Method and system for measuring and calibrating flight path of low-altitude unmanned aerial vehicle

Abstract

The invention relates to the crossing field of an unmanned aerial vehicle technology and a precision measurement technology, in particular to a low-altitude unmanned aerial vehicle flight path measurement calibration method and system, and the method comprises the steps: building a unified coordinate system; locking an unmanned aerial vehicle target and acquiring measurement data; judging whether the unmanned aerial vehicle target is lost; when it is judged that the unmanned aerial vehicle target is lost, unmanned aerial vehicle positioning system data are transmitted back to relock the target; processing the measurement data; and analyzing the flight error of the unmanned aerial vehicle based on the processed data. According to the embodiment of the invention, the high-precision total station and the unmanned aerial vehicle RTK positioning system are adopted for cooperative measurement, the problem of signal loss is effectively solved, a multi-dimensional system error analysis model is innovatively introduced, systematic errors, RTK positioning drifting and other error sources can be automatically identified and corrected, a complete measurement-compensation-verification closed-loop system is established, and the measurement accuracy is improved. And flight path calibration and reliability verification of the unmanned aerial vehicle in a complex environment are supported.

Description

Technical Field

[0001] The present invention relates to the intersection of UAV technology and precision measurement technology, and in particular to a low-altitude UAV flight trajectory measurement and calibration method and system. Background Art

[0002] Precision measurement radar is a radar system capable of accurately determining target coordinates and trajectory in real time. It has important applications in military, aerospace, and other fields. In ballistic missile range measurement, this radar system can perform tasks such as range navigation zone safety, missile thrust assessment, rocket stage separation, multiple warhead relative position measurement, and reentry point measurement. To determine the accuracy of precision measurement radar systems, a verification method using a corner reflector mounted on an unmanned aerial vehicle (UAV) is currently commonly used. This technical solution uses a small UAV as a controllable mobile target. A high-precision flight control system accurately replicates a preset trajectory, enabling the radar system to continuously track and measure the target. By establishing a spatial and temporal synchronous correlation between radar measurement data and a UAV reference trajectory, quantitative assessment of radar system measurement errors and parameter calibration are possible. Compared to traditional target verification methods, UAV verification technology offers full-latitude testing capabilities across multiple ranges, angles, and altitude levels, and offers advantages such as greater flexibility and lower cost. However, this technology still faces several technical challenges, such as the accuracy of the UAV's flight trajectory and the complexity of data synchronization and calibration. Currently, UAV trajectory measurement mainly relies on the positioning information provided by the global navigation and positioning system on board. There is an urgent need to establish a high-precision independent measurement and calibration system for UAV flight trajectories. Summary of the Invention

[0003] One of the purposes of the present invention is to provide a low-altitude UAV flight trajectory measurement calibration method and system to solve the problem of low UAV trajectory measurement accuracy in the prior art.

[0004] In order to achieve the above object, the present invention provides a low-altitude UAV flight trajectory measurement and calibration method, comprising:

[0005] Establish a unified coordinate system;

[0006] Lock the drone target and obtain measurement data;

[0007] Determine whether the drone target is lost;

[0008] If the drone loses its target, it will send back the drone positioning system data to re-lock the target;

[0009] processing the measurement data;

[0010] Based on the processed data, the flight errors of the UAV are analyzed.

[0011] Optionally, establishing a unified coordinate system includes:

[0012] The total station is set up on a known high-precision control point, and the true north direction is obtained through astronomical orientation to complete the station setting.

[0013] Optionally, when it is determined that the drone target is lost, transmitting the drone positioning system data back to re-lock the target includes:

[0014] Send back positioning system location data through drones;

[0015] The position data is converted into spatial rectangular coordinates according to formula (1) to formula (5),

[0016] X=(N+H)cosBcosL, (1)

[0017] Y=(N+H)cosBsinL, (2)

[0018] Z=[N(1-e 2 )+H]sinB, (3)

[0019]

[0020] Among them, X, Y, Z are spatial rectangular coordinates, B is latitude, L is longitude, H is elevation, N is the radius of curvature of the ellipsoid, e 2 is the square of the first eccentricity of the ellipsoid, a is the major semi-axis, and b is the minor semi-axis;

[0021] The spatial rectangular coordinates are converted into spherical coordinates according to formula (6) and formula (7),

[0022]

[0023]

[0024] Among them, θ is the zenith angle, Φ is the azimuth angle;

[0025] The target is re-locked according to the spherical coordinates.

