Real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle positioning performance test and metering calibration, in particular to an unmanned aerial vehicle real-time dynamic positioning error estimation and analysis system, which comprises a reference construction module, a calculation module and a calculation module, controlling a tracker arranged on the first base station to aim at a positioning plane prism arranged on the second base station to execute measurement, and determining a current measurement coordinate system based on the known coordinates and the measurement result; the error calibration module is used for controlling the unmanned aerial vehicle to hover above the third base station based on the measurement coordinate system and known coordinates of the third base station, controlling the tracker to measure the position of the unmanned aerial vehicle to obtain first truth value data, synchronously obtaining self-positioning data of the unmanned aerial vehicle and GNSS data collected by the fourth base station, and outputting the first truth value data and the second truth value data; calculating a static hovering positioning error based on the time-aligned first true value data and the self-positioning data; according to the invention, the global known coordinate precision of the base station is directly transmitted to the measurement coordinate system of the tracker, so that the reliability of measurement data is ensured.

Description

Technical Field

[0001] This invention relates to the field of UAV positioning performance testing and metrological calibration technology, specifically to a UAV real-time dynamic positioning error estimation and analysis system. Background Technology

[0002] With the widespread application of drones in surveying, inspection, logistics, agriculture, and other fields, their positioning accuracy directly affects the quality and safety of operations. Currently, drones mostly use GNSS (Global Navigation Satellite System) and Inertial Navigation System (INS) for positioning. However, in complex environments, they are susceptible to multipath effects, satellite signal blockage, weather conditions, and other factors, resulting in positioning errors. Existing technologies for evaluating drone positioning accuracy often rely on post-flight analysis, downloading log data and performing error statistics after the flight, lacking real-time and dynamic capabilities. Furthermore, the evaluation process often lacks a high-precision spatial reference standard, leading to insufficient reliability of the evaluation results.

[0003] Traditional testing methods often use static reference stations or long baseline fields for comparison, but these methods are complex to deploy, costly, and difficult to implement in real-time error monitoring of UAVs during dynamic flight. Therefore, there is an urgent need for a system capable of real-time, high-precision evaluation of UAV dynamic positioning errors, providing technical support for the calibration, performance verification, and error compensation of UAV positioning systems. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles (UAVs).

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles, comprising:

[0006] The baseline construction module acquires the known coordinates of the first, second, and third base stations, controls the tracker set at the first base station to aim at the positioning plane prism set at the second base station to perform measurement, and determines the measurement coordinate system based on the known coordinates and measurement results.

[0007] Error calibration module: Based on the known coordinates of the measurement coordinate system and the third base station, control the UAV to hover above the third base station, control the tracker to measure the position of the UAV to obtain the first true value data, and simultaneously obtain the self-positioning data of the UAV and the GNSS data collected by the fourth base station. Based on the time-aligned first true value data and self-positioning data, calculate the static hovering positioning error.

[0008] Error analysis module: Based on the static hovering positioning error, the module makes a judgment. According to the judgment result, the tracker is controlled to aim at the positioning plane prism again to verify the stability of the measurement coordinate system. Then, the UAV is controlled to perform dynamic flight. During dynamic flight, the tracker is controlled to measure the position of the UAV to obtain the second true value data, and simultaneously acquires the UAV self-positioning data, the fourth base station GNSS data and meteorological data. The dynamic positioning error is calculated based on the second true value data.

[0009] In a preferred embodiment, the reference construction module controls a tracker located at a first base station to aim at a positioning plane prism located at a second base station based on the known coordinates, and obtains the first measurement result of the tracker on the positioning plane prism, the first measurement result including raw observation data of angle and distance;

[0010] Based on the known coordinates of the second base station and the original observation data contained in the first measurement result, and based on the original angle and distance data in the first measurement result, the first coordinate of the positioning plane prism in the tracker's own measurement coordinate system is calculated. Based on the known coordinates of the second base station in the global coordinate system, the second coordinate of the plane prism in the global coordinate system is determined. A set of coordinate transformation parameters for transforming the tracker's own measurement coordinate system to the global coordinate system is determined. The coordinate transformation parameters are stored as the measurement parameters of the tracker for this measurement, thereby establishing a measurement coordinate system with the first base station as the reference and aligned with the global coordinate system.

[0011] In a preferred embodiment, the error calibration module ground control center calculates the coordinates of the spatial target point where the UAV should hover based on the measurement coordinate system and the obtained coordinates of the third base station. The ground control center sends take-off and hovering commands to the UAV flight control system, and the commands include the coordinates of the spatial target point.

[0012] The drone is controlled to fly and hover stably at the target point. The ground control center controls the laser tracker to switch to automatic tracking mode and locks the 365° prism on the bottom of the drone. The laser tracker continuously measures the three-dimensional coordinates of the prism in the measurement coordinate system at a high frequency, which serves as the first true data stream of the drone's position.

