Low-altitude unmanned aerial vehicle flight trajectory measurement calibration method and system

Abstract

The present application relates to the intersection field of unmanned aerial vehicle technology and precision measurement technology, and in particular to a low-altitude unmanned aerial vehicle flight trajectory measurement and calibration method and system, comprising: establishing a unified coordinate system; locking an unmanned aerial vehicle target and obtaining measurement data; determining whether the unmanned aerial vehicle target is lost; in the case where the unmanned aerial vehicle target is determined to be lost, returning unmanned aerial vehicle positioning system data to re-lock the target; processing the measurement data; and based on the processed data, analyzing the flight error of the unmanned aerial vehicle. The present application embodiment uses high-precision total station and unmanned aerial vehicle RTK positioning system cooperative measurement to effectively solve the signal loss problem, innovatively introduces a multi-dimensional system error analysis model, can automatically identify and correct systematic errors, RTK positioning drift and other error sources, establishes a complete measurement-compensation-verification closed-loop system, and supports unmanned aerial vehicle flight trajectory calibration and reliability verification in complex environments.

Description

Technical Field

[0001] This invention relates to the intersection of unmanned aerial vehicle (UAV) technology and precision measurement technology, and specifically to a method and system for measuring and calibrating the flight trajectory of a low-altitude UAV. Background Technology

[0002] Precision measurement radar is a radar system capable of accurately determining target coordinates and trajectories in real time, and it has significant application value in military, aerospace, and other fields. In ballistic missile range measurement, this radar system can undertake tasks such as range navigation zone security, missile thrust assessment, rocket stage separation, measurement of the relative positions of multiple warheads, and reentry point measurement. Currently, the verification method using UAVs equipped with corner reflectors is commonly used to determine the system accuracy of precision measurement radar. This technical solution uses a small UAV as a controllable moving target, and a high-precision flight control system achieves accurate reproduction of a preset trajectory, enabling the radar system to continuously track and measure the target. By establishing a spatial-temporal synchronous correlation between radar measurement data and the UAV's reference trajectory, quantitative assessment and parameter calibration of the radar system's measurement errors can be achieved. Compared to traditional target verification methods, UAV verification technology has full-latitude testing capabilities across multiple ranges, angle domains, and altitude levels, and offers advantages such as high flexibility and low 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 positioning information provided by the onboard global navigation and positioning system, and 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 objectives of this invention is to provide a method and system for measuring and calibrating the flight trajectory of a low-altitude unmanned aerial vehicle (UAV) to solve the problem of low accuracy in UAV trajectory measurement in the prior art.

[0004] To achieve the above objectives, the present invention provides a method for measuring and calibrating the flight trajectory of a low-altitude unmanned aerial vehicle (UAV), comprising:

[0005] Establish a unified coordinate system;

[0006] Lock onto the drone target and acquire measurement data;

[0007] Determine if the drone target has been lost;

[0008] If the drone target is determined to be lost, data from the drone positioning system should be transmitted back to re-lock onto the target;

[0009] The measurement data is processed;

[0010] Based on the processed data, the flight error of the drone is analyzed.

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

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

[0013] Optionally, in the event that the drone target is determined to be lost, transmitting drone positioning system data to re-lock onto the target includes:

[0014] Location data is transmitted back to the positioning system via drone;

[0015] The location data is converted into spatial rectangular coordinates according to formulas (1) to (5).

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

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

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

[0019]

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

[0021] The spatial rectangular coordinates are converted to spherical coordinates according to formulas (6) and (7).

[0022]

[0023]

[0024] Where θ is the zenith angle and Φ is the azimuth angle;

[0025] Based on the spherical coordinates, re-lock onto the target.

[0026] Optionally, processing the measurement data includes:

[0027] The measurement data is preprocessed to remove outliers.

[0028] Optionally, processing the measurement data includes:

[0029] The measurement data is resampled, including:

[0030] The measurement 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] To ensure the continuity of the first and second derivatives of adjacent segments at the connection point, the interpolation position at any time t is obtained by solving the polynomial coefficients through matrix equations.

[0034] Optionally, based on the processed data, the analysis of the UAV's flight error includes a visual comparison, which includes:

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

[0036] Plot the X, Y, and Z axis curves of the measurement data and the UAV positioning system data over time, and compare the offsets in specific directions.