[0026] Optionally, processing the measurement data includes:

[0027] The measurement data is preprocessed to remove abnormal values.

[0028] Optionally, processing the measurement data includes:

[0029] The measurement data is resampled, comprising:

[0030] The measured data is segmented and fitted according to formula (8),

[0031] S(t)=at 3 +bt 2 +ct+d, (8)

[0032] Where S(t) is a piecewise cubic polynomial, a, b, c, d are polynomial coefficients, and t is the time variable;

[0033] Ensure that the first and second order derivatives of adjacent segments are continuous at the connection points, and solve the polynomial coefficients through the matrix equation to obtain the interpolation position at any time t.

[0034] Optionally, analyzing the flight error of the UAV based on the processed data includes visual comparison, where the visual comparison includes:

[0035] Plotting two-dimensional and three-dimensional trajectories of the measurement data and the UAV positioning system data to obtain path consistency;

[0036] Plot the change curves of the X, Y, and Z axes of the measurement data and the UAV positioning system data over time, and compare the offset in a specific direction.

[0037] Optionally, analyzing the flight error of the UAV based on the processed data includes calculating statistical indicators, wherein the statistical indicator calculation includes:

[0038] According to formula (9), the Euclidean distance difference at each time point is calculated.

[0039]

[0040] Where Δd is the difference in Euclidean distance between point (x1, y1, z1) and point (x2, y2, z2);

[0041] Calculate the root mean square error according to formula (10):

[0042]

[0043] Where RMSE is the root mean square error, Δd i is the Euclidean distance difference of the i-th sample, and n is the number of samples;

[0044] The mean absolute error is calculated according to formula (11):

[0045]

[0046] Among them, MAE is the mean absolute error.

[0047] Optionally, analyzing the flight error of the UAV based on the processed data includes system error analysis, and the system error analysis includes:

[0048] Calculate the error value at each time point according to formula (12):

[0049] Δ i =P RTK (t i )-P total-station (t i ), (12)

[0050] Among them, Δ i At time point t i The error value at P RTK (t i ) is at time point t i UAV positioning data at P total-station (t i ) is the time point t i Measurement data at

[0051] Calculate the sliding mean according to formula (13):

[0052]

[0053] Among them, μ i At time point t i The sliding mean at, k is the radius of the sliding window, P total-station (t j ) is at time t j Measurement data at

[0054] Performing regression model fitting on the error value and the sliding mean at each time point to obtain a linear model and a nonlinear model;

[0055] Calculating the coefficients of determination of the linear model and the nonlinear model respectively;

[0056] Selecting the linear model or the nonlinear model as the optimal model according to the determination coefficient;

[0057] It is determined whether there is a system error in the current system according to the optimal model.

[0058] Optionally, determining whether the current system has a systematic error according to the optimal model includes:

[0059] The t-value statistic of the slope of the optimal model is calculated according to formula (14):

[0060]

[0061] Where t is the t-value statistic of the slope, a is the slope, SE a is the standard error of the slope;

[0062] Calculate the degrees of freedom according to formula (15),

[0063] df = np-1, (15)

[0064] Where df is the degree of freedom, n is the sample size, and p is the number of independent variables;

[0065] Obtain the corresponding p-value according to the t-value statistic and the degrees of freedom;

[0066] The existence of systematic error is determined based on the p-value.

[0067] On the other hand, the present invention provides a low-altitude UAV flight trajectory measurement and calibration system, the system comprising:

[0068] Total station, used to lock onto drone targets and acquire measurement data;

[0069] UAVs, including those equipped with RTK high-precision positioning systems;

[0070] A processor is connected to the total station and the drone, and the processor is configured to execute any of the methods described above.