[0013] The ground control center simultaneously acquires the following data:

[0014] The drone position ground truth data stream from the laser tracker includes the instrument's own timestamp and corresponding 3D coordinate values;

[0015] The self-localization data stream from the UAV flight control system includes the flight control system timestamp and the corresponding three-dimensional coordinate values;

[0016] The observation data stream from the fourth base station GNSS receiver includes receiver timestamps and satellite status parameters;

[0017] An environmental data stream from a weather sensor, which includes sensor timestamps and parameters such as temperature, air pressure, and humidity.

[0018] The ground control center uses the Network Time Protocol as a unified time reference, corrects the timestamps carried by the above data to this unified reference, and generates a synchronized data sequence with the same series of time tags.

[0019] For each time tag in the synchronized data sequence, the ground control center performs a point-by-point comparison operation to extract the true coordinates of the UAV's position measured by the laser tracker under that time tag from the synchronized data sequence, and at the same time extract the positioning coordinates given by the UAV's self-localization system under that time tag.

[0020] Next, calculate the instantaneous positioning error of the UAV in the horizontal direction at that moment. This error value is the straight-line distance between the true coordinates and the self-positioning coordinates on the horizontal plane. Calculate the instantaneous positioning error of the UAV in the elevation direction at that moment. This error value is the absolute difference between the two in the elevation direction.

[0021] Simultaneously calculate the three-dimensional instantaneous positioning error of the UAV at that moment. The error value is the straight-line distance between the two in three-dimensional space. Arrange the above-mentioned instantaneous errors corresponding to all time tags in chronological order to form a horizontal instantaneous error sequence, an elevation instantaneous error sequence, and a three-dimensional instantaneous error sequence.

[0022] The ground control center performed statistical analysis on the three instantaneous error sequences obtained. For the horizontal instantaneous error sequence, its mean was calculated to reflect the central trend of the sequence, and its standard deviation was calculated to reflect the dispersion of the sequence. At the same time, the maximum and minimum values ​​in the sequence were found to reflect the extreme range of the sequence. The same statistical calculation was performed on the elevation instantaneous error sequence and the three-dimensional instantaneous error sequence to obtain their respective mean, standard deviation, maximum and minimum values.

[0023] The ground control center will compile all the parameters obtained from the above statistical calculations, including the average, standard deviation, maximum and minimum values ​​of horizontal, vertical and three-dimensional errors, along with basic test information such as total hovering time, data acquisition frequency, and tracker measurement uncertainty, according to the preset report format, and finally generate a structured static positioning error assessment report.

[0024] The ground control center extracts GNSS observation data corresponding to all time tags from the synchronized data sequence. This data includes the number of visible satellites, position accuracy attenuation factor, signal-to-noise ratio of each satellite, and satellite constellation distribution. After extraction, the data is preprocessed to remove unreasonable data points.

[0025] The ground control center calculates key quality indicators based on the extracted GNSS observation data. First, it calculates the average number of visible satellites, the average position accuracy attenuation factor, and the distribution ratio of signal-to-noise ratio in different intensity ranges during the entire observation period. Second, it analyzes the characteristics of each indicator changing over time and identifies and records key events during the observation process where the number of satellites is lower than the set lower limit or the position accuracy attenuation factor is higher than the set upper limit.

[0026] The ground control center will compile the calculated key quality indicators and identified key events according to the preset report format to generate a GNSS data quality report.

[0027] In a preferred embodiment, the error analysis module ground control center reads the static positioning error assessment report of the UAV above the third base station and the synchronously collected GNSS data quality report, and makes a judgment based on preset rules;

[0028] The specific judgment steps are as follows:

[0029] The laser tracker is temporarily interrupted from tracking the UAV. The positioning plane prism of the second base station is described again, and a quick verification measurement value is obtained. The value is compared with the known coordinates of the second base station. If the deviation is within the tolerance, the measurement coordinate system is confirmed to be stable. If the deviation exceeds the tolerance, the coordinate system reconstruction operation is re-executed.

[0030] Regardless of whether the judgment is executed, the ground control center sends a dynamic flight execution to the UAV. During the UAV's dynamic flight, the execution is repeated and the laser tracker's true value data, the UAV's automatic data, the fourth base station's GNSS data, and meteorological data are continuously acquired.

[0031] During the dynamic flight phase of the UAV, the ground control center repeats the data alignment and instantaneous error calculation steps similar to those in the static error assessment to generate horizontal instantaneous error sequences, elevation instantaneous error sequences, and three-dimensional instantaneous error sequences during dynamic flight. Each error value corresponds to a synchronized time tag.

[0032] The ground control center extracts multi-parameter time series that completely correspond to the dynamic flight period, including dynamic error series, satellite state parameter series, meteorological parameter series, and UAV motion state series;

[0033] The ground control center establishes a graph with time as the horizontal axis and error value and associated parameter value as the vertical axis. In the graph, the dynamic error curve is plotted on the main vertical axis, and the selected associated parameter curve is plotted on the secondary vertical axis. Through visual observation, the peak points where the error is significantly higher than the average level are marked on the error curve, and the values ​​of the associated parameter curves at the corresponding times are checked. This allows for a qualitative analysis of the synchronization relationship between the error peak and the associated parameter anomalies. At the same time, by calculating the correlation coefficient between the dynamic error sequence and each associated parameter sequence, the degree of linear correlation between them is quantitatively analyzed.

