A high-precision receiver analog signal comprehensive detection method based on Beidou satellite

Abstract

This invention discloses a comprehensive detection method for high-precision receiver analog signals based on BeiDou satellites, specifically relating to the field of signal detection technology. It involves acquiring satellite navigation signal data received by the receiver under preset simulated motion trajectory conditions, constructing a navigation solution reference trajectory and a high-dynamic trajectory evolution model, analyzing the evolution trend of differential corrections, and extracting dynamic response parameters from the differential data. The dynamic response parameters are then jointly modeled with the motion state solution model data to output synchronization offset data. Based on the synchronization offset data and time-series trajectory prediction data, the residuals between the actual solution position and the expected trajectory at each sampling time point are analyzed. Combining the high-dynamic trajectory evolution model and the residual distribution, the method determines whether there is a risk of false detection due to error response lag in the satellite navigation signal data, effectively improving the testing accuracy and evaluation reliability of the receiver's differential positioning performance.

Description

Technical Field

[0001] This invention relates to the field of signal detection technology, and more specifically, to a comprehensive detection method for analog signals from a high-precision receiver based on the BeiDou satellite system. Background Technology

[0002] In the performance verification process of high-precision BeiDou receivers, analog signal testing methods, as a key simulation tool, are widely used in the verification and evaluation of the receiver's navigation and positioning capabilities and differential positioning performance.

[0003] In simulation tests of high-precision BeiDou receivers supporting differential positioning, current simulation systems generally employ standard message broadcasting and fixed error injection strategies, but fail to realistically reproduce the phenomenon of "latency in differential correction response under high-dynamic motion conditions" in actual scenarios. When the receiver receives analog signals in an accelerated state, due to the inherent publication period and propagation delay of the differential correction data, the corrections cannot be aligned with the motion state in real time, resulting in a pseudo-precision enhancement effect in some instantaneous position solutions (i.e., the receiver outputs abnormally good values ​​instantaneously before the error manifests). Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a high-precision receiver analog signal comprehensive detection method based on BeiDou satellite to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for comprehensive detection of analog signals from a high-precision receiver based on BeiDou satellites includes the following steps: S1: Acquire satellite navigation signal data received by the receiver under simulated motion trajectory conditions; S2: Based on pseudorange observations and carrier phase observations, construct navigation solution reference trajectory and high dynamic trajectory evolution model respectively, and output motion state solution model data and time series trajectory prediction data; S3: Analyze the evolution trend of the differential correction at continuous time points and extract the dynamic response characteristic parameters from the differential data; S4: Jointly model the motion state solution model data with the dynamic response characteristic parameters, construct the synchronization offset metric, and output the synchronization offset data; S5: Based on synchronization offset data and time series trajectory prediction data, analyze the residual between the actual solution position of the receiver and the expected trajectory at each sampling time point, and output a pseudo-precision feature point sequence; S6: Based on the pseudo-precision feature point sequence, combined with the high dynamic trajectory evolution model and residual distribution, determine whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and output the comprehensive detection result.

[0006] In a preferred embodiment, S1 specifically refers to: The Beidou high-precision receiver receives satellite navigation signal data broadcast by Beidou navigation satellites under pre-set simulated motion trajectory conditions; The received satellite navigation signal data is classified to obtain pseudorange observations, carrier phase observations, and differential correction data.

[0007] In a preferred embodiment, S2 specifically refers to: The navigation solution reference trajectory model uses pseudorange observations and carrier phase observations to calculate the position using the standard satellite navigation positioning algorithm, generating navigation solution reference trajectory data for the receiver at each sampling time point; The high-dynamic trajectory evolution model generates a dynamically predicted trajectory based on pre-set simulated motion trajectory conditions using kinematic trajectory calculation methods; The navigation solution reference trajectory data is transformed to generate motion state solution model data, including position, velocity and acceleration data obtained from navigation solution; The dynamically predicted trajectory is transformed into time series trajectory prediction data, including the discretized trajectory location point sequence output by the dynamic prediction trajectory model.

[0008] In a preferred embodiment, S3 specifically refers to: The differential correction data is divided into continuous time series to obtain differential correction data for multiple time series intervals; Statistical analysis was performed on the update interval of the differential correction data within each time series interval to obtain the update cycle parameter; Statistical analysis was performed on the time delay between the actual broadcast time and the actual reception time of the differential correction data in each time series interval to obtain the time delay characteristic parameters. Calculate the rate of change of the error correction component in the differential correction data within each time series interval to obtain the parameter of the rate of change of the correction component. Generate dynamic response characteristic parameters, including update cycle parameters, time delay characteristic parameters, and correction component change rate parameters.

[0009] In a preferred embodiment, S4 specifically refers to: Based on the motion state solution model data, extract the actual motion state feature data at each sampling time point; Based on the dynamic response characteristic parameters, calculate the actual response time of the differential correction data at each sampling time point and the time offset between the sampling time point and the sampling time point; Based on the actual motion state characteristic data and time offset, calculate the synchronization offset metric at each sampling time point; Synchronization offset metrics at all sampling time points are summarized to generate synchronization offset data.