[0071] Beneficial effects of the present invention:

[0072] The implementation method of the present invention adopts a high-precision total station and an unmanned aerial vehicle RTK positioning system for collaborative measurement, realizes redundant complementarity of trajectory data through multi-source data fusion technology, effectively solves the problem of signal loss, and innovatively introduces a multi-dimensional system error analysis model. It can automatically identify and correct error sources such as systematic errors and RTK positioning drift, establish a complete measurement-compensation-verification closed-loop system, and support UAV flight trajectory calibration and reliability verification in complex environments.

[0073] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0075] Figure 1 Flowchart of a method for measuring and calibrating a low-altitude UAV flight trajectory according to one embodiment of the present invention;

[0076] Figure 2 A flowchart of a method for transmitting drone positioning system data to re-lock a target according to one embodiment of the present invention;

[0077] Figure 3 is a flowchart of a statistical indicator calculation method according to one embodiment of the present invention;

[0078] Figure 4 is a flow chart of a system error analysis method according to one embodiment of the present invention;

[0079] Figure 5 A flowchart of a method for determining whether a system error exists in a current system according to an embodiment of the present invention;

[0080] Figure 6 This is a structural block diagram of a low-altitude UAV flight trajectory measurement and calibration system according to one embodiment of the present invention;

[0081] Figure 7 The figure is a schematic diagram of low-altitude UAV flight trajectory measurement calibration according to one embodiment of the present invention.

[0082] Description of Reference Numerals

[0083] 1. Total station; 2. Drone; 3. Processor. DETAILED DESCRIPTION

[0084] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0085] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of laws and regulations. In the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use such solutions.

[0086] like Figure 1 The flowchart of the low altitude UAV flight trajectory measurement and calibration method according to one embodiment of the present invention is shown. Figure 1 In the measurement calibration method, the measurement calibration method may include the following steps:

[0087] In step S10, a unified coordinate system is established;

[0088] In step S11, the drone target is locked and measurement data is acquired;

[0089] In step S12, it is determined whether the drone target is lost;

[0090] In step S13, if it is determined that the drone target is lost, the drone positioning system data is transmitted back to re-lock the target;

[0091] In step S14, the measurement data is processed;

[0092] In step S15, the flight error of the UAV is analyzed based on the processed data.

[0093] In this Figure 1 In the illustrated low-altitude UAV flight trajectory measurement and calibration method, step S10 is used to establish a unified coordinate system. To facilitate subsequent data processing and analysis, it is necessary to unify the coordinate systems of the measurement system and the positioning system carried by the UAV. In this embodiment, the specific method for establishing the unified coordinate system in step S10 can be various forms known to those skilled in the art. In one example of the present invention, the method for establishing the unified coordinate system in step S10 can be to set up a total station on a known high-precision control point, obtain the true north direction through astronomical orientation, and complete the station setting. After completing the unification of the coordinate system, before the system is operated, the system can also be time synchronized (time synchronization of the total station, UAV, and processor) and the communication delay of the system can be measured. Specifically, in this example, the specific method for measuring the communication delay can include: sending a fixed data packet to the total station via optical fiber, and the total station immediately returning a confirmation signal after receiving the data. At the same time, the first channel of the oscilloscope is connected to the transmitting end optical fiber signal, the second channel of the oscilloscope is connected to the total station return signal, and a signal generator is used to trigger the oscilloscope to ensure synchronization of the two channels. The time difference between the rising edges of the two pulses is directly measured by an oscilloscope (with an accuracy of nanoseconds). According to the formula T delay =T received -T Sent To calculate the communication delay.

[0094] Step S11 is used to lock the drone target and obtain measurement data. Specifically, in this example, the drone can be automatically locked by performing a super search through the total station, and the drone position data can be collected in real time. Step S12 is used to determine whether the drone target is lost. Step S13 is used to transmit the drone positioning system data back to re-lock the target when it is determined that the drone target is lost. Specifically, in this example, when the target is lost, the drone transmits its position data to the processor wirelessly, and the processor performs coordinate conversion (converting geodetic coordinates into spherical coordinates) to obtain the azimuth and zenith angle of the drone relative to the total station, thereby driving the total station to quickly re-aim the drone.