[0034] The ground control center multiplies the corresponding two centered values ​​at each same time point in the dynamic error centered sequence and the correlation parameter centered sequence, thus obtaining a new sequence composed of the product values ​​at each time point, called the covariance sequence. Then, the sum of squares of all values ​​in the dynamic error centered sequence and the sum of squares of all values ​​in the correlation parameter centered sequence are calculated respectively. The sum of squares of the dynamic error centered sequence is divided by the total number of data points minus one, and then the square root operation is performed on the result to obtain the standard deviation of the dynamic error sequence. The standard deviation of the correlation parameter sequence is calculated in the same way to obtain the standard deviation of the dynamic error sequence and the standard deviation of the correlation parameter sequence.

[0035] The ground control center first calculates the sum of all values ​​in the covariance sequence, then divides this sum by the total number of data points minus one to obtain an intermediate result. The standard deviation of the calculated dynamic error sequence is multiplied by the standard deviation of the correlation parameter sequence, and finally the aforementioned intermediate result is divided by this product. The final value obtained is the correlation coefficient between the dynamic error sequence and the correlation parameter sequence.

[0036] When the absolute value of the correlation coefficient is close to one, it indicates that the linear correlation between the two sequences is strong. When the absolute value is close to zero, it indicates that the linear correlation is weak. Based on the calculated correlation coefficient value falling within the preset range, the ground control center makes a qualitative judgment on the linear correlation between the two phenomena and uses this conclusion as part of the correlation analysis report.

[0037] The ground control center adds annotations to the plotted curves, including the error values ​​and corresponding associated parameter values ​​at the error peak points, the legends of each curve, and the calculated key correlation coefficients. Finally, the fully annotated positioning error curve is stored as an image file and output along with the calculated correlation coefficients as a visualization result of the dynamic error analysis.

[0038] The beneficial effects of this invention are as follows: This invention utilizes a positioning plane prism on a second base station to calibrate the tracker on a first base station, directly transferring the globally known coordinate accuracy of the base station to the measurement coordinate system of the tracker. This ensures the reliability of the measurement data and the credibility of the results. It acquires the basic positioning error in a static hovering state, assesses the initial performance of the UAV positioning system, and then intelligently determines whether the measurement benchmark needs to be re-verified based on the static error results. This ensures the stability of all subsequent coordinate systems and prevents systematic deviations introduced by minor equipment offsets. Through time synchronization processing, these data are aligned on the time axis, enabling in-depth correlation analysis. This not only calculates the numerical value of the positioning error but also correlates the error's trend with real-time changes in position signal quality and fluctuations in weather conditions, helping to distinguish and identify the main influencing factors causing the positioning error. Attached Figure Description

[0039] Figure 1 This is a system block diagram of the present invention;

[0040] Figure 2 This is an overall layout diagram of the present invention;

[0041] Figure 3 This is a structural diagram of the 360° small prism mounted on the drone of the present invention;

[0042] Figure 4 This is a three-dimensional structural diagram of the tracking device of the present invention;

[0043] Figure 5 This is a structural diagram of the positioning plane prism of the present invention;

[0044] Figure 6 This is a structural diagram of the GNSS measurement receiver of the present invention;

[0045] Figure 7 This is a structural diagram of the meteorological sensor of the present invention.

[0046] In the diagram: 1-4, four base stations of the ultra-short baseline field; 5, weather sensor; 6, UAV; 7, tracker; 8, positioning plane prism; 9, GNSS measurement receiver; 601, 360-degree small prism; 602, UAV camera; 603, UAV data transmission antenna; 701, tracker power switch; 702, laser emitter; 703, laser detection camera; 704, display screen; 705, tracker mounting base; 801, plane prism cross-shaped focusing area; 802, leveling bubble; 803, leveling knob; 804, bottom bolt; 901, GNSS measurement receiver base; 902, GNSS measurement receiver switch and display screen. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0049] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0050] like Figures 1-7 This embodiment provides: a real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles (UAVs), comprising:

[0051] The baseline construction module acquires the known coordinates of the first, second, and third base stations, controls the tracker set at the first base station to aim at the positioning plane prism set at the second base station to perform measurement, and determines the measurement coordinate system based on the known coordinates and measurement results.

[0052] In this embodiment of the invention, the reference construction module needs to be specifically described. Based on the known coordinates, the reference construction module controls the tracker set at the first base station to aim at the positioning plane prism set at the second base station, and obtains the first measurement result of the tracker on the positioning plane prism. The first measurement result includes the original observation data of angle and distance.

[0053] Based on the known coordinates of the second base station and the original observation data contained in the first measurement result, and based on the original angle and distance data in the first measurement result, the first coordinate of the positioning plane prism in the tracker's own measurement coordinate system is calculated. Based on the known coordinates of the second base station in the global coordinate system, the second coordinate of the plane prism in the global coordinate system is determined. A set of coordinate transformation parameters for transforming the tracker's own measurement coordinate system to the global coordinate system is determined. The coordinate transformation parameters are stored as the measurement parameters of the tracker for this measurement, thereby establishing a measurement coordinate system with the first base station as the reference and aligned with the global coordinate system.

