability and accuracy under complex geographical and climatic conditions. This leads to signal interruption or misleading in actual applications, thus affecting the accuracy and reliability of navigation. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a real-time dynamic testing method for a navigation type GNSS receiver.

[0006] In order to achieve the above object, 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: Collect GNSS signal data, segment the received GNSS signals into time window samples, analyze the signal strength fluctuation and loss events for each sample, record the geometric coverage of each segment and calculate the number of coverage points to generate fractal dimension data;

[0008] S2: performing difference analysis on the fractal dimension data, identifying abnormal changes in the signal pattern by comparing signal strengths, assessing the degree of signal irregularity in various navigation scenarios, and obtaining signal complexity analysis results;

[0009] S3: Based on the signal complexity analysis results, analyze the influence of climate data and geographical environment, perform attribution analysis of signal changes, calculate the influence of building shielding and terrain on the signal, and obtain environmental adaptability analysis results;

[0010] S4: simulating signal interference in an urban environment according to the environmental adaptability analysis result, including shielding and multipath effects, recording the response of the GNSS receiver under the influence of the simulated environmental factors, tracking the speed of processing signals, and generating response performance data;

[0011] S5: analyzing the processing response of the receiver to the signal interference in the simulated environment according to the performance data of the response, quantifying the dynamic change of the signal processing, and obtaining a dynamic response model;

[0012] S6: According to the dynamic response model, with reference to the real-time satellite data, predict the instantaneous position of the GNSS receiver, compare the prediction result with the current position information, perform error correction, and obtain the real-time position accuracy analysis result.

[0013] The present invention has improvements in that the fractal dimension data includes the number of scale coverage points within the time window, the geometric shape of the coverage area and the scale change rate; the signal complexity analysis result includes the abnormal fluctuation point of the signal, the signal interruption frequency and the scene-dependent variability index; the environmental adaptability analysis result includes the climate impact score, the terrain shielding level and the building impact coefficient; the response performance data includes the signal recovery time, the signal capture success rate and the error rate under the multipath effect; the dynamic response model includes the prediction accuracy of the model, the response speed index and the environmental change adaptability index; the real-time position accuracy analysis result includes the position prediction error, the number of error corrections and the final position accuracy index.

[0014] The present invention has the following improvements: collecting GNSS signal data, segmenting the received GNSS signal into time window samples, analyzing the signal strength fluctuation and loss events for each sample, recording the geometric coverage of each segment and calculating the number of coverage points, and generating fractal dimension data in the following specific steps:

[0015] S101: Based on the collected GNSS signal data, the continuous signal is divided into independent time periods according to time tags, each section represents a sampling period, and a time window sample is generated;

[0016] S102: Based on the time window samples, measure the maximum and minimum intensity values ​​of the signal sample by sample, record the fluctuation amplitude of the value and the frequency of events of complete signal loss, and obtain a signal event record;

[0017] S103: Based on the signal event record, the geometric coverage area of ​​each time window sample is calculated by the box counting method, the distribution density and spatial continuity of the points in the area are evaluated, the spatial uniformity of the signal is measured, and fractal dimension data is generated.

[0018] The present invention is improved in that the box counting algorithm is according to the formula:

[0019]

[0020] Calculate the fractal dimension D;

[0021] Among them, represents the size of the box, N() represents the minimum number of boxes required to cover the data, c and d are weight coefficients, is a factor that adjusts the box count based on the frequency characteristics of the signal, G int is an adjustment factor based on signal strength, and log represents the natural logarithm.

[0022] The present invention is improved in that the specific steps of performing difference analysis on the fractal dimension data, identifying abnormal changes in the signal pattern by comparing the signal strength, evaluating the signal irregularity in various navigation scenarios, and obtaining the signal complexity analysis results are as follows:

[0023] S201: Based on the fractal dimension data, comparative analysis of the time window data is performed to identify samples that are different from the data in other time windows, locate the time period and spatial distribution, and generate initial difference analysis results;

[0024] S202: Based on the initial difference analysis result, compare the maximum signal intensity fluctuation and multiple interruption events of the sample, mark the time window of abnormal fluctuation, and obtain the signal abnormal point identification result;

[0025] S203: Based on the signal anomaly point identification result, evaluate and quantify the performance of the signal in the navigation scenario, refer to the impact of the scenario on the signal quality, analyze the irregularity of the signal, and obtain the signal complexity analysis result.

[0026] The present invention is improved in that, based on the signal complexity analysis result, the influence of climate data and geographical environment is analyzed, the attribution analysis of signal changes is performed, the influence of building shielding and terrain on the signal is calculated, and the specific steps of obtaining the environmental adaptability analysis result are as follows:

[0027] S301: Based on the signal complexity analysis result, collect meteorological data of the same period, including temperature, humidity and wind speed, evaluate the impact of meteorological data on GNSS signal strength and stability, and generate climate impact data;

[0028] S302: Based on the climate impact data, geographical information is collected, including the height and layout of surrounding buildings, and structures that cause signal shielding or reflection are analyzed to obtain geographical environment impact data;

[0029] S303: Based on the geographic environment impact data, quantify the impact of buildings and terrain in the signal coverage area, determine the reduction or enhancement effect of each factor on signal quality, and obtain environmental adaptability analysis results.