[0010] In a preferred embodiment, S5 specifically refers to: Based on synchronization offset data and time series trajectory prediction data, the actual solution position of the receiver and the theoretical prediction position of the corresponding sampling time point are determined at each sampling time point. Calculate the difference between the actual solution location and the theoretical predicted location at each sampling time point to generate the location residual for each sampling time point; Based on the position residuals of all sampling time points, sampling time points with position residual error values ​​lower than the normal expected error range are identified as pseudo-precision feature points. Each pseudo-precision feature point is marked and recorded, and a pseudo-precision feature point sequence is output.

[0011] In a preferred embodiment, S6 specifically refers to: Determine the sampling time point corresponding to each pseudo-precision feature point in the pseudo-precision feature point sequence; Based on time series trajectory prediction data, the theoretical position of the dynamic predicted trajectory corresponding to the sampling time point of each pseudo-precision feature point is extracted; The position residuals corresponding to the sampling time points of each pseudo-precision feature point in the pseudo-precision feature point sequence are statistically analyzed to generate a position residual distribution. Calculate the deviation of the positional residual of each pseudo-precision feature point from the theoretical position of the dynamically predicted trajectory; Based on the deviation, it is determined whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and a comprehensive detection result is output.

[0012] The technical effects and advantages of the high-precision receiver analog signal comprehensive detection method based on BeiDou satellites in this invention are as follows: 1. By acquiring satellite navigation signal data received by the receiver under simulated motion trajectory conditions, the motion state and signal characteristics of the receiver in a high-dynamic environment can be realistically reproduced. Based on pseudorange observations and carrier phase observations, a navigation solution reference trajectory and a high-dynamic trajectory evolution model are constructed, ensuring accurate reproduction of the receiver's real-time positioning results and providing a theoretical benchmark for comparison with the ideal motion trajectory. The evolution trend of differential corrections over time is analyzed and dynamic response characteristic parameters are extracted, enabling quantitative characterization of the correction release period, propagation delay, and rate of change of correction components. By jointly modeling the receiver's motion state and differential response characteristic parameters and constructing a synchronization offset metric, the time offset between the correction response and the motion state can be accurately measured. Based on synchronization offset data and theoretically predicted trajectory residual analysis, pseudo-precision feature points that cause abnormally high position solution values ​​due to response lag can be identified.

[0013] 2. By combining the high dynamic trajectory model and residual distribution to comprehensively judge all pseudo-precision feature points, it can accurately determine whether there is a risk of false detection due to differential data lag in the analog signal environment and form a comprehensive detection result, which greatly improves the accuracy and reliability of the evaluation of the differential positioning performance of high-precision Beidou receivers. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a high-precision receiver analog signal comprehensive detection method based on BeiDou satellite according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0016] Example Figure 1 This invention presents a method for comprehensive detection of analog signals from a high-precision receiver based on the BeiDou satellite system, comprising the following steps: S1: Acquire satellite navigation signal data received by the receiver under simulated motion trajectory conditions; S2: Based on pseudorange observations and carrier phase observations, construct navigation solution reference trajectory and high dynamic trajectory evolution model respectively, and output motion state solution model data and time series trajectory prediction data; S3: Analyze the evolution trend of the differential correction at continuous time points and extract the dynamic response characteristic parameters from the differential data; S4: Jointly model the motion state solution model data with the dynamic response characteristic parameters, construct the synchronization offset metric, and output the synchronization offset data; S5: Based on synchronization offset data and time series trajectory prediction data, analyze the residual between the actual solution position of the receiver and the expected trajectory at each sampling time point, and output a pseudo-precision feature point sequence; S6: Based on the pseudo-precision feature point sequence, combined with the high dynamic trajectory evolution model and residual distribution, determine whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and output the comprehensive detection result.

[0017] S1: Acquire satellite navigation signal data received by the receiver under simulated motion trajectory conditions, including: The Beidou high-precision receiver receives satellite navigation signal data broadcast by Beidou navigation satellites under pre-set simulated motion trajectory conditions; Based on the testing requirements of the high-precision receiver, technicians used trajectory simulation tools to set simulated motion trajectory conditions. These conditions included the specific motion mode, starting position, direction of motion, speed, acceleration, turning radius, and duration. For example, the simulated trajectory conditions were set as follows: the high-precision receiver moves eastward along a straight trajectory from its initial position with a constant acceleration of 3 meters per second squared, for a total duration of 120 seconds. This simulated trajectory setting allowed for the change of the receiver's position, speed, and acceleration at different points in time. Technicians then used a satellite navigation signal simulation generator to generate satellite navigation signal data based on the set simulated motion trajectory conditions. The satellite navigation signal simulation generator operates as follows: based on pre-set simulated motion trajectory conditions, it calculates the theoretical position coordinates of the high-precision receiver at each sampling time point; based on the theoretical position coordinates, it uses satellite ephemeris data, satellite clock bias data, and related propagation and error models, such as ionospheric delay models and tropospheric delay models, to generate satellite navigation signal data that is completely identical to the actual broadcast characteristics of the BeiDou satellite navigation system; the generated satellite navigation signal data includes pseudo-random code modulation signals and navigation message data broadcast by BeiDou navigation satellites; the pseudo-random code modulation signals include modulation frequency, code rate, carrier frequency, phase information, etc., and the navigation message data includes satellite ephemeris parameters, clock bias parameters, satellite state parameters, and system time, etc.; the satellite navigation signal data generated in the above manner is broadcast through the radio frequency channel, and the high-precision receiver receives the satellite navigation signal data broadcast through the radio frequency channel through the receiving antenna.