[0054] It should be noted that the first base station is equipped with a high-precision laser tracker, which is equipped with a moving target recognition and tracking function, built-in angle and distance measurement sensors, and can transmit measurement data in real time through wired or wireless networks.

[0055] Second base station: A positioning plane prism is fixedly installed. The prism serves as a passive cooperative target, and the position of its center in the base station coordinate system is precisely calibrated.

[0056] The third base station serves as a spatial location reference point. This base station has a clear visual marker on its top, and its center coordinates are known. The airspace above this base station is the designated hovering position for drones.

[0057] The fourth base station is equipped with a fixed GNSS measurement receiver, which can receive and decode multi-frequency satellite navigation signals in real time, obtain raw observation values ​​(pseudorange, carrier phase) and satellite status information (number of satellites, signal ratio, PDOP value, etc.), and transmit them through the network.

[0058] Unmanned Aerial Vehicle Platform: Equipped with a flight control system, a GNSS receiving antenna, and a 360-degree reflecting prism. The spatial offset between the center of the prism and the phase center of the UAV's GNSS antenna has been determined through pre-calibration.

[0059] Error calibration module: Based on the known coordinates of the measurement coordinate system and the third base station, control the UAV to hover above the third base station, control the tracker to measure the position of the UAV to obtain the first true value data, and simultaneously obtain the self-positioning data of the UAV and the GNSS data collected by the fourth base station. Based on the time-aligned first true value data and self-positioning data, calculate the static hovering positioning error.

[0060] In this embodiment of the invention, the error calibration module needs to be specifically described. Based on the measurement coordinate system and the obtained coordinates of the third base station, the ground control center calculates the coordinates of the spatial target point where the UAV should hover. The ground control center sends take-off and hovering commands to the UAV flight controller, and the commands include the coordinates of the spatial target point.

[0061] The drone is controlled to fly and hover stably at the target point. The ground control center controls the laser tracker to switch to automatic tracking mode and locks the 365° prism on the bottom of the drone. The laser tracker continuously measures the three-dimensional coordinates of the prism in the measurement coordinate system at a high frequency, which serves as the first true data stream of the drone's position.

[0062] The ground control center simultaneously acquires the following data:

[0063] The drone position ground truth data stream from the laser tracker includes the instrument's own timestamp and corresponding 3D coordinate values;

[0064] The self-localization data stream from the UAV flight control system includes the flight control system timestamp and the corresponding three-dimensional coordinate values;

[0065] The observation data stream from the fourth base station GNSS receiver includes receiver timestamps and satellite status parameters;

[0066] An environmental data stream from a weather sensor, which includes sensor timestamps and parameters such as temperature, air pressure, and humidity.

[0067] The ground control center uses the Network Time Protocol as a unified time reference, corrects the timestamps carried by the above data to this unified reference, and generates a synchronized data sequence with the same series of time tags.

[0068] For each time tag in the synchronized data sequence, the ground control center performs a point-by-point comparison operation to extract the true coordinates of the UAV's position measured by the laser tracker under that time tag from the synchronized data sequence, and at the same time extract the positioning coordinates given by the UAV's self-localization system under that time tag.

[0069] Next, calculate the instantaneous positioning error of the UAV in the horizontal direction at that moment. This error value is the straight-line distance between the true coordinates and the self-positioning coordinates on the horizontal plane. Calculate the instantaneous positioning error of the UAV in the elevation direction at that moment. This error value is the absolute difference between the two in the elevation direction.

[0070] Simultaneously calculate the 3D instantaneous positioning error of the UAV at that moment, which is the straight-line distance between the two in 3D space. Arrange the above-mentioned instantaneous errors corresponding to all time tags in chronological order to form a horizontal instantaneous error sequence, an elevation instantaneous error sequence, and a 3D instantaneous error sequence;

[0071] The ground control center performed statistical analysis on the three instantaneous error sequences obtained. For the horizontal instantaneous error sequence, its mean was calculated to reflect the central trend of the sequence, and its standard deviation was calculated to reflect the dispersion of the sequence. At the same time, the maximum and minimum values ​​in the sequence were found to reflect the extreme range of the sequence. The same statistical calculation was performed on the elevation instantaneous error sequence and the three-dimensional instantaneous error sequence to obtain their respective mean, standard deviation, maximum and minimum values.

[0072] The ground control center will compile all the parameters obtained from the above statistical calculations, including the average, standard deviation, maximum and minimum values ​​of horizontal, vertical and three-dimensional errors, along with basic test information such as total hovering time, data acquisition frequency, and tracker measurement uncertainty, according to the preset report format, and finally generate a structured static positioning error assessment report.