[0030] The present invention is improved in that the specific steps of simulating signal interference in an urban environment, including shielding and multipath effects, according to the environmental adaptability analysis results, recording the response of the GNSS receiver under the influence of the simulated environmental factors, tracking the speed of processing signals, and generating the corresponding performance data are as follows:

[0031] S401: Based on the environmental adaptability analysis result, a virtual city model is configured to simulate signal interference scenarios of high buildings and various terrains, shielding and multipath effect parameters are set, and simulation environment settings are generated;

[0032] S402: Based on the simulation environment setting, using a sensor to record the response of the GNSS receiver to interference, including signal loss and recovery time, to obtain initial response data;

[0033] S403: Based on the initial response data, track and record the receiver's response process to the signal interference, analyze the signal processing speed and recovery efficiency, and generate response performance data.

[0034] The present invention is improved in that, according to the performance data of the response, the processing response of the receiver to the signal interference in the simulated environment is analyzed, the dynamic change of the signal processing is quantified, and the specific steps of obtaining the dynamic response model are as follows:

[0035] S501: Based on the performance data of the response, record the time series of the receiver response in each simulated interference scenario, measure the duration from signal loss to recovery, and generate dynamic change data;

[0036] S502: Based on the dynamic change data, determine the time required for the signal to recover to a normal level under each interference condition, evaluate the impact of the interference intensity on the signal stability, and obtain a signal stability evaluation result;

[0037] S503: Based on the signal stability evaluation result, the signal recovery time and the continuous impact data of interference in each scenario are integrated, and the stability of the receiver in the face of environmental signal interference is determined by calculating the Lyapunov exponent to obtain a dynamic response model.

[0038] The present invention is improved in that the Lyapunov exponent calculation method is according to the formula:

[0039]

[0040] Determine the stability of the receiver in the face of environmental signal interference;

[0041] Where, ΔE k is the receiver output change after the kth test, ΔE0 is the receiver output state before the test begins, T norm The time period used for normalization calculations, ΔT sync is the signal synchronization delay adjustment, α2 and β3 are weight coefficients, Z k and Z0 represent the additional navigation parameters at the kth and initial states, respectively.

[0042] The present invention is improved in that the specific steps of predicting the instantaneous position of the GNSS receiver according to the dynamic response model and referring to the real-time satellite data, comparing the prediction result with the current position information, performing error correction, and obtaining the real-time position accuracy analysis result are as follows:

[0043] S601: Based on the dynamic response model, synchronize real-time satellite data, use the model to perform position prediction, and generate real-time position prediction data;

[0044] S602: Based on the real-time position prediction data, compare the predicted position with the GNSS position obtained by the current measurement, record the position deviation between the two, and obtain initial error data;

[0045] S603: Based on the initial error data, multiple iterative corrections are performed to gradually optimize the error between the predicted position and the current position, and obtain real-time position accuracy analysis results.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, by collecting and analyzing signal samples in different time windows, the fluctuations and loss events of signal strength can be evaluated more accurately, and the ability to immediately identify signal loss and interference is enhanced. By calculating fractal dimensions and performing difference analysis, abnormal changes in signal patterns are effectively identified, and the understanding of navigation accuracy in complex signal environments is improved. In addition, the signal complexity is analyzed and combined with climate and geographic environment data to make the attribution of signal changes more accurate, thereby optimizing the adaptability of the receiver under different environmental conditions. By simulating signal interference in urban environments, the receiver's response to multipath effects and building occlusion is enhanced, achieving more efficient signal processing and rapid tracking of dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a real-time dynamic testing method for a navigation GNSS receiver is proposed for the present invention;

[0049] Figure 2 This is a detailed flow chart of step S1 of the present invention;

[0050] Figure 3 This is a detailed flow chart of step S2 of the present invention;

[0051] Figure 4 This is a detailed flow chart of step S3 of the present invention;

[0052] Figure 5 This is a detailed flow chart of step S4 of the present invention;

[0053] Figure 6 This is a detailed flow chart of step S5 of the present invention;

[0054] Figure 7 This is a detailed flow chart of step S6 of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0057] Example

[0058] See also Figure 1 The present invention provides a technical solution: a real-time dynamic test method for a navigation GNSS receiver, comprising the following steps:

[0059] S1: Collect GNSS signal data, segment the received GNSS signals into time window samples, analyze the signal strength fluctuation and loss events for each sample, record the geometric coverage of each segment and calculate the number of coverage points to generate fractal dimension data;

[0060] S2: Perform differential analysis on fractal dimension data, identify abnormal changes in signal patterns by comparing signal strength, assess the degree of signal irregularity in various navigation scenarios, and obtain signal complexity analysis results;