[0037] Optionally, based on the processed data, the analysis of the UAV's flight error includes the calculation of statistical indicators, which include:

[0038] The Euclidean distance difference at each time point is calculated according to formula (9).

[0039]

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

[0041] The root mean square error is calculated according to formula (10).

[0042]

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

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

[0045]

[0046] Where MAE is the mean absolute error.

[0047] Optionally, based on the processed data, the analysis of the UAV's flight error includes systematic error analysis, which includes:

[0048] The error value at each time point is calculated according to formula (12).

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

[0050] Where, Δ i For time point t i The error value at point P RTK (t i ) represents the time point t i UAV location data at the location, P total-station (t i (t) represents time point t i Measurement data at the location;

[0051] Calculate the moving average according to formula (13).

[0052]

[0053] Where, μ i For time point t i The moving average at point P, where k is the radius of the sliding window, and P total-station (t j ) is at time t j Measurement data at the location;

[0054] For the error value and moving average at each time point, a regression model is fitted to obtain a linear model and a nonlinear model;

[0055] Calculate the coefficients of determination for the linear and nonlinear models respectively;

[0056] The linear or nonlinear model is selected as the optimal model based on the determination coefficient.

[0057] Determine whether there is a system error in the current system based on the optimal model.

[0058] Optionally, determining whether a systematic error exists in the current system based on the optimal model includes:

[0059] Calculate the t-statistic of the slope of the optimal model according to formula (14).

[0060]

[0061] Where t is the t-statistic of the slope, a is the slope, and SE a 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 degrees of freedom, n is the sample size, and p is the number of independent variables;

[0065] The corresponding p-value is obtained based on the t-value statistic and degrees of freedom.

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

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

[0068] A total station is used to lock onto drone targets and acquire measurement data;

[0069] Drones, including drones equipped with RTK high-precision positioning systems;

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

[0071] The beneficial effects of this invention are:

[0072] The embodiments of this invention employ a high-precision total station and an UAV RTK positioning system for collaborative measurement. By using multi-source data fusion technology, redundant and complementary trajectory data is achieved, effectively solving the signal loss problem. An innovative multi-dimensional system error analysis model is introduced, which can automatically identify and correct error sources such as systematic errors and RTK positioning drift, and establish a complete measurement-compensation-verification closed-loop system to 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 following detailed description section. Attached Figure Description

[0074] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0075] Figure 1 A flowchart of a low-altitude unmanned aerial vehicle (UAV) flight trajectory measurement and calibration method according to an embodiment of the present invention;

[0076] Figure 2 A flowchart illustrating a method for transmitting UAV positioning system data back to re-lock onto a target according to an embodiment of the present invention;

[0077] Figure 3 A flowchart of a statistical index calculation method according to an embodiment of the present invention;

[0078] Figure 4 A flowchart of a system error analysis method according to an embodiment of the present invention;

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

[0080] Figure 6 This is a block diagram of a low-altitude unmanned aerial vehicle (UAV) flight trajectory measurement and calibration system according to an embodiment of the present invention.

[0081] Figure 7 This is a schematic diagram of a low-altitude unmanned aerial vehicle (UAV) flight trajectory measurement and calibration according to an embodiment of the present invention.

[0082] Explanation of reference numerals in the attached figures

[0083] 1. Total station; 2. Unmanned aerial vehicle (UAV); 3. Processor. Detailed Implementation

[0084] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope 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 all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0086] like Figure 1 The diagram shows a flowchart of a low-altitude unmanned aerial vehicle (UAV) flight trajectory measurement and calibration method according to an embodiment of the present invention. Figure 1 In this measurement calibration method, the steps may include:

[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 has been lost;

[0090] In step S13, if it is determined that the drone target is lost, the drone positioning system data is transmitted back to relock 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 such Figure 1 In the low-altitude UAV flight trajectory measurement and calibration method shown, 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 of various forms known to those skilled in the art. In one example of this 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 true north through astronomical orientation, and complete the station setup. After unifying the coordinate system, before the system operates, time synchronization (time synchronization of the total station, UAV, and processor) can be performed, and the system's communication delay can be measured. Specifically, in this example, the specific method for measuring the communication delay can include: sending fixed data packets to the total station via optical fiber, and the total station immediately sending back an acknowledgment signal after receiving the data. Simultaneously, the first channel of the oscilloscope is connected to the transmitting optical fiber signal, and the second channel of the oscilloscope is connected to the total station's return signal. A signal generator is used to trigger the oscilloscope to ensure synchronization between the two channels. The rise time difference between two pulses can be directly measured using an oscilloscope (with accuracy down to the nanosecond level), according to the formula T. delay =T received -T Sent To calculate communication latency.

