Large unmanned aerial vehicle flight path optimization method based on enhanced aviation magnetic compensation

Abstract

The invention discloses a large-scale unmanned aerial vehicle flight path optimization method based on enhanced aviation magnetic compensation, and relates to the related technical field of large-scale unmanned aerial vehicle aviation magnetic survey, and the method comprises the following steps: based on a T-L model, expanding a compensation coefficient of the T-L model through deleting partial items and employing a dynamic parameterization method, and obtaining an improved T-L model; and controlling the large unmanned aerial vehicle to cancel the yaw operation, and optimizing the flight path of the large unmanned aerial vehicle according to the improved T-L model. According to the method, the accuracy of magnetic anomaly detection is improved, meanwhile, high-risk yaw operation is canceled, the geometric structure of the flight path is optimized, the operation safety of a large unmanned aerial vehicle is remarkably enhanced, and a reliable solution is provided for modern aviation magnetic measurement.

Description

Technical Field

[0001] The present invention relates to the technical field related to large-scale unmanned aerial vehicle (UAV) aeromagnetic survey, and in particular to a large-scale UAV flight path optimization method based on enhanced aeromagnetic compensation. Background Art

[0002] Airborne magnetic surveying is a geophysical technique that uses magnetometers mounted on aircraft to measure variations in the Earth's magnetic field. The primary purpose of airborne magnetic surveys is to investigate subsurface geological structures and identify mineral deposits. This is achieved by equipping aircraft with magnetometers. The instruments collect geomagnetic data at specific altitudes along a pre-set flight path over the target area. However, because aircraft are constructed of metal, they are inherently magnetic. As they move through the Earth's magnetic field, they generate interfering magnetic fields, which can significantly reduce the accuracy of the collected data. Therefore, compensating for this interference is crucial to ensuring the reliability of the results. The TL model is widely used for signal compensation in existing technologies, but it still has some limitations. For example, its 18 compensation coefficients are prone to multicollinearity, which can affect the accuracy of the solution. With the increasing use of large unmanned aerial vehicles (UAVs) in airborne magnetic surveys, there is an urgent need to address the multicollinearity and geomagnetic field gradient effects inherent in the TL model and develop new calibration flight protocols for these platforms to meet the current needs of UAV-based magnetic surveys. Summary of the Invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide a large UAV flight path optimization technology based on enhanced aeromagnetic compensation, aiming to improve the aeromagnetic compensation performance and flight safety of large UAVs.

[0004] To achieve the above technical objectives, the present application provides a large UAV flight path optimization method based on enhanced aeromagnetic compensation, comprising the following steps:

[0005] Based on the TL model, the compensation coefficient of the TL model is expanded by deleting some items and adopting the dynamic parameterization method to obtain the improved TL model.

[0006] Control the large UAV to cancel the yaw operation and optimize the flight path of the large UAV based on the improved TL model.

[0007] Preferably, when the compensation coefficient is expanded, the compensation coefficient is expanded according to the heading angle of the large unmanned aerial vehicle.

[0008] Preferably, in expanding the compensation coefficient, the compensation coefficient is expanded by constructing a first-order Fourier series according to the static component and the dynamic component corresponding to the fixed coefficient of the TL model and the heading angle.

[0009] Preferably, when optimizing the flight path, the large UAV is controlled to start from a horizontal flight attitude and perform pitch and roll maneuvers in sequence in each segment of the flight path.

[0010] Preferably, when optimizing the flight path, the flight path is optimized into an octagonal trajectory consisting of two overlapping quadrilaterals.

[0011] Preferably, when forming an octagonal trajectory, the large UAV is controlled to fly in a clockwise direction according to the first quadrilateral of the octagonal trajectory, and then fly in a diamond configuration according to the second quadrilateral of the octagonal trajectory.