[0018] The received satellite navigation signal data is classified to obtain pseudorange observations, carrier phase observations, and differential correction data; After receiving satellite navigation signal data, the high-precision receiver uses its internal data processing unit to demodulate and decode the data. Specifically, this involves: carrier stripping and code phase measurement of the pseudo-random code modulation signal in the satellite navigation signal data to obtain pseudorange observations; the pseudorange observations are the difference between the code phase of the satellite navigation signal data and the reference code phase generated locally by the receiver, used for navigation and positioning calculations; the data processing unit performs carrier phase measurement on the satellite navigation signal data through a carrier tracking loop to obtain carrier phase observations, which are the difference between the carrier signal phase in the received satellite navigation signal data and the local carrier signal phase; simultaneously, the high-precision receiver extracts differential correction data from the satellite navigation signal data. This differential correction data includes satellite clock error corrections, satellite orbit error corrections, ionospheric delay corrections, and tropospheric delay corrections. This differential data is broadcast in real-time by the BeiDou navigation satellites in the form of navigation messages and received in real-time by the high-precision receiver, used to correct the receiver's positioning calculation results.

[0019] S2: Based on pseudorange and carrier phase observations, construct navigation solution reference trajectory and high-dynamic trajectory evolution models respectively, outputting motion state solution model data and time series trajectory prediction data, including: The navigation solution reference trajectory model uses pseudorange observations and carrier phase observations to calculate the position using the standard satellite navigation positioning algorithm, generating navigation solution reference trajectory data for the receiver at each sampling time point; The high-precision receiver's data processing unit acquires pseudorange and carrier phase observations. The data processing unit uses standard satellite navigation positioning algorithms to calculate these observations, including pseudorange point positioning and carrier phase differential positioning algorithms. Taking pseudorange point positioning as an example, the data processing unit calculates the spatial coordinates of each BeiDou navigation satellite based on navigation message data broadcast by the BeiDou navigation satellites. The data processing unit uses the pseudorange observations and satellite spatial coordinates to establish a set of observation equations. Specifically, the pseudorange observations from the receiver to each BeiDou navigation satellite are equal to the spatial geometric distance between the receiver's position coordinates and the satellite's position coordinates, plus the pseudorange error term caused by the receiver's clock bias. The data processing unit... The observation equations are solved using mathematical methods, such as the least squares method or Kalman filtering, to obtain the receiver's three-dimensional spatial position coordinates. Taking the carrier phase differential positioning algorithm as an example: the carrier phase differential positioning algorithm establishes a set of carrier phase observation equations between the reference station and the station under test, and uses double-difference carrier phase observations to eliminate satellite clock errors, receiver clock errors, tropospheric delay errors, and ionospheric delay errors, thus calculating the receiver's position coordinates at each sampling time point. The high-precision receiver's data processing unit executes the above navigation standard positioning algorithm at each sampling time point and records the navigation solution position data corresponding to each sampling time point. The continuous navigation solution position data constitutes the output data of the navigation solution reference trajectory model, i.e., the navigation solution reference trajectory data.

[0020] The high-dynamic trajectory evolution model generates a dynamically predicted trajectory based on pre-set simulated motion trajectory conditions using kinematic trajectory calculation methods; Using kinematic trajectory calculation methods, kinematic trajectory extrapolation calculations are performed on pre-set simulated motion trajectory conditions. For example, the simulated motion trajectory conditions are set as follows: the receiver moves in a straight line with constant acceleration from the initial coordinate position, the initial velocity is zero, the acceleration is 3 meters per second squared, the direction of motion is due east, and the motion time is 120 seconds. The kinematic trajectory calculation method extrapolates the trajectory based on the basic kinematic formulas, including but not limited to: displacement equals initial velocity multiplied by motion time plus half multiplied by acceleration and then multiplied by the square of motion time; velocity equals initial velocity plus acceleration multiplied by motion time. By performing second-by-second calculations using the above kinematic trajectory calculation methods, the theoretical position coordinates of the receiver at each sampling time point are obtained. The theoretical position coordinates at all sampling time points continuously form a dynamic predicted trajectory.

[0021] The navigation solution reference trajectory data is transformed to generate motion state solution model data, including position, velocity and acceleration data obtained from navigation solution; The position coordinates of each sampling time point are extracted from the navigation solution reference trajectory data. The velocity of the receiver at each sampling time point is calculated by dividing the difference in position coordinates between adjacent sampling time points by the sampling time interval. The acceleration of the receiver at each sampling time point is calculated by dividing the difference in velocity between adjacent sampling time points by the sampling time interval. After the above data transformation, the navigation solution reference trajectory data is expanded into motion state solution model data that includes position, velocity and acceleration data.

[0022] The dynamically predicted trajectory is transformed into time series trajectory prediction data, including the discretized trajectory location point sequence output by the dynamic prediction trajectory model; A fixed sampling interval is set on the dynamically predicted trajectory, such as one sampling point per second. The specific position coordinates are extracted from the continuous trajectory data according to the set sampling interval. All the sampled position coordinates form a continuous discrete position sequence, and the discrete position sequence constitutes the time series trajectory prediction data. The time series trajectory prediction data reflects the theoretical position of the receiver at each discrete sampling time point.