[0095] In this embodiment, the specific method for transmitting the drone positioning system data to re-lock the target in step S13 can be various forms known to those skilled in the art. In one example of the present invention, step S13 can include: Figure 2 The steps shown in Figure 2 In the step S13, the following steps may be performed:

[0096] In step S20, the positioning system position data is transmitted back by the drone;

[0097] In step S21, the position data is converted into spatial rectangular coordinates according to formulas (1) to (5),

[0098] X=(N+H)cosBcosL, (1)

[0099] Y=(N+H)cosBsinL, (2)

[0100] Z=[N(1-e 2 )+H]sinB, (3)

[0101]

[0102] Among them, X, Y, Z are spatial rectangular coordinates, B is latitude, L is longitude, H is elevation, N is the radius of curvature of the ellipsoid, e 2 is the square of the first eccentricity of the ellipsoid, a is the major semi-axis, and b is the minor semi-axis;

[0103] In step S22, the spatial rectangular coordinates are converted into spherical coordinates according to formula (6) and formula (7),

[0104]

[0105] Among them, θ is the zenith angle, Φ is the azimuth angle;

[0106] In step S23, the target is re-locked according to the spherical coordinates.

[0107] In this Figure 2 In the illustrated method, step S20 is used to transmit the positioning system's position data from the drone. Steps S21 and S22 perform coordinate conversion, first converting the drone's position data into spatial rectangular coordinates and then converting the spatial rectangular coordinates into spherical coordinates. Step S23 is used to re-locate the drone based on the spherical coordinates.

[0108] Step S14 is used to process the measurement data. In order to ensure the accuracy of the measurement data and the precision of the data analysis, the measurement data needs to be processed. Specifically, in this example, the measurement data can be pre-processed to remove outliers, and then the measurement data can be resampled to ensure the integrity of the data. In this embodiment, the specific method of resampling the measurement data can be various forms known to those skilled in the art. In one example of the present invention, cubic spline interpolation is used to resample the collected discrete data. The data resampling steps may include:

[0109] In step S30, the measured data is segmented and fitted according to formula (8):

[0110] S(t)=at3 +bt 2 +ct+d, (8)

[0111] Where S(t) is a piecewise cubic polynomial, a, b, c, d are polynomial coefficients, and t is the time variable;

[0112] In step S31 , the continuity of the first-order and second-order derivatives of adjacent segments at the connection points is ensured, and the polynomial coefficients are solved by the matrix equation to obtain the interpolation position at any time t.

[0113] Step S30 is used to perform segmented fitting on the measured data, dividing the original data into several segments according to time, and fitting each segment with a cubic polynomial. For example, the total station samples a point every 1 second, and the goal of interpolation is to generate denser data (such as a point every 0.1 seconds). Step S31 is used to solve the interpolation. To ensure the smoothness of the curve, adjacent segments are required to meet the following conditions at the connection points (nodes): the first-order derivative is continuous, the second-order derivative is continuous, and the fitting curve is prevented from having "sharp corners" or unnatural turns. By establishing a matrix equation (a system of linear equations), the coefficients (a, b, c, d) of each cubic polynomial are solved, and the interpolation result at any time point t is finally obtained. In this embodiment, the interpolation error can be reduced by increasing the sampling rate and / or filtering. Specifically, it can be achieved by controlling multiple total stations to sample synchronously, increasing the density of the original data, and reducing the guesswork of the interpolation on the intermediate state. It can be achieved by performing data filtering preprocessing to denoise the original data before interpolation.

[0114] Step S15 is used to analyze the UAV's flight errors based on the processed data. In this embodiment, to ensure the comprehensiveness of the error analysis, a multi-dimensional system error analysis model is introduced. Specifically, in this example, the multi-dimensional system error analysis model may include visual comparison, statistical indicator calculation, and system error analysis. In this embodiment, the specific method of visual comparison in step S15 can be various forms known to those skilled in the art. In one example of the present invention, the visual comparison step may include:

[0115] In step S40, two-dimensional and three-dimensional trajectories of the measurement data and the UAV positioning system data are plotted to obtain path matching conditions;

[0116] In step S41, the change curves of the measurement data and the UAV positioning system data along the X, Y, and Z axes over time are plotted respectively to compare the offset in a specific direction.