[0061] S3: Based on the signal complexity analysis results, analyze the influence of climate data and geographical environment, conduct attribution analysis of signal changes, calculate the influence of building shielding and terrain on the signal, and obtain the environmental adaptability analysis results;

[0062] S4: Simulate signal interference in urban environments, including obstruction and multipath effects, based on the results of the environmental adaptability analysis, record the response of the GNSS receiver under the influence of simulated environmental factors, track the speed of processing signals, and generate response performance data;

[0063] S5: Analyze the processing response of the receiver to signal interference in a simulated environment based on the response performance data, quantify the dynamic changes of signal processing, and obtain a dynamic response model;

[0064] S6: According to the dynamic response model, refer to the real-time satellite data to predict the instantaneous position of the GNSS receiver, compare the prediction result with the current position information, perform error correction, and obtain the real-time position accuracy analysis result.

[0065] The fractal dimension data includes the number of scale coverage points within the time window, the geometric shape of the coverage area, and the scale change rate. The signal complexity analysis results include the abnormal fluctuation points of the signal, the signal interruption frequency, and the scene-dependent variability index. The environmental adaptability analysis results include the climate impact score, the terrain obstruction level, and the building impact coefficient. The response performance data includes the signal recovery time, the signal capture success rate, and the error rate under the multipath effect. The dynamic response model includes the model's prediction accuracy, response speed index, and environmental change adaptability index. The real-time position accuracy analysis results include the position prediction error, the number of error corrections, and the final position accuracy index.

[0066] See also Figure 2 , collect GNSS signal data, segment the received GNSS signals into time window samples, analyze the signal strength fluctuation and loss events for each sample, record the geometric coverage of each segment and calculate the number of coverage points. The specific steps to generate fractal dimension data are as follows:

[0067] S101: Based on the collected GNSS signal data, the continuous signal is divided into independent time periods according to the time mark, each section represents a sampling period, and the process of generating time window samples is as follows;

[0068] The total signal data is divided into multiple window samples of length Δt according to the timestamp index. For each sample, the original signal data in the corresponding time window is extracted and stored as a separate sample data set. Such division ensures the independence of the samples and the integrity of the corresponding time series. Calculation is performed, where T represents the total signal recording time in seconds, obtained from the timestamp of the GNSS receiver data. Δt represents the time length of a single sample in seconds, set according to the test requirements. N is the number of data points in the sample.

[0069] S102: Based on the time window samples, measure the maximum and minimum intensity values ​​of the signal sample by sample, record the fluctuation amplitude of the value and the frequency of the event of complete signal loss, and obtain the signal event record process as follows;

[0070] Based on the time window samples, the maximum value S of the signal is calculated for each sample. max and the minimum value S min , and further calculate the signal intensity fluctuation amplitude ΔS for each sample. At the same time, record the frequency f of complete signal loss during the sample period, that is, the frequency of zero signal value. These data provide important information for analyzing the stability and reliability of the signal under different conditions. The fluctuation amplitude is calculated by the formula ΔS=S max -S min Calculate, the event frequency by the formula Calculate, S max and Smin Respectively represent the maximum and minimum signal strength values ​​in the sample, which are obtained by traversing the data points in the sample. x is an indicative function. When the signal S i It takes 1 when it is 0, otherwise it takes 0. i represents the signal value in the identification sample.

[0071] S103: Based on the signal event record, the geometric coverage area of ​​each time window sample is calculated by the box counting method, the distribution density and spatial continuity of the points in the area are evaluated, the spatial uniformity of the signal is measured, and the process of generating fractal dimension data is as follows;

[0072] Project the signal data of each sample into two-dimensional space, cover the entire data set with a box size, and count the minimum number of boxes required N(). This method evaluates the distribution density and spatial continuity of the signal points in the sample, and then measures the spatial uniformity of the signal to generate fractal dimension data;

[0073] The box counting algorithm follows the formula:

[0074]

[0075] Calculate the fractal dimension D;

[0076] Among them, represents the size of the box in meters, which is determined by the spatial resolution of the data set. The size of the box is dynamically adjusted from the smallest scale of the signal data to the largest scale to cover all data points. N() represents the minimum number of boxes required to cover the data, which is obtained by projecting the signal data into two-dimensional space and applying the box counting method, that is, counting the number of boxes required to completely cover all data points. c and d are weight coefficients used to adjust the influence of frequency and intensity factors on the calculation of fractal dimension, which are usually determined by fitting the historical data set and regression analysis of the prediction error. is a factor that adjusts the box count based on the frequency characteristics of the signal, specifically, It can be obtained by integrating the power spectrum density P(f1) of the signal, and the calculation formula is: f1 is the specific frequency component in the signal, G int It is an adjustment factor based on signal strength, used to adjust the box count according to changes in signal strength. It is obtained by calculating the standard deviation of the signal strength. The formula is: s y is the signal strength of the y-th sample, is the mean signal intensity, X is the number of samples, and log represents the natural logarithm, which is used to calculate the logarithmic ratio of the size to the number of boxes.