[0012] The present invention also discloses a large-scale UAV flight path optimization system based on enhanced aeromagnetic compensation, which is used to implement the aforementioned large-scale UAV flight path optimization method based on enhanced aeromagnetic compensation, comprising:

[0013] The model improvement module is used to expand the compensation coefficient of the TL model by deleting some items and adopting a dynamic parameterization method based on the TL model to obtain an improved TL model;

[0014] The flight optimization module is used to control the large UAV to cancel the yaw operation and optimize the flight path of the large UAV based on the improved TL model.

[0015] Preferably, the model improvement module is further used to expand the compensation coefficient according to the heading angle of the large UAV, wherein the compensation coefficient is expanded by constructing a first-order Fourier series according to the static component and dynamic component corresponding to the fixed coefficient of the TL model according to the heading angle.

[0016] Preferably, the flight optimization module is further used to control the large UAV to start from a horizontal flight attitude, perform pitch and roll maneuvers in sequence in each segment of the flight path, and optimize the flight path into an octagonal trajectory consisting of two overlapping quadrilaterals.

[0017] Preferably, the flight optimization module is further configured to control the large UAV to fly in a clockwise direction according to the first quadrilateral of the octagonal trajectory, and then to fly in a diamond configuration according to the second quadrilateral of the octagonal trajectory.

[0018] The present invention discloses the following technical effects:

[0019] The present invention improves the accuracy of magnetic anomaly detection and significantly enhances the operational safety of large UAVs by eliminating high-risk yaw operations and optimizing the geometric structure of the flight path, providing a reliable solution for modern aerial magnetic surveys. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 is the aircraft reference coordinate system of the present invention;

[0022] Figure 2 The present invention uses the TL model and the improved TL model to compensate for horizontal flight data;

[0023] Figure 3 is the flight path conventionally used for compensation according to the present invention;

[0024] Figure 4 is a schematic diagram of the large UAV of the present invention;

[0025] Figure 5 is the improved aeromagnetic compensated flight path described in the present invention;

[0026] Figure 6 These are the calibration flight circle A and the verification flight circle B described in the present invention;

[0027] Figure 7 This is the effect of self-compensation using the calibration flight circle A described in the present invention;

[0028] Figure 8 This is the effect of self-compensation using verification flight circle B as described in the present invention;

[0029] Figure 9 is the compensation effect of cross-validation between the calibration flight circle A and the verification flight circle B described in the present invention;

[0030] Figure 10 are path C and path D during horizontal flight according to the present invention;

[0031] Figure 11 The results of compensating the horizontal flight path C according to the present invention are shown in Figure 1, where (a) the compensation result using the traditional TL model; (b) the compensation result using the improved TL model (coefficients from calibration flight circle A); and (c) the compensation result using the improved TL model (coefficients from validation flight circle B).

[0032] Figure 12The results of compensating the horizontal flight path D according to the present invention are shown in Figure 1, where (a) the compensation result using the traditional TL model; (b) the compensation result using the improved TL model (coefficients from calibration flight circle A); and (c) the compensation result using the improved TL model (coefficients from validation flight circle B).

[0033] Figure 13 It is a schematic flow chart of the method described in the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0035] like Figures 1-13 As shown, the present invention provides a large-scale UAV flight path optimization technology based on enhanced aeromagnetic compensation, which specifically includes the following contents:

[0036] The TL model provides a concise framework for describing the interfering magnetic field generated by aircraft in airborne magnetic surveys. The model divides the interfering magnetic field into three main parts: the inherent magnetic field, the induced magnetic field, and the eddy current magnetic field. Using this model, the interfering magnetic field in airborne magnetic surveys can be effectively reconstructed and compensated. Through the calculation of this model, the interfering magnetic field in the airborne magnetic survey process can be well inverted. First, based on the flight status of the aircraft, the following is established: Figure 1 In the coordinate system shown, point O is the origin of the entire coordinate system and is located at the center of the aircraft; the T axis is parallel to the fuselage and points to the nose; the L axis is parallel to the wing; the V axis is perpendicular to the bottom of the fuselage and points to the ground; the H axis is perpendicular to the bottom of the fuselage and points to the ground. E Represents the total geomagnetic field, which points to the center of the Earth. E The angle between the magnetic field and the T axis is defined as X; the magnetic field H E The angle between the L axis and the Earth's magnetic field is defined as Y; the Earth's magnetic field H E The angle between the Z axis and the V axis is defined as Z. T It is the vector sum of the total magnetic field composed of the T, L and V axes. It can be seen that the total magnetic field H T With the Earth's magnetic field H E Inconsistency; this is because the total magnetic field H TIt includes not only the Earth's magnetic field, but also the interfering magnetic field H I The formula is as follows:

[0037] H T =H E +H I ;

[0038] In actual flight experiments, the total magnetic field data H can be measured T , of which the geomagnetic field accounts for the majority, and the interference magnetic field generated by aeromagnetic exploration is a non-negligible part:

[0039]

[0040] Where H1, H2, and H3 represent the magnetic field data in the T-axis direction, L-axis direction, and V-axis direction obtained from the fluxgate vector magnetometer sensor, respectively.

[0041]

[0042] In the TL model, the interfering magnetic field is divided into three parts: the inherent magnetic field, the induced magnetic field and the eddy current magnetic field.

[0043]

[0044] Among them, c i Represents direction cosines cosX, cosY, cosZ, Represents the time derivative of direction cosine cosX, cosY, cosZ, p ij The coefficient representing the constant field, u ij The coefficient representing the induction field, r ij Represents the coefficient of the eddy current field.

[0045] Improved TL model:

[0046] According to the relationship between direction cosines, the present invention can obtain the following equation:

[0047] cos 2 X+cos 2 Y+cos 2 Z = 1;

[0048]

[0049] Therefore, reducing the original 18 coefficients to 16 significantly alleviates the multicollinearity problem in the equation. This optimization not only improves the accuracy of the solution but also effectively reduces the interference magnetic field fitting error caused by calculation errors.

[0050] In the TL model, it is assumed that the aircraft's fuselage structure and onboard equipment remain stable during flight operations and will not produce additional displacement due to operational changes. However, in actual flight, due to factors such as wind speed and turning, even seemingly rigid objects may undergo slight deformation. Therefore, the compensation coefficient C i It will change with the change of heading and needs to be adjusted dynamically, which means that the traditional TL model cannot fully cope with the interfering magnetic field generated in actual flight.

[0051] Therefore, the present invention needs to make some modifications to TL to make it more suitable for use in actual environments.

[0052] Assuming the compensation coefficient C i It changes periodically with the heading angle θ and can be approximated as a first-order Fourier series, and the following results can be obtained:

[0053] C i =α i +β i cosθ+γ i sinθ;

[0054] Where, α i Is the static component corresponding to the fixed coefficient in the original TL model. i and γ i is the dynamic component, which is used to capture the effect of heading angle.

[0055] The cyclical variation of the heading angle θ (0° to 360°) makes the Fourier series a natural choice. A first-order approximation (involving only cosθ and sinθ) strikes a balance between computational complexity and model accuracy. For example, deformations such as wing bending can cause the magnetic moment distribution to vary with heading, and the dynamic coefficients can capture these non-rigid effects.

[0056] Then, the original TL equation should be written as follows:

[0057]

[0058] In the formula, the newly added β i cosθ and γ i The sinθ term explicitly models the coupling effect between the basis functions and the heading. As the aircraft turns, the direction cosines cosX, cosY, and cosZ, along with the heading angle θ, jointly influence the interference intensity, and these extended terms accurately capture this relationship.

[0059] At this stage, the number of compensation coefficients that need to be determined increases to 48. These coefficients are derived from the direction cosine vectors, which are calculated using triaxial data acquired by a triaxial fluxgate vector magnetometer. Each sample contains 48 elements and describes the aircraft's attitude. Because airborne magnetic surveys typically involve tens of thousands of samples, these elements together form a large matrix. Simultaneously, scalar magnetic field data is collected using an optically pumped magnetometer. By solving this matrix, the 48 compensation coefficients can be estimated. Once these coefficients are determined, they are applied to the direction cosine matrix to fit the instantaneous interfering magnetic field.