[0023] S3: Analyze the evolution trend of the differential correction at continuous time points, and extract the dynamic response characteristic parameters from the differential data, including: The differential correction data is divided into continuous time series to obtain differential correction data for multiple time series intervals; After receiving satellite navigation signal data, the high-precision receiver extracts differential correction data from the satellite navigation signal data through the data processing unit. This differential correction data is data broadcast in real-time by the BeiDou navigation satellites and received in real-time by the high-precision receiver to improve positioning accuracy. In the time dimension, it is represented as a data sequence that continuously updates with the sampling time. Based on the sampling time, the continuously updated differential correction data is segmented according to a set time interval. The segmentation method is as follows: the length of each time interval is set to a fixed time interval, for example, 10 seconds per time interval. Therefore, a continuous time sequence of 120 seconds is divided into 12 consecutive time sequence intervals. Each time sequence interval contains differential correction data at each sampling time point within the corresponding interval. For example, the first time sequence interval contains differential correction data within the range of 0 to 10 seconds, the second time sequence interval contains differential correction data within the range of 10 to 20 seconds, and so on. Through this continuous time sequence division, differential correction data for multiple time sequence intervals is obtained.

[0024] Statistical analysis was performed on the update interval of the differential correction data within each time series interval to obtain the update cycle parameter; Record the actual update time of the differential correction data within each time series interval, i.e., the specific time when the BeiDou navigation satellite broadcasts the differential correction data in real time. For example, in the first time series interval, the differential correction data is broadcast and updated at 1 second, 3 seconds, 5 seconds, 7 seconds, and 9 seconds, respectively, so the update intervals are 2 seconds, 2 seconds, 2 seconds, and 2 seconds, respectively. Statistically calculate all update intervals within the interval, and calculate the average update interval and the fluctuation range of the update interval. The average update interval is the average value of the update intervals, for example, the calculated result is 2 seconds. The fluctuation range of the update interval is the difference between the maximum and minimum values ​​of the interval. For example, if both the maximum and minimum values ​​are 2 seconds, then the fluctuation range of the update interval is 0 seconds. The above calculation results are the update cycle parameters of the differential correction data.

[0025] Statistical analysis was performed on the time delay between the actual broadcast time and the actual reception time of the differential correction data in each time series interval to obtain the time delay characteristic parameters. Record the actual broadcast time of the differential correction data within each time series interval, i.e., when the BeiDou navigation satellite broadcasts the differential correction data. Simultaneously record the actual reception time of the corresponding differential correction data received by the high-precision receiver. For example, if the actual broadcast time of the differential correction data is 10.0 seconds and the actual reception time of the high-precision receiver is 10.3 seconds, then the delay time is 0.3 seconds. Perform statistical analysis on the delay time values ​​of all differential correction data within each interval to calculate the average, maximum, minimum, and standard deviation of the delay time within the time series interval. The above statistical analysis results form the time delay characteristic parameters. Calculate and record the corresponding time delay characteristic parameters for each time series interval.

[0026] Calculate the rate of change of the error correction component in the differential correction data within each time series interval to obtain the parameter of the rate of change of the correction component. The error correction components include satellite clock error correction, satellite orbit error correction, ionospheric delay correction, and tropospheric delay correction. Taking the ionospheric delay correction as an example, if the ionospheric delay correction is 1.2 meters at the 1st second and 1.5 meters at the 3rd second within a certain time series interval, then the rate of change of the ionospheric delay correction within the time interval is (1.5 meters - 1.2 meters) divided by (3 seconds - 1 second), which is 0.15 meters per second. For each error correction component, the rate of change within each time series interval is calculated, and the average, maximum, and minimum values ​​of the rate of change of each error correction component within each interval are statistically analyzed. The above statistical calculation results form the correction component rate of change parameter.

[0027] Generate dynamic response characteristic parameters, including update cycle parameters, time delay characteristic parameters, and correction component change rate parameters; The update cycle parameter, time delay characteristic parameter, and correction component change rate parameter are collectively summarized into a complete data set and defined as dynamic response characteristic parameter.

[0028] S4: Jointly model the motion state solution model data with the dynamic response characteristic parameters to construct a synchronization offset metric and output synchronization offset data, including: Based on the motion state solution model data, extract the actual motion state feature data at each sampling time point; The motion state solution model data is generated from the navigation solution reference trajectory data after data transformation, including position data, velocity data, and acceleration data obtained from navigation solution. Position data consists of the receiver's three-dimensional spatial coordinates at each sampling time point, such as X, Y, and Z coordinates expressed in a geocentric coordinate system. Velocity data is the receiver's three-axis velocity component information at each sampling time point, obtained through differential calculation of position data; that is, the velocity values ​​along the X, Y, and Z axes, for example, 3 meters per second in the X-axis direction, 0 meters per second in the Y-axis direction, and 0 meters per second in the Z-axis direction. Acceleration data is the receiver's three-axis acceleration component information at each sampling time point, obtained through differential calculation of velocity data. For example, the acceleration in the X-axis direction is 3 meters per second squared, the acceleration in the Y-axis direction is 0 meters per second squared, and the acceleration in the Z-axis direction is 0 meters per second squared. Data is extracted from the motion state solution model data at each sampling time point, and the position data, velocity data and acceleration data are mapped to form the actual motion state feature data at each sampling time point. For example, at the sampling time point of the 10th second, the actual motion state feature data is: position coordinates X is 500 meters, Y is 0 meters, Z is 0 meters, velocity X is 30 meters per second, Y is 0 meters per second, Z is 0 meters per second, and acceleration X is 3 meters per second squared, Y is 0 meters per second squared, Z is 0 meters per second squared. The actual motion state feature data at each sampling time point is extracted in the above way.