[0073] The ground control center extracts GNSS observation data corresponding to all time tags from the synchronized data sequence. This data includes the number of visible satellites, position accuracy attenuation factor, signal-to-noise ratio of each satellite, and satellite constellation distribution. After extraction, the data is preprocessed to remove unreasonable data points, such as invalid data with zero satellites or excessively large position accuracy attenuation factors.

[0074] The ground control center calculates key quality indicators based on the extracted GNSS observation data. First, it calculates the average number of visible satellites, the average position accuracy attenuation factor, and the distribution ratio of signal-to-noise ratio in different intensity ranges during the entire observation period. Second, it analyzes the characteristics of each indicator over time, such as the frequency of changes in the number of satellites and the degree of fluctuation of the position accuracy attenuation factor. At the same time, it identifies and records key events during the observation process where the number of satellites is lower than the set lower limit or the position accuracy attenuation factor is higher than the set upper limit.

[0075] The ground control center organizes the calculated key quality indicators and identified key events according to the preset report format to generate a GNSS data quality report. This report includes at least the observation period and equipment information, overall quality assessment, detailed indicator analysis table, key event record table, and suggestions for subsequent tests. After the report is generated, it is stored in a designated location and displayed in conjunction with the static positioning error assessment report.

[0076] The key events include:

[0077] Insufficient Satellite Count Event: This event is recorded when the number of visible satellites calculated or observed in real time is lower than the preset minimum satellite count threshold required to maintain effective positioning. This threshold is usually set to be greater than or equal to 4, but can be specifically set according to the actual positioning model used (such as whether elevation constraints are required). This event directly indicates that the geometric conditions for positioning are insufficient, which may lead to positioning failure or a sharp drop in accuracy.

[0078] Positioning accuracy factor exceeding limit event: When the value of any of the position accuracy reduction factor or similar geometric accuracy attenuation factor, horizontal accuracy attenuation factor, or vertical accuracy attenuation factor that reflects the quality of satellite spatial geometry exceeds the corresponding preset upper limit value, this event is recorded. This event indicates that even if the number of satellites is sufficient, their spatial distribution may lead to a large positioning error amplification effect.

[0079] Severe signal quality deterioration event: When the average carrier signal ratio of a specific satellite or all satellites is observed to be lower than the preset quality lower limit threshold, this event is recorded. A low signal-to-noise ratio usually means weak signal strength, severe interference or severe obstruction, which will directly affect the quality of the observations, especially the cycle slip detection and repair of carrier phase observations.

[0080] Specific observation data missing event: This event is recorded when pseudorange or carrier phase observations at a specific frequency point are continuously missing within a preset continuous observation period, or when the observation data stream of a specific satellite is interrupted. This may be due to signal obstruction, receiver channel failure, or severe interference, which will affect multi-frequency joint calculation and ionospheric error elimination.

[0081] Satellite health status abnormality event: When the health status of one or more satellites is determined to be unhealthy or unusable based on the navigation message information decoded by the GNSS receiver, the event is recorded. Using unhealthy satellites for positioning calculations will introduce system errors.

[0082] Frequent cycle slip events: When the algorithm detects multiple cycle slips in the carrier phase observation value within a short period of time during data processing, and the frequency of these slips exceeds the preset normal level, this event is recorded. Cycle slips will disrupt the continuity of the phase observation value, and if the repair is unsuccessful, it will seriously affect the high-precision positioning results.

[0083] Data integrity interruption event: This event is recorded when the data stream from the GNSS receiver itself experiences an unexpected and complete interruption that exceeds the allowed duration. This indicates that there may be a failure in the data transmission link or the receiver itself.

[0084] Error analysis module: Based on the static hovering positioning error, the module makes a judgment. According to the judgment result, the tracking device is controlled to aim at the positioning plane prism again to verify the stability of the measurement coordinate system. Then, the UAV is controlled to perform dynamic flight. During dynamic flight, the tracking device is controlled to measure the position of the UAV to obtain the second true value data, and simultaneously acquires the UAV self-positioning data, the fourth base station GNSS data and meteorological data. The dynamic positioning error is calculated based on the second true value data.

[0085] In this embodiment of the invention, the error analysis module needs to be specifically described. The ground control center reads the static positioning error assessment report of the UAV above the third base station and the synchronously collected GNSS data quality report, and makes a judgment based on preset rules.

[0086] The specific judgment steps are as follows:

[0087] The laser tracker is temporarily interrupted from tracking the UAV. The positioning plane prism of the second base station is described again, and a quick verification measurement value is obtained. The value is compared with the known coordinates of the second base station. If the deviation is within the tolerance, the measurement coordinate system is confirmed to be stable. If the deviation exceeds the tolerance, the coordinate system reconstruction operation is re-executed.

[0088] Regardless of whether the judgment is executed, the ground control center sends a dynamic flight execution to the UAV. During the UAV's dynamic flight, the execution is repeated and the laser tracker's true value data, the UAV's automatic data, the fourth base station's GNSS data, and meteorological data are continuously acquired.

[0089] During the dynamic flight phase of the UAV, the ground control center repeats the data alignment and instantaneous error calculation steps similar to those in the static error assessment to generate horizontal instantaneous error sequences, elevation instantaneous error sequences, and three-dimensional instantaneous error sequences during dynamic flight. Each error value corresponds to a synchronized time tag.