[0060] To evaluate compensation effectiveness, two key metrics are introduced: standard deviation (STD) and improvement ratio (IR). The standard deviation, a quantitative indicator of residual magnetic interference after compensation, is used to evaluate the performance of airborne magnetic compensation systems. The improvement ratio, defined as the ratio of the standard deviations before and after compensation, quantifies the improvement in compensation effectiveness. A larger improvement ratio indicates better compensation and a more pronounced reduction in magnetic interference.

[0061]

[0062] Among them, X i represents the data of each sampling point, μ represents the average value. STD is the square difference of the data, which can well reflect the overall situation. b and H a When the total magnetic field data of the optical pump is subsequently used in the present invention, it is necessary to consider using a bandpass filter to remove the Earth's magnetic field to obtain a pure interference field, which makes it easier to use the TL model for aeromagnetic compensation.

[0063] H T =H E +H I =H E +H pe +H in +H ed =H E +UC;

[0064] Where U is the direction cosine and C is the compensation factor.

[0065] bpf(H T )=bpf(H E )+bpf(U)C;

[0066] After the filter processing, if the geomagnetic field is stable at a specific frequency, then bpf(H E)=0. However, due to the non-ideal nature of the calibration operation, the nonlinearity of the filter, and the inhomogeneity of the geomagnetic field, the aircraft will experience geomagnetic field coupling during maneuvers, generating additional geomagnetic interference signals. Since the frequency of this signal is the same as the frequency of the signal generated by the maneuvering action, ordinary filters cannot completely filter out the influence of the geomagnetic field. The residual geomagnetic field signal bpf(H E ) can be represented by T, L, and V axes.

[0067] bpf(H E )=g x cosα·bpf(T)+g y cosβ·bpf(L)+g z cosγ·bpf(V)

[0068] The filtered residual magnetic field is decomposed into horizontal and vertical components through Taylor series expansion. Finally, the horizontal component geomagnetic field model is constructed using longitude and latitude, and the vertical component geomagnetic field model is constructed using altitude. Therefore, the filtered residual magnetic field bpf (H E ) can be represented by the fit of three coefficients.

[0069] bpf(H E )=bpf(W)a+bpf(J)b+bpf(L)c

[0070] The TL model can be rewritten as follows:

[0071]

[0072] Where W represents the latitude vector, J represents the longitude vector, and L represents the altitude vector. Therefore, the total number of compensation coefficients that need to be solved increases from 48 to 57.

[0073] By adjusting this model to reduce the residual geomagnetic field interference caused by maneuver coupling, the interfering magnetic field during airborne magnetic sensing can better match the actual situation. The actual interfering magnetic field obtained through optical pumping is subtracted from the interfering magnetic field fitted by the improved TL model, thus generating a compensated airborne magnetic sensing signal. The effectiveness of compensation determines the difficulty of signal target recognition, so an excellent compensation mechanism is crucial. However, the TL model can only partially optimize the compensation effect, and the details of the compensation still require model modification.

[0074] Improved TL model effect:

[0075] To verify the effectiveness of the improved model, a series of tests were conducted in the East my country Sea using a small unmanned aerial vehicle (UAV) equipped with an optically pumped magnetometer, a three-axis fluxgate magnetometer, a GPS module, and an inertial navigation system. Calibration flights were conducted according to a conventional protocol, involving sequential yaw, pitch, and roll maneuvers along the four directions of a predetermined flight path. Horizontal flight data was also collected in the surrounding area to test the effectiveness of the compensation.

[0076] During the flight calibration process, scalar magnetic field data is acquired by an optically pumped magnetometer, while three-axis vector data is recorded by a fluxgate magnetometer. The aircraft is magnetically compensated using both a traditional TL model and an improved TL model. Figure 2 The results shown here demonstrate the effectiveness of both models in reducing residual magnetic interference. Table 1 shows the compensation results: The standard deviation of the interfering magnetic field was 0.1131 nT before compensation. After compensation using the TL model, the standard deviation was reduced to 0.0229 nT, and the improved TL model to 0.0184 nT. This represents a 24.41% improvement, demonstrating the excellent effectiveness of the improved TL model.