[0029] Based on the dynamic response characteristic parameters, calculate the actual response time of the differential correction data at each sampling time point and the time offset between the sampling time point and the sampling time point; The update period parameter, time delay parameter, and correction component change rate parameter are extracted from the dynamic response characteristic parameters. The update period parameter represents the average update interval of the differential correction data within each time interval; for example, the update period parameter is 2 seconds in the first interval. The time delay parameter represents the statistical delay between the actual broadcast and reception times of the differential correction data; for example, the average delay is 0.3 seconds within a certain time interval. The correction component change rate parameter describes the rate of change of the error correction component; for example, the rate of change of the ionospheric delay correction is 0.15 meters per second. Based on the dynamic response characteristic parameters, the actual response time of the differential correction data at each sampling time point is calculated. The calculation method is as follows: the actual broadcast time of the differential correction data is summed with the statistically obtained average delay. For example, at the 10-second sampling time point, the actual broadcast time of the differential correction data is 10 seconds, and the average delay time is 0.3 seconds, so the actual response time is 10.3 seconds. The time offset between the actual response time and the corresponding sampling time point is calculated by subtracting the sampling time point from the actual response time. For example, if the actual response time is 10.3 seconds and the sampling time point is 10 seconds, the time offset is 0.3 seconds. The time offset of each sampling time point is calculated using the above method.

[0030] Based on the actual motion state characteristic data and time offset, calculate the synchronization offset metric at each sampling time point; The synchronization offset metric represents the degree of matching between the differential correction data response time and the actual motion state of the receiver. First, velocity and acceleration data are extracted at each sampling time point. For example, at the 10-second sampling time point, the velocity data is 30 meters per second and the acceleration data is 3 meters per second squared. Simultaneously, the time offset at this sampling point is extracted as 0.3 seconds. The synchronization offset metric is calculated by using velocity and acceleration to calculate the change in position of the receiver within the time offset. Specifically, the change in position equals the velocity data multiplied by the time offset, plus half of that multiplied by the acceleration data, and then multiplied by the square of the time offset. For example, the change in position at the 10-second sampling point is (30 m / s × 0.3 seconds) plus (half × 3 m / s). 2 ×0.3 seconds 2 The calculated result is 9.135 meters; the above position change is the synchronization offset metric, which describes the position deviation caused by the deviation of the actual response time of the differential correction data from the sampling time; the synchronization offset metric is calculated for all sampling time points using the above method.

[0031] The synchronization offset metrics at all sampling time points are summarized to generate synchronization offset data; The data aggregation method involves creating a data table, using each sampling time point as the row label and the synchronization offset metric as the table data. For example, in the data table, the synchronization offset metric corresponding to the sampling point at the 10th second might be 9.135 meters, while the synchronization offset metric corresponding to the sampling point at the 20th second might be 18.27 meters, and so on, generating synchronization offset data. This synchronization offset data describes the impact of differential correction data response delay on the receiver position deviation during the test.

[0032] S5: Based on synchronization offset data and time-series trajectory prediction data, analyze the residual between the actual solution position of the receiver and the expected trajectory at each sampling time point, and output a pseudo-precision feature point sequence, including: Based on synchronization offset data and time series trajectory prediction data, the actual solution position of the receiver and the theoretical prediction position of the corresponding sampling time point are determined at each sampling time point. Synchronization offset data is a metric for each sampling time point, representing the positional offset of the differential correction data's actual response time relative to the sampling time point. For example, the synchronization offset metric at the 10-second sampling time point is 9.135 meters. Simultaneously, time-series trajectory prediction data is acquired, including theoretical position coordinates at each sampling time point, specifically a discrete sequence of trajectory position points. For example, at the 10-second sampling time point, the theoretical predicted position coordinates are X = 509.135 meters, Y = 0 meters, and Z = 0 meters. The actual calculated position and the theoretical predicted position are extracted at each sampling time point, with the actual calculated position being the position obtained from the high-precision receiver. The position is obtained by combining pseudorange observations and carrier phase observations with differential correction data through a navigation solution algorithm. For example, the actual solution position coordinates at the 10th second are 500 meters X, 0 meters Y, and 0 meters Z. The theoretical predicted position is the theoretical position coordinates obtained by kinematic trajectory calculation based on preset simulated motion trajectory conditions. The actual solution position and the theoretical predicted position are correspondingly formed into position pairs at each sampling time point. For example, at the sampling time point of the 10th second, the actual solution position (500 meters, 0 meters, 0 meters) and the theoretical predicted position (509.135 meters, 0 meters, 0 meters) form a corresponding data pair. The above position pairs are determined at each sampling time point.