[0090] The ground control center extracts multi-parameter time series that completely correspond to the dynamic flight period, including dynamic error series, satellite status parameter series (such as number of satellites, position accuracy attenuation factor, average signal-to-noise ratio), meteorological parameter series (temperature, air pressure, humidity), and UAV motion status series (horizontal speed, vertical speed, pitch angle, roll angle). The time labels of these series have been aligned with a unified time reference to ensure that each parameter is strictly synchronized in time.

[0091] The ground control center establishes a graph with time as the horizontal axis and error value and associated parameter value as the vertical axis. In the graph, the dynamic error curve is plotted on the main vertical axis, and the selected associated parameter curve is plotted on the secondary vertical axis. Through visual observation, the peak points where the error is significantly higher than the average level are marked on the error curve, and the values ​​of the associated parameter curves at the corresponding times are checked. This allows for a qualitative analysis of the synchronization relationship between the error peak and the associated parameter anomalies. At the same time, by calculating the correlation coefficient between the dynamic error sequence and each associated parameter sequence, the degree of linear correlation between them is quantitatively analyzed.

[0092] The ground control center multiplies the corresponding two centered values ​​at each same time point in the dynamic error centered sequence and the correlation parameter centered sequence, thus obtaining a new sequence composed of the product values ​​at each time point, called the covariance sequence. Then, the sum of squares of all values ​​in the dynamic error centered sequence and the sum of squares of all values ​​in the correlation parameter centered sequence are calculated respectively. The sum of squares of the dynamic error centered sequence is divided by the total number of data points minus one, and then the square root operation is performed on the result to obtain the standard deviation of the dynamic error sequence. The standard deviation of the correlation parameter sequence is calculated in the same way to obtain the standard deviation of the dynamic error sequence and the standard deviation of the correlation parameter sequence.

[0093] The ground control center first calculates the sum of all values ​​in the covariance sequence, then divides this sum by the total number of data points minus one to obtain an intermediate result. The standard deviation of the calculated dynamic error sequence is multiplied by the standard deviation of the correlation parameter sequence, and finally the aforementioned intermediate result is divided by this product. The final value obtained is the correlation coefficient between the dynamic error sequence and the correlation parameter sequence.

[0094] When the absolute value of the correlation coefficient is close to one, it indicates that the linear correlation between the two sequences is strong. When the absolute value is close to zero, it indicates that the linear correlation is weak. Based on the calculated correlation coefficient values ​​falling within a preset range (such as high correlation, medium correlation, low correlation), the ground control center makes a qualitative judgment on the degree of linear correlation between the two phenomena and uses this conclusion as part of the correlation analysis report.

[0095] The ground control center adds annotations to the plotted curves, including the error values ​​and corresponding associated parameter values ​​at the error peak points, the legends of each curve, and the calculated key correlation coefficients. Finally, the fully annotated positioning error curve is stored as an image file and output along with the calculated correlation coefficients as a visualization result of the dynamic error analysis.

[0096] Example 1:

[0097] The test site was set up with an ultra-short baseline field with sides of 50 meters. The coordinates of the four base stations (1-4) had been precisely determined through measurement. A laser tracker (accuracy 0.1mm) was installed at base station 1, a plane prism for calibration was installed at base station 2, base station 3 served as the drone's hovering point, and a dual-frequency GNSS measurement receiver was installed at base station 4. The drone was a DJI Matrice 300 RTK, with a 360-degree small prism mounted on its bottom using a special clamp. The prism's center and the drone's RTK antenna phase center had been calibrated and compensated. A temperature, pressure, and humidity integrated meteorological sensor was also deployed on site.

[0098] Implementation steps:

[0099] Start the ground data processing system software and input the coordinates of the four base stations. Control the tracker to automatically aim at the plane prism of base station No. 2 to complete the coordinate system orientation.

[0100] Commands were sent via ground station software to control the drone to take off and automatically hover directly above base station No. 3 at a height of 10 meters.

[0101] Switch the tracker to automatic tracking mode, lock the 360-degree prism at the bottom of the drone, and output the three-dimensional coordinates of the prism center at a frequency of 10Hz.

[0102] The GNSS receiver outputs satellite count, PDOP value, signal-to-noise ratio, and raw observation data at a frequency of 1 Hz; the UAV flight controller outputs its own RTK positioning coordinates at a frequency of 10 Hz; and the meteorological sensor outputs temperature, pressure, and humidity data at a frequency of 1 Hz. All data is transmitted to the ground system via a 4G module.

[0103] After receiving the data, the ground system first performs time synchronization (using an NTP network), and then transforms all coordinates to the ultra-short baseline field coordinate system. Using the coordinates measured by the tracker as the true position value, it calculates the time-by-time difference with the UAV RTK positioning coordinates and GNSS single-point positioning coordinates to obtain the horizontal error, elevation error, and three-dimensional error. Simultaneously, the system plots the error versus time curves in real time and displays the current satellite status and meteorological parameters.