[0077] Table 1 Compensation results of T-L model and improved TL model

[0078] method Before compensation (STD) After compensation (STD) Improvement ratio IR TL model 0.1131nT 0.0229nT 4.9408 Improved TL model 0.1131nT 0.0184nT 6.1467

[0079] Improved Compensated Flight Circles:

[0080] Leliak proposed a calibration flight scheme in 1961 to compensate for magnetic field interference. This scheme is still widely used today and has been proven to be very effective in improving the accuracy of aeronautical magnetic compensation. Figure 3 As shown, the traditional calibration flight protocol begins with the aircraft in a level flight attitude. The flight path follows a rectangular trajectory, sequentially from south to north, west to east, north to south, and east to west. During each leg, the aircraft must perform three specific maneuvers: yaw (±5°), pitch (±5°), and roll (±10°). These maneuvers must be performed consistently to ensure accurate compensation. Furthermore, to reduce flight time and optimize the compensation process, the horizontal flight distance between each maneuver is minimized.

[0081] Traditional calibration flight schemes present several significant challenges:

[0082] 1. Operational complexity and equipment displacement: During flight, each leg requires continuous execution of three maneuvers: yaw, pitch, and roll. These maneuvers are difficult to execute accurately at high speeds. The continuous and rapid changes in aircraft attitude increase the likelihood of small displacements between the aircraft and onboard equipment, such as the three-axis fluxgate magnetometer. This displacement generates additional interfering magnetic fields, which cannot be fully simulated by the TL model, leading to errors in the compensation results.

[0083] 2. Extended flight paths and geomagnetic instabilities: When performing yaw, pitch, and roll maneuvers, a longer flight path is required to achieve gradual adjustments. This results in an expanded calibration flight area, which may include areas of geomagnetic instability. Because the geomagnetic field significantly affects the total magnetic field data collected by the optically pumped magnetometer, this instability can reduce the accuracy of the compensation process.

[0084] 3. Safety risks of large UAVs: In the initial stage, aeromagnetic measurements were carried out using slower manned aircraft, and the risk of high-speed maneuvers was minimal. With the emergence of small UAVs, the risk remained controllable due to their small size and flexibility. Subsequently, large UAVs developed rapidly, but they were heavier and had a larger payload, which brought significant safety hazards during high-speed maneuvers. In particular, yaw maneuvers along the X-axis will produce significant pressure on the fuselage and increase the risk of structural damage. In contrast, roll and pitch maneuvers mainly affect the Y-axis and Z-axis and have less risk. In addition, in order to reduce the impact on magnetic field measurements, optically pumped magnetometers and three-axis fluxgate sensors are usually installed away from the fuselage, generally placed on the extended tail, such as Figure 4 As shown, this large drone is equipped with an additional tail section for mounting magnetic detection equipment. The required magnetic detection instruments, such as a triaxial magnetometer and an optically pumped magnetometer, are mounted within the red circle to avoid interference from magnetic noise generated by the aircraft. Improper equipment installation increases the risk of instrument displacement or structural failure, generating additional interfering magnetic fields. These factors can lead to errors in the compensation results.

[0085] Given these challenges, it is imperative to optimize the calibration flight maneuvers of large UAVs and eliminate maneuvers that adversely affect safety and compensation accuracy.

[0086] During flight, external factors such as wind can cause the aircraft to yaw. If the compensation model fails to account for yaw, its effectiveness will be reduced, impacting the quality of the magnetic data. By incorporating yaw into the calibration process, the corresponding compensation data can be collected and integrated into the model. This allows the compensation system to effectively reduce yaw-induced errors during actual flight operations.

[0087] Based on the above analysis, the present invention proposes to cancel the yaw operation during flight and only retain pitch and roll. Although yaw is crucial for compensation, it is reasonable to remove the yaw operation considering the risks and complexity it brings. To this end, the present invention designs a new calibration flight path, such as Figure 5 shown.

[0088] The improvement plan includes the following steps:

[0089] 1. The aircraft starts in a level flight attitude and performs pitch (±5°) and roll (±10°) maneuvers sequentially on each leg of the flight path.