[0077] The calculation process is as follows:

[0078] Assume that the power spectrum of the signal is P(f1), where f1 is the frequency, then It can be defined as:

[0079] The total power of a signal is taken as a measure of its frequency characteristics.

[0080] If the signal has different strength levels in different time windows, the standard deviation of the signal strength can be used to define this parameter:

[0081]

[0082] Application of the box counting method

[0083] Choosing an appropriate box size is usually determined based on the minimum and maximum dimensions of the dataset.

[0084] To calculate the number of boxes N() corresponding to each box size, we need to project all data points into two-dimensional space and cover all points.

[0085] Compute the improved fractal dimension, taking into account the frequency and intensity properties of the signal:

[0086]

[0087] The weight coefficients c and d can be determined by minimizing the prediction error. Specifically, the historical data set can be used for regression analysis to find the best fitting parameters.

[0088] The resulting fractal dimension D provides a more accurate measure of the complexity of the signal's spatial distribution. By taking into account the frequency and intensity characteristics of the signal, we can better understand the structural characteristics of the signal, especially when using GNSS in dynamic environments such as urban canyons or forested areas. This improved fractal dimension measurement can help improve the design and optimization of navigation systems, increasing their adaptability and accuracy in complex environments.

[0089] See also Figure 3 , perform difference analysis on fractal dimension data, identify abnormal changes in signal patterns by comparing signal strength, and assess the degree of signal irregularity in various navigation scenarios. The specific steps for obtaining signal complexity analysis results are as follows:

[0090] S201: Based on the fractal dimension data, comparative analysis of the time window data is performed to identify samples that are different from the data in other time windows, locate the time period and spatial distribution, and generate the initial difference analysis results;

[0091] By calculating the fractal dimension D of each time window sample and the average fractal dimension of the entire sample The absolute deviation δD is used to identify significantly different samples. If the deviation exceeds the preset threshold τ, the sample is marked as significantly different, which helps to locate the specific time period and spatial distribution and generate initial difference analysis results. The threshold τ is usually set to twice the average deviation, based on the deviation threshold commonly used in statistics, to ensure that only significantly abnormal samples are identified. Through the formula: D represents the fractal dimension of a single time window, which is calculated by the box counting method. It represents the average fractal dimension of all samples, which is obtained by averaging the fractal dimensions of all samples. τ is the set deviation threshold, which is set according to the signal analysis requirements.

[0092] S202: Based on the initial difference analysis results, the maximum fluctuation of the signal intensity of the sample and multiple interruption events are compared, and the time window of abnormal fluctuation is marked to obtain the signal abnormal point identification result. The process is as follows;

[0093] Compare the maximum signal intensity fluctuation ΔS of the sample and the frequency f of the event of complete signal loss. Calculate the standardized deviation of the signal intensity fluctuation and interruption frequency of each sample to identify the time window of abnormal fluctuation. If the standardized deviation exceeds 1.5, it means that the fluctuation or interruption event in the sample is significantly higher than the average level. Usually this standard is based on the standard deviation deviation method, which is used in statistics to identify results that are significantly higher or lower than the average level, ensuring that only obvious abnormal fluctuations are marked. By formula: and Calculate the statistic z of ΔS ΔS and the statistic z of f f , ΔS represents the maximum signal intensity fluctuation in the sample, which is obtained by comparing the maximum and minimum signal values ​​in each sample. ΔS and σ ΔS Represent the mean and standard deviation of the signal intensity fluctuations in all samples. f represents the frequency of events in which the signal is completely lost in the sample, that is, the number of times the signal value is zero divided by the total number of events. μ f and σ f represent the mean and standard deviation of the event frequency in all samples, respectively.

[0094] S203: Based on the signal abnormal point identification result, the performance of the signal in the navigation scenario is evaluated and quantified, and the irregularity of the signal is analyzed with reference to the impact of the scenario on the signal quality, and the process of obtaining the signal complexity analysis result is as follows;

[0095] A linear regression model is used to quantify the signal irregularity and obtain the signal complexity analysis results. The model considers the correlation r between the signal anomaly and the navigation scene characteristics, and calculates the signal irregularity I by the following formula: I = β0 + β1r, where r represents the correlation coefficient between the signal anomaly and the navigation scene characteristics, which is calculated by statistical methods. β0 and β1 are the coefficients of the linear regression model, which are estimated by the least squares method. I represents the signal irregularity, which is calculated by the model and is used to measure the performance of the signal in the navigation scene.