[0104] In a 5-minute hovering test, the system successfully performed real-time error analysis. The output showed that the UAV's RTK positioning horizontal error was approximately ±1 cm and the vertical error was approximately ±2 cm, with the errors showing a correlation with changes in the satellite signal-to-noise ratio. After the test, the system generated an error statistics report, including indicators such as mean error, standard deviation, and maximum error.

[0105] The above embodiments verify that the system of the present invention can achieve real-time and high-precision estimation of the dynamic positioning error of UAVs, providing an effective tool for performance evaluation, calibration and algorithm improvement of UAV positioning systems.

[0106] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0107] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0112] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: The baseline construction module acquires the known coordinates of the first, second, and third base stations, controls the tracker set at the first base station to aim at the positioning plane prism set at the second base station to perform measurement, and determines the measurement coordinate system based on the known coordinates and measurement results. Error calibration module: Based on the known coordinates of the measurement coordinate system and the third base station, control the UAV to hover above the third base station, control the tracker to measure the position of the UAV to obtain the first true value data, and simultaneously obtain the self-positioning data of the UAV and the GNSS data collected by the fourth base station. Based on the time-aligned first true value data and self-positioning data, calculate the static hovering positioning error. Error analysis module: Based on the static hovering positioning error, the module makes a judgment. According to the judgment result, the tracker is controlled to aim at the positioning plane prism again to verify the stability of the measurement coordinate system. Then, the UAV is controlled to perform dynamic flight. During dynamic flight, the tracker is controlled to measure the position of the UAV to obtain the second true value data, and simultaneously acquires the UAV self-positioning data, the fourth base station GNSS data and meteorological data. The dynamic positioning error is calculated based on the second true value data.

2. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 1, characterized in that, Based on the known coordinates, the reference construction module controls the tracker set at the first base station to aim at the positioning plane prism set at the second base station, and obtains the first measurement result of the tracker on the positioning plane prism. The first measurement result includes the raw observation data of angle and distance. Based on the known coordinates of the second base station and the original observation data contained in the first measurement result, and based on the original angle and distance data in the first measurement result, the first coordinate of the positioning plane prism in the tracker's own measurement coordinate system is calculated. Based on the known coordinates of the second base station in the global coordinate system, the second coordinate of the plane prism in the global coordinate system is determined. A set of coordinate transformation parameters for transforming the tracker's own measurement coordinate system to the global coordinate system is determined. The coordinate transformation parameters are stored as the measurement parameters of the tracker for this measurement, thereby establishing a measurement coordinate system with the first base station as the reference and aligned with the global coordinate system.

3. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 1, characterized in that, The error calibration module, based on the measurement coordinate system and the obtained coordinates of the third base station, calculates the coordinates of the spatial target point where the UAV should hover. The ground control center then sends take-off and hovering commands to the UAV flight controller, which include the coordinates of the spatial target point. The drone is controlled by non-aerial flight control and hovers stably at the target point. The ground control center controls the laser tracker to switch to automatic tracking mode and locks the 365° prism on the bottom of the drone. The laser tracker continuously measures the three-dimensional coordinates of the prism in the measurement coordinate system at high frequency, which serves as the first true data stream of the drone's position.

4. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 3, characterized in that, The ground control center simultaneously acquires the following data: The drone position ground truth data stream from the laser tracker includes the instrument's own timestamp and corresponding 3D coordinate values; The self-localization data stream from the UAV flight control system includes the flight control system timestamp and the corresponding three-dimensional coordinate values; The observation data stream from the fourth base station GNSS receiver includes receiver timestamps and satellite status parameters; An environmental data stream from a weather sensor, which includes sensor timestamps and parameters such as temperature, air pressure, and humidity.

5. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 4, characterized in that, The ground control center uses the Network Time Protocol as a unified time reference, corrects the timestamps carried by the above data to this unified reference, and generates a synchronized data sequence with the same series of time tags. For each time tag in the synchronized data sequence, the ground control center performs a point-by-point comparison operation to extract the true coordinates of the UAV's position measured by the laser tracker under that time tag from the synchronized data sequence, and at the same time extract the positioning coordinates given by the UAV's self-localization system under that time tag. Next, calculate the instantaneous positioning error of the UAV in the horizontal direction at that moment. This error value is the straight-line distance between the true coordinates and the self-positioning coordinates on the horizontal plane. Calculate the instantaneous positioning error of the UAV in the elevation direction at that moment. This error value is the absolute difference between the two in the elevation direction. Simultaneously, the three-dimensional instantaneous positioning error of the UAV at that moment is calculated. This error value is the straight-line distance between the two in three-dimensional space. All the above-mentioned instantaneous errors corresponding to all time tags are arranged in chronological order to form a horizontal instantaneous error sequence, an elevation instantaneous error sequence, and a three-dimensional instantaneous error sequence.

6. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 5, characterized in that, The ground control center performed statistical analysis on the three instantaneous error sequences obtained. For the horizontal instantaneous error sequence, its mean was calculated to reflect the central trend of the sequence, and its standard deviation was calculated to reflect the dispersion of the sequence. At the same time, the maximum and minimum values ​​in the sequence were found to reflect the extreme range of the sequence. The same statistical calculation was performed on the elevation instantaneous error sequence and the three-dimensional instantaneous error sequence to obtain their respective mean, standard deviation, maximum and minimum values. The ground control center will compile all the parameters obtained from the above statistical calculations, including the average, standard deviation, maximum and minimum values ​​of horizontal, vertical and three-dimensional errors, along with basic test information such as total hovering time, data acquisition frequency, and tracker measurement uncertainty, according to the preset report format, and finally generate a structured static positioning error assessment report. The ground control center extracts GNSS observation data corresponding to all time tags from the synchronized data sequence. This data includes the number of visible satellites, position accuracy attenuation factor, signal-to-noise ratio of each satellite, and satellite constellation distribution. After extraction, the data is preprocessed to remove unreasonable data points.

7. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 6, characterized in that, The ground control center calculates key quality indicators based on the extracted GNSS observation data. First, it calculates the average number of visible satellites, the average position accuracy attenuation factor, and the distribution ratio of signal-to-noise ratio in different intensity ranges during the entire observation period. Second, it analyzes the characteristics of each indicator changing over time and identifies and records key events during the observation process where the number of satellites is lower than the set lower limit or the position accuracy attenuation factor is higher than the set upper limit. The ground control center will compile the calculated key quality indicators and identified key events according to the preset report format to generate a GNSS data quality report.

8. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 1, characterized in that, The error analysis module reads the static positioning error assessment report of the UAV above the third base station and the synchronously collected GNSS data quality report from the ground control center, and makes a judgment based on preset rules; The specific judgment steps are as follows: The laser tracker is temporarily interrupted from tracking the UAV. The positioning plane prism of the second base station is described again, and a quick verification measurement value is obtained. The value is compared with the known coordinates of the second base station. If the deviation is within the tolerance, the measurement coordinate system is confirmed to be stable. If the deviation exceeds the tolerance, the coordinate system reconstruction operation is re-executed. Regardless of whether the judgment is executed, the ground control center sends a dynamic flight execution to the UAV. During the UAV's dynamic flight, the execution is repeated and the laser tracker's true value data, the UAV's automatic data, the fourth base station's GNSS data, and meteorological data are continuously acquired. During the dynamic flight phase of the UAV, the ground control center repeats the data alignment and instantaneous error calculation steps similar to those in the static error assessment to generate horizontal instantaneous error sequences, elevation instantaneous error sequences, and three-dimensional instantaneous error sequences during dynamic flight. Each error value corresponds to a synchronized time tag. The ground control center extracts multi-parameter time series that completely correspond to the dynamic flight period, including dynamic error series, satellite state parameter series, meteorological parameter series, and UAV motion state series.

9. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 8, characterized in that, The ground control center establishes a graph with time as the horizontal axis and error value and associated parameter value as the vertical axis. In the graph, the dynamic error curve is plotted on the main vertical axis, and the selected associated parameter curve is plotted on the secondary vertical axis. Through visual observation, the peak points where the error is significantly higher than the average level are marked on the error curve, and the values ​​of the associated parameter curves at the corresponding times are checked. This allows for a qualitative analysis of the synchronization relationship between the error peak and the associated parameter anomalies. At the same time, by calculating the correlation coefficient between the dynamic error sequence and each associated parameter sequence, the degree of linear correlation between them is quantitatively analyzed. The ground control center multiplies the corresponding two centered values ​​at each same time point in the dynamic error centered sequence and the correlation parameter centered sequence, thus obtaining a new sequence composed of the product values ​​at each time point, called the covariance sequence. Then, the sum of squares of all values ​​in the dynamic error centered sequence and the sum of squares of all values ​​in the correlation parameter centered sequence are calculated respectively. The sum of squares of the dynamic error centered sequence is divided by the total number of data points minus one, and then the square root operation is performed on the result to obtain the standard deviation of the dynamic error sequence. The standard deviation of the correlation parameter sequence is also calculated to obtain the standard deviation of the dynamic error sequence and the standard deviation of the correlation parameter sequence. The ground control center first calculates the sum of all values ​​in the covariance sequence, then divides this sum by the total number of data points minus one to obtain an intermediate result. The standard deviation of the calculated dynamic error sequence is multiplied by the standard deviation of the correlation parameter sequence, and finally the aforementioned intermediate result is divided by this product. The final value obtained is the correlation coefficient between the dynamic error sequence and the correlation parameter sequence.

10. The real-time dynamic positioning error estimation and analysis system for unmanned aerial vehicles according to claim 9, characterized in that, When the absolute value of the correlation coefficient is close to one, it indicates that the linear correlation between the two sequences is strong. When the absolute value is close to zero, it indicates that the linear correlation is weak. Based on the calculated correlation coefficient values ​​falling within the preset range, the ground control center makes a qualitative judgment on the degree of linear correlation between the two phenomena and includes this conclusion as part of the correlation analysis report.

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