[0094] Step S11 is used to lock onto the UAV target and acquire measurement data. Specifically, in this example, this could involve automatically locking onto the UAV using a super search with a total station and collecting the UAV's position data in real time. Step S12 is used to determine if the UAV target has been lost. Step S13 is used to transmit UAV positioning system data back to relock onto the target if the UAV target is determined to be lost. Specifically, in this example, if the target is lost, the UAV may wirelessly transmit its position data to the processor, which performs coordinate transformation (converting geodetic coordinates to spherical coordinates) to obtain the UAV's azimuth and zenith angle relative to the total station, thereby driving the total station to quickly re-aime onto the UAV.

[0095] In this embodiment, the specific method for transmitting UAV positioning system data back to relock the target in step S13 can be of various forms known to those skilled in the art. In one example of the present invention, step S13 may include, for example... Figure 2 The steps shown are described. Figure 2 In this context, step S13 may include:

[0096] In step S20, the drone transmits the location data back to the positioning system.

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

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

[0104]

[0105] Where θ is the zenith angle and Φ is the azimuth angle;

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

[0107] In such Figure 2 In the method shown, step S20 is used to transmit the location data of the positioning system back by the UAV. Steps S21 and S22 are used for coordinate transformation, first converting the UAV's position data into spatial rectangular coordinates, and then converting the spatial rectangular coordinates into spherical coordinates. Step S23 is used to re-lock the UAV based on the spherical coordinates.

[0108] Step S14 is used to process the measurement data. 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 may first be preprocessed to remove outliers, and then resampled to ensure data integrity. In this embodiment, the specific method for resampling the measurement data can be of 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 measurement 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 first and second derivatives of adjacent segments are ensured to be continuous at the connection point. The polynomial coefficients are solved by matrix equations to obtain the interpolation position at any time t.

[0113] Step S30 is used to perform piecewise fitting on the measurement 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 one point every 1 second, and the goal of interpolation is to generate denser data (e.g., one point every 0.1 seconds). Step S31 is used to solve the interpolation. To ensure curve smoothness, adjacent segments are required to satisfy the following at connection points (nodes): continuous first derivative and continuous second derivative, avoiding sharp angles or unnatural transitions in the fitted curve. By establishing matrix equations (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 implementation, the interpolation error can be reduced by increasing the sampling rate and / or filtering. Specifically, this can be achieved by controlling multiple total stations to sample synchronously, increasing the density of the original data, and reducing the guesswork of intermediate states during interpolation. Alternatively, data filtering preprocessing can be performed to denoise the original data before interpolation.

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

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

[0116] In step S41, the curves of the changes of the X, Y, and Z axes of the measurement data and the UAV positioning system data over time are plotted respectively, and the offsets in specific directions are compared.

[0117] Step S40 is used for trajectory plotting. Two-dimensional (horizontal) and three-dimensional trajectories of the two devices are plotted on the same graph to visually observe whether the overall paths match. Step S41 is used for axis comparison. The curves of the X, Y, and Z axes over time are plotted separately to check for any offset in a specific direction.

[0118] In this embodiment, the specific method for calculating the statistical indicator in step S15 can be of various forms known to those skilled in the art. In one example of the present invention, the step of calculating the statistical indicator may include, for example: Figure 3 The steps shown are described in this. Figure 3 In this process, this 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 points (x1, y1, z1) and (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 Let be the Euclidean distance difference of the i-th sample, and n be the number of samples;

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

[0126]

[0127] Where MAE is the mean absolute error.

[0128] In such Figure 3 In the method shown, step S50 is used to perform point-by-point difference calculation. By calculating the straight-line distance (Euclidean distance) between two three-dimensional points (x1, y1, z1) and point (x2, y2, z2), the absolute value of the single-point deviation is obtained, which is used to check the local deviation of each data point and locate the problem period or location. Step S51 is used to calculate the root mean square error of each point to measure the overall fluctuation of the differences among all points. Step S52 is used to calculate the mean absolute error to obtain the average error level and avoid individual extreme values ​​interfering with the judgment.