[0096] See also Figure 4 ,Based on the signal complexity analysis results, analyze the influence of climate data and geographical environment, conduct attribution analysis of signal changes, calculate the influence of building shielding and terrain on the signal, and obtain the specific steps of environmental adaptability analysis results as follows:

[0097] S301: Based on the signal complexity analysis results, collect meteorological data of the same period, including temperature, humidity and wind speed, evaluate the impact of meteorological data on GNSS signal strength and stability, and generate climate impact data in the following process;

[0098] Collect meteorological data for the same period, including temperature A, humidity B, and wind speed C. Calculate the correlation coefficient ρ between these meteorological factors and signal stability E A,E ,ρ B,E , and ρ C,E , evaluate the specific impact of these meteorological conditions on GNSS signal stability and generate climate impact data. The calculation formula of the correlation coefficient is as follows: X represents a meteorological variable (temperature A, humidity B or wind speed C). i′ It is a single measurement value obtained from the meteorological monitoring data of the same period, where i′ refers to the index of each time point, representing a specific measurement moment. is the average value of the meteorological variable, calculated by calculating all X i′ The average value of E represents the GNSS signal stability in the corresponding time window. i′ Is a single measurement of signal stability, taken from the recorded data from a GNSS receiver. is the average value of signal stability, calculated by calculating all E i′ The average value of is obtained.

[0099] S302: Based on the climate impact data, geographic information is collected, including the height and layout of surrounding buildings, and the structure that causes signal blocking or reflection is analyzed to obtain the geographic environment impact data;

[0100] Based on the climate impact data, we further collect geographic information, including the height h and layout of surrounding buildings. By analyzing the shielding or reflection effect of the height and layout of the building on the signal, we calculate the index of the building's impact on the signal I build This index is determined by evaluating the geometric relationship between the spatial distribution of buildings and the signal reception location to measure the impact of signal blocking or reflection: h j Indicates the height of the jth building, obtained from the geographic information system, where j is the index of the building. j It represents the distance between the building and the signal receiving point, which is also calculated from the geographic information system. λ is the attenuation constant, which is set according to the building material and signal propagation characteristics.

[0101] S303: Based on the geographical environment impact data, the impact of buildings and terrain in the signal coverage area is quantified, and the reduction or enhancement effect of each factor on the signal quality is determined. The process of obtaining the environmental adaptability analysis result is as follows;

[0102] The impact on signal quality Q can be calculated using the following model: Q = α1·I build +β2·I terrain +γ1, α1, β2 are adjustment coefficients for building and terrain effects, determined based on field tests and historical data analysis. terrain Represents the impact index of terrain on the signal. The calculation method is similar to the building impact index, but it takes into account the height, slope and distance of the terrain from the signal receiving point. γ1 is a constant term that adjusts the baseline signal quality and is set based on the basic environmental conditions of the signal measurement.

[0103] See also Figure 5 ,According to the results of environmental adaptability analysis, the specific steps of simulating signal interference in urban environments, including occlusion and multipath effects, recording the response of GNSS receivers under the influence of simulated environmental factors, tracking the speed of processing signals, and generating response performance data are as follows:

[0104] S401: Based on the environmental adaptability analysis results, a virtual city model is configured to simulate signal interference scenarios of high buildings and various terrains, and shielding and multipath effect parameters are set to generate a simulation environment setting process;

[0105] Based on the environmental adaptability analysis results, the virtual city model is configured to simulate signal interference scenarios with high buildings and various terrains. The occlusion parameter θ is set in the simulation environment. obsc and multipath effect parameter θ mp , these parameters quantify the effect of buildings and terrain on the signal: through the formula: θ env =κ1·θ obsc +κ2·θ mp, calculate the total environmental impact parameter θ env ,θ obsc Parameters representing building occlusion, calculated based on building height, density, and layout, extracted from the GIS of the virtual model. mp The parameter representing the multipath effect reflects the degree of interference caused by the signal encountering multiple reflection surfaces, which is obtained through simulation tests and historical data. κ1 and κ2 are weight coefficients used to adjust the contribution of shielding and multipath effects to the total environmental impact, which are determined based on laboratory tests and theoretical analysis.

[0106] S402: Based on the simulation environment setting, the sensor is used to record the response of the GNSS receiver to the interference, including the signal loss and recovery time, and the process of obtaining the initial response data is as follows;

[0107] Based on the simulated environment setting, sensors are used to record the response of the GNSS receiver to interference, including signal loss and recovery time. These time lengths Δt loss and Δt rec It is a key indicator used to evaluate the performance of a receiver under specific interference conditions. response =Δt loss +Δt rec , calculate the response time T response , Δt loss The time from signal loss to recapture is measured directly from the time the sensor records the signal disappearance to when it is recaptured. rec Indicates the signal recovery time, measured from the time the signal is captured by the receiver again to the time it is output stably.

[0108] S403: Based on the initial response data, the process of tracking and recording the receiver's response process to the signal interference, analyzing the signal processing speed and recovery efficiency, and generating the response performance data is as follows;

[0109] Based on the initial response data, track and record the receiver's reaction process to signal interference. By analyzing the signal processing speed and recovery efficiency, quantify the receiver's performance, through the formula: Q perf =λ1·v proc +λ2·η rec , calculate the quantitative performance index Q perf , v proc Indicates the speed of signal processing, usually calculated based on the amount of data processed and the time required for processing, and obtained directly from the receiver's system log or performance monitoring tool. recIt represents the recovery efficiency, which is usually evaluated based on the integrity and time of signal recovery, and is obtained by comparing the changes in signal quality before and after recovery. λ1 and λ2 are weight coefficients used to comprehensively evaluate the impact of processing speed and recovery efficiency on overall performance. These coefficients are determined based on previous simulation and field test experience. perf Indicates the overall response efficiency of the receiver to interference and is used to evaluate the performance of the receiver in different test scenarios.