[0033] Calculate the difference between the actual solution location and the theoretical predicted location at each sampling time point to generate the location residual for each sampling time point; Vector subtraction is used to calculate the difference between the actual calculated position and the theoretical predicted position along the X, Y, and Z axes. For example, if the actual calculated position coordinates at the 10-second sampling time point are 500 meters X, 0 meters Y, and 0 meters Z, and the corresponding theoretical predicted position coordinates are 509.135 meters X, 0 meters Y, and 0 meters Z, then the position residual is calculated as follows: X-axis direction (500 meters - 509.135 meters) = -9.135 meters, Y-axis direction (0 meters - 0 meters) = 0 meters, and Z-axis direction (0 meters - 0 meters) = 0 meters. The calculated position differences in the X, Y, and Z directions constitute the position residuals. Three-dimensional distance calculations are performed on these position residuals. The method for calculating the three-dimensional distance of the position residuals is as follows: add the squares of the position differences in the X, Y, and Z directions respectively, and then take the square root. For example, the three-dimensional distance of the position residual at the sampling time point of the 10th second is calculated as the square root of (-9.135 meters squared plus 0 meters squared plus 0 meters squared), resulting in 9.135 meters. The above three-dimensional distance of the position residuals represents the spatial deviation between the actual calculated position and the theoretically predicted position. Difference calculations are performed at each sampling time point, and the position residual at each sampling time point is recorded.

[0034] Based on the position residuals of all sampling time points, sampling time points with position residual error values ​​lower than the normal expected error range are identified as pseudo-precision feature points. The normal expected error range is determined in advance by technicians based on the performance of the navigation system and the differential positioning system. For example, the normal expected error range is determined to be ±5 meters to ±10 meters. The system then checks whether the position residual at each sampling time point is below the normal expected error range, i.e., whether it is less than the minimum limit of the normal expected error. For example, a position residual of 9.135 meters is within the normal expected error range. If the position residual at a sampling point is lower than the minimum limit of the normal expected error (e.g., 1 meter), it indicates that the position residual at the sampling point is lower than the normal positioning error level, suggesting an abnormal underestimation of the position error. Sampling time points with position residuals lower than the normal error level are identified as pseudo-precision feature points. Pseudo-precision feature points represent special sampling time points where the differential correction data does not fully reflect the error characteristics due to delay response, resulting in an abnormally reduced receiver position error. For example, sampling time points at 30 seconds, 60 seconds, and 90 seconds with abnormally lower position residuals than the normal expected error range are identified as pseudo-precision feature points.

[0035] Each pseudo-precision feature point is marked and recorded, and a sequence of pseudo-precision feature points is output. The sampling time point, actual calculated position coordinates, theoretical predicted position coordinates, and position residual are recorded in the data recording table for each pseudo-precision feature point. For example, the sampling point at the 30th second is identified as a pseudo-precision feature point and marked as: sampling time 30 seconds, actual calculated position coordinates (X1500 meters, Y0 meters, Z0 meters), theoretical predicted position coordinates (X1510 meters, Y0 meters, Z0 meters), and position residual of 10 meters, which is within the normal expected error range. Similarly, the pseudo-precision feature point information for the sampling time points at the 60th and 90th seconds is recorded. All marked and recorded pseudo-precision feature point information is integrated into a pseudo-precision feature point sequence. The pseudo-precision feature point sequence reveals the abnormal underestimation of error caused by the delayed response of differential correction data under dynamic motion conditions.

[0036] S6: Based on the pseudo-precision feature point sequence, combined with the high-dynamic trajectory evolution model and residual distribution, determine whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and output the comprehensive detection results, including: Determine the sampling time point corresponding to each pseudo-precision feature point in the pseudo-precision feature point sequence; A pseudo-precision feature point sequence is a set of special sampling time points identified at multiple sampling time points where the position error is abnormally lower than the expected normal error range. For example, after identification and marking, sampling time points at 30 seconds, 60 seconds, and 90 seconds are recorded as pseudo-precision feature points, and the recorded content includes the actual calculated position coordinates, theoretical predicted position coordinates, and position residuals corresponding to each pseudo-precision feature point. The sampling time point corresponding to each pseudo-precision feature point in the pseudo-precision feature point sequence is determined one by one. For example, the first pseudo-precision feature point corresponds to the sampling time point at 30 seconds, the second pseudo-precision feature point corresponds to the sampling time point at 60 seconds, and the third pseudo-precision feature point corresponds to the sampling time point at 90 seconds. Through the above method, the correspondence between all feature points and sampling time points in the pseudo-precision feature point sequence is obtained.