[0129] In this embodiment, the specific method for system error analysis in step S15 can be of various forms known to those skilled in the art. In one example of the present invention, the steps of system error analysis may include, for example: Figure 4 The steps shown are described in this. Figure 4 In this process, this 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] Where, Δ i For time point t i The error value at point P RTK (t i ) represents the time point t i UAV location data at the location, P total-station (t i (t) represents time point t i Measurement data at the location;

[0133] In step S61, the moving average is calculated according to formula (13).

[0134]

[0135] Where, μ i For time point t i The moving average at point P, where k is the radius of the sliding window, and P total-station (t j ) is at time t j Measurement data at the location;

[0136] In step S62, regression models are fitted to the error value and moving average at each time point to obtain linear and nonlinear models;

[0137] In step S63, the determination coefficients 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 based on 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 such 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 moving average, calculating the local mean through a sliding window (window size 2k+1) to eliminate random noise and smooth the measurement data. Step S62 is used to perform regression model fitting, clarifying the mathematical form of the systematic error through the model parameters. Specifically, in this example, regression model fitting can be performed on the data (μ i ,Δ iFitting linear and nonlinear models may include the following steps:

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

[0142] Δ i =a·μ i +b, (16)

[0143] Where a is the slope, representing the proportion of error to change with the measured value, and b is the intercept, representing the fixed deviation;

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

[0145] Δ i =c·μ i 2 +d·μ i +e, (17)

[0146] Where 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 of determination R. 2 To evaluate the goodness of fit of the model, R 2 The closer the value is to 1, the more variability in the error explained by the model. If the quadratic model R... 2 If the difference is significantly higher than that of the linear model (e.g., 0.95 vs 0.80), a nonlinear model is selected. If the difference is not significant, the simpler linear model is preferred. Step S65 is used to determine whether there is a systematic error in the current system.

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

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

[0150]

[0151] Where t is the t-statistic of the slope, a is the slope, and SE a 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 degrees 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 based on the t-statistic and degrees of freedom;

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

[0157] In such Figure 5 In the method shown, step S80 calculates the t-statistic of the slope in the optimal model. Step S81 calculates the degrees of freedom. Step S82 obtains the corresponding p-value based on the calculated t-value and degrees of freedom. Step S83 determines the existence of systematic error based on the p-value. If the p-value < 0.05 when the slope a = 0, the slope a is considered significantly non-zero, indicating the existence of systematic error; if the p-value ≥ 0.05, the slope may be due to random fluctuations. In this embodiment, determining the existence of systematic error may also include the presence of systematic error when a ≠ 0.

[0158] On the other hand, embodiments of the present invention also provide a low-altitude unmanned aerial vehicle (UAV) flight trajectory measurement and calibration system. The structural block diagram of this measurement and calibration system can be as follows: Figure 6 As shown. In this Figure 6 The measurement calibration system may include a total station 1, a drone 2, and a processor 3. The total station 1 is used to lock onto the drone target and acquire measurement data. The drone 2 includes a drone 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 perform any of the methods described above.

[0159] Total station 1 features a super-search automatic target-aiming function and can continuously track and measure targets. To improve data acquisition frequency, two or more total stations can be connected in parallel. Before using the system, time synchronization of processor 3, total station 1, and UAV 2 is required, and the communication delay between processor 3 and each total station must be measured. During system measurement, total station 1 automatically locks onto the target via super-search. If the target is lost, UAV 2 wirelessly transmits its position to processor 3. Processor 3 converts the geodetic coordinates to spherical coordinates and controls total station 1 to quickly re-aime on the UAV. Figure 7 A schematic diagram for measuring and calibrating the flight trajectory of a low-altitude unmanned aerial vehicle (UAV).

[0160] The beneficial effects of this invention are:

[0161] The embodiments of this invention employ a high-precision total station and an UAV RTK positioning system for collaborative measurement. By using multi-source data fusion technology, redundant and complementary trajectory data is achieved, effectively solving the signal loss problem. An innovative multi-dimensional system error analysis model is introduced, which can automatically identify and correct error sources such as systematic errors and RTK positioning drift, and establish a complete measurement-compensation-verification closed-loop system to support UAV flight trajectory calibration and reliability verification in complex environments.