[0110] See also Figure 6 ,According to the performance data of the response, the specific steps of analyzing the receiver's processing response to signal interference in a simulated environment, quantifying the dynamic changes of signal processing, and obtaining the dynamic response model are as follows:

[0111] S501: Based on the response performance data, the time series of the receiver response in each simulated interference scenario is recorded, and the duration from signal loss to recovery is measured. The process of generating dynamically changing data is as follows;

[0112] Based on the performance data, the time series response of the receiver under each interference scenario is recorded. This includes the signal loss t loss To restore rec The duration Δt′ is calculated by the formula: Δt′ = t rec -t loss , calculate the time from signal loss to recovery, t rec (Timestamp of signal recovery) indicates the specific time when the receiver reacquires the signal, which is obtained from the timestamp recorded in the receiver log file. loss (Timestamp of signal loss) indicates the starting time when the receiver loses the signal, which is also obtained through the log file record of the receiver. Δt′ is used to measure the rapidity and efficiency of the receiver's response to interference and obtain dynamic change data.

[0113] S502: Based on the dynamic change data, the time required for the signal to recover to the normal level under each interference condition is measured, and the influence of the interference intensity on the signal stability is evaluated. The process of obtaining the signal stability evaluation result is as follows;

[0114] Using dynamic change data, determine the time T required for the signal to recover to normal level under each interference condition norm This step is used to evaluate the impact of interference strength on signal stability. The formula is: T norm =max(Δt′), T norm (Normalization Time) is the longest signal recovery time observed in a series of interference tests. It is used to evaluate the receiver's recovery capability under the most extreme conditions. The maximum value is obtained by analyzing all recorded Δt′ data.

[0115] S503: Based on the signal stability evaluation result, the signal recovery time and the continuous impact data of interference in each scenario are integrated, and the stability of the receiver in the face of environmental signal interference is determined by calculating the Lyapunov exponent, and the process of obtaining the dynamic response model is as follows;

[0116] By calculating the Lyapunov exponent λ3, integrating the signal recovery time Δt′ and the continuous impact of interference in each scenario, the dynamic response model M is generated;

[0117] The Lyapunov exponent is calculated according to the formula:

[0118]

[0119] Determine the stability of the receiver in the face of environmental signal interference;

[0120] Among them, λ′3 represents the improved Lyapunov index, which is used to quantify the sensitivity of the system to the initial conditions and characterize the stability or instability of the dynamic response of the system to disturbances. k is the receiver output change after the kth test, which is calculated by comparing the signal quality before and after the test. ΔE0 is the receiver output state before the test starts, which is used as a comparison benchmark. norm The time period used to normalize the calculations to ensure that the Lyapunov exponents reflect long-term behavior, ΔT sync is the signal synchronization delay adjustment, which is used to consider the delay in real-time data processing. α2 and β3 are weight coefficients, which are used to adjust the navigation parameter Z in the kth and initial states, respectively. k The contribution of Z0 and Z1 to the signal change. These coefficients are obtained by data fitting and are usually optimized by regression analysis using historical test data. k and Z0 represent the additional navigation parameters at the kth and initial states, respectively, such as the relative position change and signal strength of the satellite. Adding these parameters can increase the sensitivity of the model to environmental changes.

[0121] The calculation process is as follows:

[0122] Parameter definition and initialization

[0123] ΔE k The parameters ΔE0 and ΔE1 represent the receiver output change after the kth test and the receiver output state before the test. These values ​​are calculated directly by comparing the signal strength of the receiver before and after the test to ensure that they reflect the actual signal changes.

[0124] ΔE k =E after,k -E before,k

[0125] ΔE0=E initial

[0126] Among them, E after,k and E before,k are the signal strengths before and after the kth test, E initial is the signal strength before the test starts.

[0127] Z k The parameters of Z0 and Z1 reflect additional data related to navigation, such as the relative position change of satellites or other environmental factors, which can be obtained from the receiver or the relevant navigation system database.

[0128] Z k =f nav (data nav,k )

[0129] Z0=f nav (data nav,initial )

[0130] Among them, f nav Is a function used to get the navigation data from data nav,k and data nav,initial Extract or calculate relevant navigation parameters.

[0131] The α2 and β3 weight coefficients are determined by statistical analysis of historical data, and regression analysis or least squares method is usually used to optimize these parameters to ensure that the impact of the parameters on output changes can be best reflected.

[0132] α2,β3=optimize(ΔE k ,Z k ,ΔE0,Z0)

[0133] The optimization process involves analyzing the historical data set to find the best fit coefficients.

[0134] By combining the above parameters and adjustments, the Lyapunov exponent is calculated:

[0135]

[0136] Here, T norm is the normalized time period, ΔT sync This is to take into account the data processing delay.