[0037] Based on time series trajectory prediction data, the theoretical position of the dynamic predicted trajectory corresponding to the sampling time point of each pseudo-precision feature point is extracted; The time-series trajectory prediction data consists of theoretical position coordinates at each sampling time point, represented as a continuous discrete sequence of trajectory position points. For example, the theoretical predicted position coordinates at the 30-second sampling time point are X900m, Y0m, Z0m; at the 60-second sampling time point, they are X3600m, Y0m, Z0m; and at the 90-second sampling time point, they are X8100m, Y0m, Z0m. Based on the sampling time points corresponding to pseudo-precision feature points, the theoretical predicted position coordinates corresponding to each pseudo-precision feature point's sampling time point are extracted. For example, after determining that the 30-second point is a pseudo-precision feature point, the corresponding theoretical predicted position coordinates (X900m, Y0m, Z0m) are extracted; after determining that the 60-second point is a pseudo-precision feature point, the corresponding theoretical predicted position coordinates (X3600m, Y0m, Z0m) are extracted; similarly, after determining that the 90-second point is a pseudo-precision feature point, the corresponding theoretical predicted position coordinates (X8100m, Y0m, Z0m) are extracted.

[0038] The position residuals corresponding to the sampling time points of each pseudo-precision feature point in the pseudo-precision feature point sequence are statistically analyzed to generate a position residual distribution. The positional residual is the three-dimensional spatial distance difference between the actual calculated position and the theoretical predicted position at each sampling time point corresponding to a pseudo-precision feature point. For example, the positional residual is 2.5 meters at the 30-second sampling time point, 3.8 meters at the 60-second sampling time point, and 1.9 meters at the 90-second sampling time point. Statistical analysis of the positional residual is performed on each pseudo-precision feature point, including calculating the average, maximum, and minimum positional residuals, as well as the standard deviation of the positional residuals. For example, statistical analysis is performed on the positional residuals at the 30-second, 60-second, and 90-second sampling time points in the pseudo-precision feature point sequence. The calculated average positional residual is 2.73 meters, the maximum is 3.8 meters (corresponding to 60 seconds), the minimum is 1.9 meters (corresponding to 90 seconds), and the standard deviation is 0.78 meters. After the statistical analysis is completed, the positional residual distribution is generated and presented in the form of a data chart, such as a distribution graph with positional residuals on the horizontal axis and frequency or sampling point sequence number on the vertical axis.

[0039] Calculate the deviation of the positional residual of each pseudo-precision feature point from the theoretical position of the dynamically predicted trajectory; The deviation of the position residual of each pseudo-precision feature point relative to the theoretical position coordinates of the dynamically predicted trajectory is calculated. The deviation is the relative percentage of the position residual on the theoretical position coordinates of the dynamically predicted trajectory. The calculation method is as follows: divide the position residual by the total displacement distance between the corresponding theoretical position of the dynamically predicted trajectory and the starting point of the initial trajectory, and then multiply by 100%. For example, if the position residual at the 30-second sampling time point is 2.5 meters and the total displacement of the corresponding theoretical position of the dynamically predicted trajectory is 900 meters, then the deviation is calculated as (2.5 meters ÷ 900 meters) × 100% ≈ 0.278%. Similarly, if the position residual at the 60-second sampling time point is 3.8 meters and the total displacement is 3600 meters, the deviation is (3.8 meters ÷ 3600 meters) × 100% ≈ 0.106%. If the position residual at the 90-second sampling time point is 1.9 meters and the total displacement is 8100 meters, the deviation is (1.9 meters ÷ 8100 meters) × 100% ≈ 0.023%.

[0040] Based on the deviation, determine whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and output a comprehensive detection result; The deviation of all pseudo-precision feature points is calculated. Based on the magnitude of the deviation, it is determined whether there is a risk of false detection due to differential response lag. The judgment criteria are pre-set as follows: pseudo-precision feature points with a deviation exceeding 0.1% are considered feature points with a risk of false detection due to differential response lag. For example, if the deviation at the 30-second sampling time point is 0.278% (greater than 0.1%), the pseudo-precision feature point is considered to have a risk of false detection; if the deviation at the 60-second sampling time point is 0.106% (greater than 0.1%), the pseudo-precision feature point is considered to have a risk of false detection; and if the deviation at the 90-second sampling time point is 0.023% (less than 0.1%), the pseudo-precision feature point is considered to have no risk of false detection. The presence of a false detection risk in pseudo-precision feature points is a local point-like error characteristic. The presence of a false detection risk caused by differential response lag in satellite navigation signal data is a holistic judgment conclusion obtained based on a comprehensive analysis of all pseudo-precision feature points. During a 120-second satellite navigation signal data test, three pseudo-precision feature points were identified. Two of these pseudo-precision feature points posed a risk of false detection, while one did not. The percentage of pseudo-precision feature points with a risk of false detection was calculated, for example, to be 66.7%. A criterion was established to determine whether the satellite navigation signal data had a risk of false detection due to differential response lag. For example, the criterion was set as follows: if the percentage of feature points with a risk of false detection exceeds 50% of the total number of pseudo-precision feature points, then the satellite navigation signal data was considered to have a risk of false detection due to differential response lag; otherwise, it was considered that the satellite navigation signal data did not have a risk of false detection due to differential response lag. Based on the criterion and the statistically analyzed result of 66.7% of the feature points with a risk of false detection, a determination was made that the satellite navigation signal data had a risk of false detection due to differential response lag, and a comprehensive detection result was generated. The comprehensive detection results include the identification of pseudo-precision feature points, individual analysis conclusions on the false detection risk of pseudo-precision feature points, overall statistical analysis of the false detection risk of pseudo-precision feature points, and the overall false detection risk assessment results for satellite navigation signal data. The identification of pseudo-precision feature points includes the sampling time point information of all identified pseudo-precision feature points. The individual analysis conclusions on the false detection risk of pseudo-precision feature points include the calculated deviation for each pseudo-precision feature point and the corresponding false detection risk assessment results. The overall statistical analysis of the false detection risk of pseudo-precision feature points includes the number and proportion of pseudo-precision feature points with false detection risk among all pseudo-precision feature points. The overall false detection risk assessment results for satellite navigation signal data indicate whether differential response lag in the satellite navigation signal data causes false detection risk.