[0117] Step S40 is used to plot the trajectory. The two-dimensional (horizontal) and three-dimensional trajectories of the two devices are plotted on the same chart to visually verify that the overall paths match. Step S41 is used for axis comparison. The X, Y, and Z axes are plotted over time to check for any deviations in specific directions.

[0118] In this embodiment, the specific method of calculating the statistical index in step S15 can be various forms known to those skilled in the art. In one example of the present invention, the step of calculating the statistical index can include: Figure 3 The steps shown in Figure 3 In the example, the step may include:

[0119] In step S50, the Euclidean distance difference at each time point is calculated according to formula (9):

[0120]

[0121] Where Δd is the difference in Euclidean distance between point (x1, y1, z1) and point (x2, y2, z2);

[0122] In step S51, the root mean square error is calculated according to formula (10):

[0123]

[0124] Where RMSE is the root mean square error, Δd i is the Euclidean distance difference of the i-th sample, and n is the number of samples;

[0125] In step S52, the mean absolute error is calculated according to formula (11):

[0126]

[0127] Among them, MAE is the mean absolute error.

[0128] In this Figure 3 In the illustrated method, step S50 performs point-by-point difference calculations. By calculating the straight-line distance (Euclidean distance) between two 3D points (x1, y1, z1) and the point (x2, y2, z2), the absolute value of the single-point deviation is obtained. This is used to examine the local deviation of each location data point and locate problematic time periods or locations. Step S51 calculates the root mean square error (RMSE) for each point to measure the overall fluctuation of the differences across all points. Step S52 calculates the mean absolute error (MAE) to obtain an average error level and prevent individual extreme values ​​from interfering with judgment.

[0129] In this embodiment, the specific method of the system error analysis in step S15 can be various forms known to those skilled in the art. In one example of the present invention, the step of the system error analysis can include the following steps: Figure 4 The steps shown in Figure 4 In the example, the step may include:

[0130] In step S60, the error value at each time point is calculated according to formula (12):

[0131] Δ i =P RTK (t i )-P total-station (t i ), (12)

[0132] Among them, Δ i At time point t i The error value at P RTK (t i ) is at time point t i UAV positioning data at P total-station (t i ) is the time point t i Measurement data at

[0133] In step S61, the sliding mean is calculated according to formula (13):

[0134]

[0135] Among them, μ i At time point t i The sliding mean at, k is the radius of the sliding window, P total-station (t j ) is at time t j Measurement data at

[0136] In step S62, a regression model is fitted on the error value and the sliding mean at each time point to obtain a linear model and a nonlinear model;

[0137] In step S63, the coefficients of determination of the linear model and the nonlinear model are calculated respectively;

[0138] In step S64, a linear model or a nonlinear model is selected as the optimal model according to the coefficient of determination;

[0139] In step S65, it is determined whether there is a system error in the current system based on the optimal model.

[0140] In this Figure 4 In the method shown, step S60 is used to calculate the error value at each time point to quantify the instantaneous error. Step S61 is used to calculate the sliding mean, and the local mean is calculated by sliding the window (the window size is 2k+1), eliminating random noise and smoothing the measurement data. Step S62 is used to fit the regression model, and the mathematical form of the system error is clarified by the model parameters. Specifically, in this example, the regression model fitting can be a i ,Δ i) Fitting linear and nonlinear models can include the following steps:

[0141] In step S70, a linear model is obtained according to formula (16):

[0142] Δ i =a·μ i +b, (16)

[0143] Where a is the slope, which represents the ratio of the error to the measured value, and b is the intercept, which represents the fixed deviation;

[0144] In step S71, a quadratic model is obtained according to formula (17).

[0145] Δ i =c·μ i 2 +d·μ i +e, (17)

[0146] Among them, c and d are the coefficients of the quadratic model, and e is a constant term representing the fixed deviation.