[0137] The Lyapunov exponent λ′3 provides a more accurate measure for evaluating the responsiveness of GNSS receivers to environmental changes and signal interference in real-time dynamic testing. By integrating additional navigation data and adjusting weight coefficients, the index can more comprehensively reflect the stability of the device in complex environments, help optimize device design and performance, and enhance its adaptability and accuracy in changing and challenging navigation environments.

[0138] See also Figure 7 ,According to the dynamic response model, refer to the real-time satellite data, predict the instantaneous position of the GNSS receiver, compare the prediction result with the current position information, perform error correction, and obtain the specific steps of real-time position accuracy analysis results as follows:

[0139] S601: Based on the dynamic response model, synchronize the real-time satellite data, use the model to perform position prediction, and generate real-time position prediction data;

[0140] Based on dynamic response model M, synchronized real-time satellite data D sat , and use the model to predict the position. Using the current position P current and model adjustment factor ΔM, combined with real-time satellite data, to predict the position: P predicted =P current +ΔM·D sat , P predicted (Predicted position) is based on current real-time data and response model prediction. current is the actual position of the receiver most recently measured by GNSS. ΔM is the position adjustment factor extracted from the dynamic response model M. sat is the data currently received from the satellite system.

[0141] S602: Based on the real-time position prediction data, the predicted position is compared with the GNSS position obtained by the current measurement, and the position deviation between the two is recorded to obtain the initial error data.

[0142] Using the predicted position P predicted and the position P actually measured by GNSS actual , compare and calculate the position deviation between the two: ΔP = P predicted -P actual , ΔP is the difference between the predicted position and the actual position and is used to evaluate the accuracy of the prediction.

[0143] S603: Based on the initial error data, multiple iterations of correction are performed to gradually optimize the error between the predicted position and the current position, and the process of obtaining the real-time position accuracy analysis result is as follows;

[0144] Through iteration, the model parameters are adjusted according to the position deviation ΔP, and the predicted position is gradually optimized to reduce the error and improve the positioning accuracy: P optimized =P predicted -α3·ΔP,P optimized is the optimized predicted position, which is adjusted to reduce the error. α3 is the adjustment coefficient, which determines the degree to which the predicted position is adjusted in each iteration. This iterative process continues until ΔP drops to an acceptable range or the predetermined number of iterations is reached.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A real-time dynamic test method for a navigation GNSS receiver, characterized in that: The following steps are involved: Collect GNSS signal data, segment the received GNSS signals into time window samples, analyze the signal strength fluctuation and loss events for each sample, record the geometric coverage of each segment and calculate the number of coverage points to generate fractal dimension data; Performing difference analysis on the fractal dimension data, identifying abnormal changes in the signal pattern by comparing signal strength, assessing the degree of signal irregularity in various navigation scenarios, and obtaining signal complexity analysis results; Based on the signal complexity analysis results, the influence of climate data and geographical environment is analyzed, the attribution analysis of signal changes is performed, the influence of building shielding and terrain on the signal is calculated, and the environmental adaptability analysis results are obtained; Simulating signal interference in an urban environment, including shielding and multipath effects, based on the environmental adaptability analysis results, recording the response of the GNSS receiver under the influence of the simulated environmental factors, tracking the speed of processing signals, and generating response performance data; Analyzing the processing response of the receiver to the signal interference in the simulated environment according to the performance data of the response, quantifying the dynamic changes of the signal processing, and obtaining a dynamic response model; According to the dynamic response model, with reference to real-time satellite data, the instantaneous position of the GNSS receiver is predicted, the prediction result is compared with the current position information, error correction is performed, and the real-time position accuracy analysis result is obtained.

2. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: The fractal dimension data includes the number of scale coverage points within the time window, the geometric shape of the coverage area and the scale change rate; the signal complexity analysis results include the abnormal fluctuation points of the signal, the signal interruption frequency and the scene-dependent variability index; the environmental adaptability analysis results include the climate impact score, the terrain shielding level and the building impact coefficient; the response performance data includes the signal recovery time, the signal capture success rate and the error rate under the multipath effect; the dynamic response model includes the model's prediction accuracy, the response speed index and the environmental change adaptability index; the real-time position accuracy analysis results include the position prediction error, the number of error corrections and the final position accuracy index.

3. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: Collect GNSS signal data, segment the received GNSS signals into time window samples, analyze the signal strength fluctuation and loss events for each sample, record the geometric coverage of each segment and calculate the number of coverage points. The specific steps to generate fractal dimension data are as follows: Based on the collected GNSS signal data, the continuous signal is divided into independent time periods according to the time stamp, each of which represents a sampling period, to generate time window samples; Based on the time window samples, measure the maximum and minimum intensity values ​​of the signal sample by sample, record the fluctuation amplitude of the value and the frequency of events in which the signal is completely lost, and obtain a signal event record; Based on the signal event records, the geometric coverage area of ​​each time window sample is calculated by the box counting method, the distribution density and spatial continuity of the points in the area are evaluated, the spatial uniformity of the signal is measured, and the fractal dimension data is generated.