[0041] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0042] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. 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, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0043] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0044] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0045] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0046] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0048] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0049] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims. Finally: The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for comprehensive detection of analog signals from a high-precision receiver based on the BeiDou satellite system, characterized in that, Includes the following steps: S1: Acquire satellite navigation signal data received by the receiver under simulated motion trajectory conditions; S2: Based on pseudorange observations and carrier phase observations, construct navigation solution reference trajectory and high dynamic trajectory evolution model respectively, and output motion state solution model data and time series trajectory prediction data; S3: Analyze the evolution trend of the differential correction at continuous time points, and extract the dynamic response characteristic parameters from the differential data, specifically: The differential correction data is divided into continuous time series to obtain differential correction data for multiple time series intervals; Statistical analysis was performed on the update interval of the differential correction data within each time series interval to obtain the update cycle parameter; Statistical analysis was performed on the time delay between the actual broadcast time and the actual reception time of the differential correction data in each time series interval to obtain the time delay characteristic parameters. Calculate the rate of change of the error correction component in the differential correction data within each time series interval to obtain the parameter of the rate of change of the correction component. Generate dynamic response characteristic parameters, including update cycle parameters, time delay characteristic parameters, and correction component change rate parameters; S4: Jointly model the motion state solution model data with the dynamic response characteristic parameters to construct a synchronization offset metric and output synchronization offset data, specifically: Based on the motion state solution model data, extract the actual motion state feature data at each sampling time point; Based on the dynamic response characteristic parameters, calculate the actual response time of the differential correction data at each sampling time point and the time offset between the sampling time point and the sampling time point; Based on the actual motion state characteristic data and time offset, calculate the synchronization offset metric at each sampling time point; The synchronization offset metrics at all sampling time points are summarized to generate synchronization offset data; S5: Based on synchronization offset data and time series trajectory prediction data, analyze the residual between the actual solution position of the receiver and the expected trajectory at each sampling time point, and output a pseudo-precision feature point sequence; S6: Based on the pseudo-precision feature point sequence, combined with the high dynamic trajectory evolution model and residual distribution, determine whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and output the comprehensive detection result.

2. The method for comprehensive detection of analog signals from a high-precision receiver based on BeiDou satellites according to claim 1, characterized in that, S1, specifically: The Beidou high-precision receiver receives satellite navigation signal data broadcast by Beidou navigation satellites under pre-set simulated motion trajectory conditions; The received satellite navigation signal data is classified to obtain pseudorange observations, carrier phase observations, and differential correction data.

3. The method for comprehensive detection of analog signals from a high-precision receiver based on BeiDou satellite as described in claim 2, characterized in that, S2, specifically: The navigation solution reference trajectory model uses pseudorange observations and carrier phase observations to calculate the position using the standard satellite navigation positioning algorithm, generating navigation solution reference trajectory data for the receiver at each sampling time point; The high-dynamic trajectory evolution model generates a dynamically predicted trajectory based on pre-set simulated motion trajectory conditions using kinematic trajectory calculation methods; The navigation solution reference trajectory data is transformed to generate motion state solution model data, including position, velocity and acceleration data obtained from navigation solution; The dynamically predicted trajectory is transformed into time series trajectory prediction data, including the discretized trajectory location point sequence output by the dynamic prediction trajectory model.

4. The method for comprehensive detection of analog signals from a high-precision receiver based on BeiDou satellite as described in claim 3, characterized in that, S5, specifically: Based on synchronization offset data and time series trajectory prediction data, the actual solution position of the receiver and the theoretical prediction position of the corresponding sampling time point are determined at each sampling time point. Calculate the difference between the actual solution location and the theoretical predicted location at each sampling time point to generate the location residual for each sampling time point; Based on the position residuals of all sampling time points, sampling time points with position residual error values ​​lower than the normal expected error range are identified as pseudo-precision feature points. Each pseudo-precision feature point is marked and recorded, and a pseudo-precision feature point sequence is output.

5. The method for comprehensive detection of analog signals from a high-precision receiver based on BeiDou satellites according to claim 4, characterized in that, S6, specifically: Determine the sampling time point corresponding to each pseudo-precision feature point in the pseudo-precision feature point sequence; Based on time series trajectory prediction data, the theoretical position of the dynamic predicted trajectory corresponding to the sampling time point of each pseudo-precision feature point is extracted; The position residuals corresponding to the sampling time points of each pseudo-precision feature point in the pseudo-precision feature point sequence are statistically analyzed to generate a position residual distribution. Calculate the deviation of the positional residual of each pseudo-precision feature point from the theoretical position of the dynamically predicted trajectory; Based on the deviation, it is determined whether there is a risk of false detection caused by differential response lag in the satellite navigation signal data, and a comprehensive detection result is output.

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