[0162] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0163] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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 processor, 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] 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.

[0165] 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.

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

[0167] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0168] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0169] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0170] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for measuring and calibrating the flight trajectory of a low-altitude unmanned aerial vehicle (UAV), characterized in that, The method includes: Establish a unified coordinate system; Lock onto the drone target and acquire measurement data Determine if the drone target has been lost; If the drone target is determined to be lost, data from the drone positioning system should be transmitted back to re-lock onto the target; The measurement data is processed; Based on the processed data, the flight error of the drone is analyzed; In cases where the drone target is determined to be lost, transmitting drone positioning system data to re-lock onto the target includes: Location data is transmitted back to the positioning system via drone; The location data is converted into spatial rectangular coordinates according to formulas (1) to (5). ，（1） ，（2） ，（3） ，（4） ，（5） in, Using spatial rectangular coordinates, As a dimension, Longitude For elevation, Let be the radius of curvature of the ellipsoid. Let be the square of the first eccentricity of the ellipsoid. For the major half-axis, It is the short half-axis; The spatial rectangular coordinates are converted to spherical coordinates according to formulas (6) and (7). ，（6） ，（7） in, Zenith angle, It is the azimuth angle; Based on the spherical coordinates, the target is re-locked; Based on the processed data, the analysis of the UAV's flight error includes systematic error analysis, which includes: Calculate the error value at each time point according to formula (12). ，（12） in, For at a certain point in time Error value at that point For at a certain point in time The location data of the drone at that location For time points Measurement data at the location; Calculate the moving average according to formula (13). ，（13） in, For at a certain point in time The moving average at that point, The radius of the sliding window, In time Measurement data at the location; For the error value and moving average at each time point, a regression model is fitted to obtain a linear model and a nonlinear model; Calculate the coefficients of determination for the linear and nonlinear models respectively; The linear or nonlinear model is selected as the optimal model based on the determination coefficient. Determine whether there is a system error in the current system based on the optimal model.

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 true north is obtained through astronomical orientation to complete the station setup.

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

4. The calibration method according to claim 1, characterized in that, Processing the measurement data includes: The measurement data is resampled, including: The measurement data is segmented and fitted according to formula (8). ，（8） in, It is a piecewise cubic polynomial. , , , For polynomial coefficients, It is a time variable; To ensure the continuity of the first and second derivatives of adjacent segments at the connection point, the polynomial coefficients are solved using matrix equations to obtain the result for any time interval. The interpolation position.

5. The calibration method according to claim 1, characterized in that, Based on the processed data, the analysis of the drone's flight error includes a visual comparison, which includes: Draw two-dimensional and three-dimensional trajectories of the measurement data and the UAV positioning system data to obtain the path matching situation; Plot the X, Y, and Z axis curves of the measurement data and the UAV positioning system data over time, and compare the offsets in specific directions.

6. The calibration method according to claim 1, characterized in that, Based on the processed data, the analysis of the UAV's flight error includes the calculation of statistical indicators, which includes: The Euclidean distance difference at each time point is calculated according to formula (9). ，（9） in, For point and points The difference in Euclidean distance between them; The root mean square error is calculated according to formula (10). ，（10） in, The root mean square error, For the first The difference in Euclidean distance between samples The number of samples; The mean absolute error is calculated according to formula (11). ，（11） in, This represents the mean absolute error.

7. The calibration method according to claim 1, characterized in that, Determining whether the current system has systematic errors based on the optimal model includes: The slope of the optimal model is calculated according to formula (14). Value statistics ，（14） in, for slope Value statistics The slope The standard error of the slope; Calculate the degrees of freedom according to formula (15). ，（15） in, For degrees of freedom, For sample size, The number of independent variables; According to the above Value statistics and degrees of freedom are obtained accordingly value; According to the above The value determines the existence of systematic error.

8. A low-altitude unmanned aerial vehicle (UAV) flight trajectory measurement and calibration system, characterized in that, The system includes: A total station is used to lock onto drone targets and acquire measurement data; Drones, including drones equipped with RTK high-precision positioning systems; A processor, connected to the total station and the UAV, is configured to perform the method as described in any one of claims 1 to 7.

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