[0147] Step S63 is used to calculate the coefficient of determination R 2 Step S64 is used to determine the coefficient R 2 To evaluate the goodness of fit of the model, R 2 The closer it is to 1, the more error variability the model explains. If the quadratic model R 2 If the difference is significantly higher than the linear model (e.g., 0.95 vs. 0.80), the nonlinear model is selected. If the difference is not significant, the simpler linear model is preferred. Step S65 is used to determine whether the current system has systematic errors.

[0148] In this embodiment, the specific method of step S65 for determining whether the current system has a system error can be various forms known to those skilled in the art. In one example of the present invention, the steps of step S65 can include the following: Figure 5 The steps shown in Figure 5 In the example, the step may include:

[0149] In step S80, the t-value statistic of the slope of the optimal model is calculated according to formula (14),

[0150]

[0151] Where t is the t-value statistic of the slope, a is the slope, SE a is the standard error of the slope;

[0152] In step S81, the degrees of freedom are calculated according to formula (15),

[0153] df = np-1, (15)

[0154] Where df is the degree of freedom, n is the sample size, and p is the number of independent variables;

[0155] In step S82, the corresponding p-value is obtained according to the t-value statistic and the degrees of freedom;

[0156] In step S83, the existence of systematic error is determined based on the p-value.

[0157] In this Figure 5 In the method shown. Step S80 calculates the t-value statistic of the slope in the optimal model. Step S81 is used to calculate the degrees of freedom. Step S82 is used to obtain the corresponding p-value value based on the calculated t-value and degrees of freedom. Step S83 is used to judge the existence of a systematic error based on the p-value value. When the slope a=0, if p-value<0.05, it is considered that the slope a is significantly non-zero and there is a systematic error; if p-value≥0.05, it is considered that the slope may be caused by random fluctuations. In this embodiment, judging the existence of a systematic error can also include when a≠0, then there is a systematic error.

[0158] On the other hand, the embodiment of the present invention also provides a low-altitude UAV flight trajectory measurement and calibration system. The structural block diagram of the measurement and calibration system can be as follows Figure 6 As shown. Figure 6 In the embodiment, the measurement and calibration system may include a total station 1, a drone 2, and a processor 3. The total station 1 is used to lock onto a drone target and acquire measurement data. The drone 2 may be equipped with an RTK high-precision positioning system. The processor 3 is connected to the total station 1 via optical fiber and to the drone 2 wirelessly. The processor is configured to execute any of the methods described above.

[0159] The total station 1 has a super search automatic target aiming function and can continuously track and measure the target. To increase the frequency of data acquisition, two or more total stations can be connected in parallel. Before the system is used, it is necessary to synchronize the processor 3, total station 1 and drone 2, and measure the communication delay time between the processor 3 and each total station. When the system is measuring, the total station 1 automatically locks the target through super search. When the target is lost, the drone 2 transmits its position to the processor 3 via wireless. The processor 3 converts the geodetic coordinates into spherical coordinates and controls the total station 1 to quickly re-aim the drone. Figure 7 Schematic diagram of low-altitude UAV flight trajectory measurement and calibration.

[0160] Beneficial effects of the present invention:

[0161] The implementation method of the present invention adopts a high-precision total station and an unmanned aerial vehicle RTK positioning system for collaborative measurement, realizes redundant complementarity of trajectory data through multi-source data fusion technology, effectively solves the problem of signal loss, and innovatively introduces a multi-dimensional system error analysis model. It can automatically identify and correct error sources such as systematic errors and RTK positioning drift, establish a complete measurement-compensation-verification closed-loop system, and support UAV flight trajectory calibration and reliability verification in complex environments.

[0162] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0163] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0166] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0167] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0168] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0169] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0170] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A low-altitude UAV flight trajectory measurement and calibration method, characterized in that: The method comprises: Establish a unified coordinate system; Lock the drone target and obtain measurement data; Determine whether the drone target is lost; If the drone loses its target, it will send back the drone positioning system data to re-lock the target; processing the measurement data; Based on the processed data, the flight errors of the UAV are analyzed.

2. The calibration method according to claim 1, characterized in that: Establishing a unified coordinate system includes: The total station is set up on a known high-precision control point, and the true north direction is obtained through astronomical orientation to complete the station setting.