4. The real-time dynamic testing method for a navigation-type GNSS receiver according to claim 3, characterized in that: The box counting algorithm follows the formula: Calculate the fractal dimension D; Among them, represents the size of the box, N() represents the minimum number of boxes required to cover the data, c and d are weight coefficients, is a factor that adjusts the box count based on the frequency characteristics of the signal, G int is an adjustment factor based on signal strength, and log represents the natural logarithm.

5. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: The specific steps of performing difference analysis on the fractal dimension data, identifying abnormal changes in the signal pattern by comparing the signal strength, evaluating the signal irregularity in various navigation scenarios, and obtaining the signal complexity analysis results are as follows: Based on the fractal dimension data, comparative analysis of the time window data is performed to identify samples that are different from the data in other time windows, locate the time period and spatial distribution, and generate initial difference analysis results; Based on the initial difference analysis results, the maximum fluctuation of the signal intensity of the sample and multiple interruption events are compared, the time window of abnormal fluctuation is marked, and the signal abnormal point identification result is obtained; Based on the signal anomaly identification result, the performance of the signal in the navigation scenario is evaluated and quantified, and the impact of the scenario on the signal quality is referred to, the irregularity of the signal is analyzed, and the signal complexity analysis result is obtained.

6. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: Based on the signal complexity analysis results, the influence of climate data and geographical environment is analyzed, the attribution analysis of signal changes is performed, the influence of building shielding and terrain on the signal is calculated, and the specific steps for obtaining the environmental adaptability analysis results are as follows: Based on the signal complexity analysis results, collect meteorological data of the same period, including temperature, humidity and wind speed, evaluate the impact of meteorological data on GNSS signal strength and stability, and generate climate impact data; Based on the climate impact data, geographic information is collected, including the height and layout of surrounding buildings, and structures that cause signal obstruction or reflection are analyzed to obtain geographic environment impact data; Based on the geographic environment impact data, the impact of buildings and terrain in the signal coverage area is quantified, the reduction or enhancement effect of each factor on the signal quality is determined, and the environmental adaptability analysis result is obtained.

7. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: The specific steps of simulating signal interference in an urban environment, including shielding and multipath effects, based on the environmental adaptability analysis results, recording the response of the GNSS receiver under the influence of the simulated environmental factors, tracking the speed of processing signals, and generating response performance data are as follows: Based on the environmental adaptability analysis results, a virtual city model is configured to simulate signal interference scenarios of high buildings and various terrains, shielding and multipath effect parameters are set, and a simulation environment setting is generated; Based on the simulation environment setting, using a sensor to record the response of the GNSS receiver to interference, including signal loss and recovery time, to obtain initial response data; Based on the initial response data, the receiver's reaction process to the signal interference is tracked and recorded, the signal processing speed and recovery efficiency are analyzed, and the response performance data is generated.

8. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: According to the performance data of the response, the specific steps of analyzing the processing response of the receiver to the signal interference in the simulated environment, quantifying the dynamic changes of the signal processing, and obtaining the dynamic response model are as follows: Based on the performance data of the response, a time series of the receiver's response under each simulated interference scenario is recorded, and the duration from signal loss to signal recovery is measured to generate dynamic change data; Based on the dynamic change data, the time required for the signal to recover to a normal level under each interference condition is measured, the influence of the interference intensity on the signal stability is evaluated, and a signal stability evaluation result is obtained; Based on the signal stability evaluation results, the signal recovery time and the continuous impact data of interference in each scenario are integrated, and the stability of the receiver in the face of environmental signal interference is determined by calculating the Lyapunov exponent to obtain a dynamic response model.

9. The real-time dynamic testing method for a navigation-type GNSS receiver according to claim 8, characterized in that: The Lyapunov exponent calculation method is according to the formula: Determine the stability of the receiver in the face of environmental signal interference; Where, ΔE k is the receiver output change after the kth test, ΔE0 is the receiver output state before the test begins, T norm The time period used for normalization calculations, ΔT sync is the signal synchronization delay adjustment, α2 and β3 are weight coefficients, Z k and Z0 represent the additional navigation parameters at the kth and initial states, respectively.

10. The real-time dynamic testing method for a navigation GNSS receiver according to claim 1, characterized in that: The specific steps of predicting the instantaneous position of the GNSS receiver according to the dynamic response model and referring to the real-time satellite data, comparing the prediction result with the current position information, performing error correction, and obtaining the real-time position accuracy analysis result are as follows: Based on the dynamic response model, synchronize real-time satellite data, use the model to perform position prediction, and generate real-time position prediction data; Based on the real-time position prediction data, the predicted position is compared with the GNSS position obtained by the current measurement, and the position deviation between the two is recorded to obtain initial error data; Based on the initial error data, multiple iterative corrections are performed to gradually optimize the error between the predicted position and the current position, and obtain real-time position accuracy analysis results.

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