3. The calibration method according to claim 1, characterized in that: In the event that the drone loses its target, the following steps are performed to transmit the drone positioning system data to re-lock the target: Send back positioning system location data through drones; The position data is converted into spatial rectangular coordinates according to formula (1) to formula (5), X=(N+H)cosBcosL, (1) Y=(N+H)cosBsinL, (2) Z=[N(1-e 2 )+H]sinB, (3) Among them, X, Y, Z are spatial rectangular coordinates, B is latitude, L is longitude, H is elevation, N is the radius of curvature of the ellipsoid, e 2 is the square of the first eccentricity of the ellipsoid, a is the major semi-axis, and b is the minor semi-axis; The spatial rectangular coordinates are converted into spherical coordinates according to formula (6) and formula (7), Among them, θ is the zenith angle, Φ is the azimuth angle; The target is re-locked according to the spherical coordinates.

4. The calibration method according to claim 1, characterized in that: Processing the measurement data includes: The measurement data is preprocessed to remove abnormal values.

5. The calibration method according to claim 1, characterized in that: Processing the measurement data includes: The measurement data is resampled, comprising: The measured data is segmented and fitted according to formula (8), S(t)=at 3 +bt 2 +ct+d, (8) Where S(t) is a piecewise cubic polynomial, a, b, c, d are polynomial coefficients, and t is the time variable; Ensure that the first and second order derivatives of adjacent segments are continuous at the connection points, and solve the polynomial coefficients through the matrix equation to obtain the interpolation position at any time t.

6. The calibration method according to claim 1, characterized in that: Based on the processed data, the flight error of the UAV is analyzed, including visual comparison, and the visual comparison includes: Plotting two-dimensional and three-dimensional trajectories of the measurement data and the UAV positioning system data to obtain path consistency; Plot the change curves of the X, Y, and Z axes of the measurement data and the UAV positioning system data over time, and compare the offset in a specific direction.

7. The calibration method according to claim 1, characterized in that: Analyzing the flight error of the UAV based on the processed data includes calculating statistical indicators, which include: According to formula (9), the Euclidean distance difference at each time point is calculated. Where Δd is the difference in Euclidean distance between point (x1, y1, z1) and point (x2, y2, z2); Calculate the root mean square error according to formula (10): Where RMSE is the root mean square error, Δd i is the Euclidean distance difference of the i-th sample, and n is the number of samples; Calculate the mean absolute error according to formula (11): Among them, MAE is the mean absolute error.

8. The calibration method according to claim 1, characterized in that: Based on the processed data, the flight error of the UAV is analyzed, including system error analysis, which includes: Calculate the error value at each time point according to formula (12): Δ i =P RTK (t i )-P total-station (t i ), (12) Among them, Δ i At time point t i The error value at P RTK (t i ) is at time point t i UAV positioning data at P total-station (t i ) is the time point t i Measurement data at Calculate the sliding mean according to formula (13): Among them, μ i At time point t i The sliding mean at, k is the radius of the sliding window, P total-station (t j ) is the time t j Measurement data at Performing regression model fitting on the error value and the sliding mean at each time point to obtain a linear model and a nonlinear model; Calculating the coefficients of determination of the linear model and the nonlinear model respectively; Selecting the linear model or the nonlinear model as the optimal model according to the determination coefficient; It is determined whether there is a system error in the current system according to the optimal model.

9. The calibration method according to claim 8, characterized in that: Determining whether the current system has a systematic error according to the optimal model includes: The t-value statistic of the slope of the optimal model is calculated according to formula (14): Where t is the t-value statistic of the slope, a is the slope, SE a is the standard error of the slope; Calculate the degrees of freedom according to formula (15), df = np-1, (15) Where df is the degree of freedom, n is the sample size, and p is the number of independent variables; Obtain the corresponding p-value according to the t-value statistic and the degrees of freedom; The existence of systematic error is determined based on the p-value.

10. A low-altitude UAV flight trajectory measurement and calibration system, characterized in that: The system comprises: Total station, used to lock onto drone targets and acquire measurement data; UAVs, including those equipped with RTK high-precision positioning systems; A processor is connected to the total station and the drone, and the processor is configured to execute the method according to any one of claims 1 to 9.

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