[0090] 2. The flight path consists of two overlapping quadrilaterals forming an octagonal trajectory. The first quadrilateral flies in a clockwise direction, followed by the second quadrilateral flying in a diamond-shaped trajectory.

[0091] Compared with the traditional calibration flight scheme, this design has the following advantages:

[0092] 1. Yaw effect compensation: Although yaw operation is cancelled, the octagonal flight path contains seven turns, which effectively simulates the magnetic interference caused by yaw and compensates for its absence during the calibration process.

[0093] 2. Enhanced safety of large UAVs: By eliminating high-risk yaw maneuvers, the proposed solution reduces the possibility of structural damage and displacement of tail-mounted equipment.

[0094] 3. Optimize flight efficiency: Eliminating yaw maneuvers shortens each flight segment. Furthermore, the use of overlapping quadrilaterals reduces the overall flight area, thereby minimizing the impact of geomagnetic gradient changes on the compensation process.

[0095] Experiments and results:

[0096] An experiment was conducted in a certain sea area. Figure 6 Two calibration missions, labeled A and B, were performed in adjacent areas. The experiments used a state-of-the-art large-scale magnetic exploration unmanned aerial vehicle (UAV), equipped with an optically pumped magnetometer, a three-axis fluxgate magnetometer, an inertial navigation system, a GPS module, and additional payload instrumentation. During flight, the optically pumped magnetometer recorded scalar total magnetic field data, while the three-axis fluxgate magnetometer provided vector magnetic field data in the aircraft coordinate system. The inertial navigation system captured the pitch, roll, and yaw angles during maneuvers, and the GPS module provided real-time data on altitude, velocity, longitude, and latitude.

[0097] Mission A flew from west to east, then exited the compensation zone and headed northwest. Mission B entered from south to north and ultimately exited southeast. The drone flew at an altitude of approximately 3,500 meters at a speed of 50 meters per second, with winds reaching 10 meters per second. These conditions posed significant challenges and risks to the operation of large drones, highlighting the importance of the proposed flight plan.

[0098] Data acquisition was performed at a sampling frequency of 10 Hz. All scalar and vector data were preprocessed according to the improved TL model. A Butterworth filter with a bandwidth of 0.04 Hz to 0.6 Hz was used to isolate relevant magnetic interference signals.

[0099] from Figure 7 and Figure 8 It can be seen that the improved TL model achieves significant self-compensation results for calibration flight circle A and verification flight circle B. As shown in Table 2, the standard deviation of the interfering magnetic field measurements generated during calibration flight circle A is 0.2912 nT. Using the improved TL model for compensation, the standard deviation is reduced to 0.0404 nT, significantly reducing the interfering noise. The improvement ratio is 7.3171. For calibration flight circle B, the interfering magnetic field is reduced from 0.3264 nT to 0.0415 nT, an improvement ratio of 7.8634. These results verify that the proposed calibration flight scheme meets the aeromagnetic compensation requirements of this UAV platform.

[0100] Table 2 Effect of self-compensation using calibration flight circle A and verification flight circle B

[0101] Flying Circle Before compensation (STD) After compensation (STD) Improvement ratio IR A 0.2912nT 0.0404nT 7.3173 B 0.3264nT 0.0415nT 7.8634

[0102] In order to further evaluate the stability of the model, the compensation effect is evaluated by cross-validation between the calibration flight circle A and the validation flight circle B. Figure 9 As shown in Figure 3, (a) illustrates the compensation effect of flight circle A on flight circle B, while (b) illustrates the compensation effect of flight circle B on flight circle A. Table 3 shows the cross-compensation effect of flight circles A and B. The interfering magnetic field of flight circle A is reduced from 0.2912 nT to 0.0436 nT, an improvement ratio of 6.6789. Similarly, the interfering magnetic field strength of flight circle B is reduced from 0.3264 nT to 0.0465 nT, an improvement ratio of 7.0194. Both rings have a significant compensation effect on noise. This cross-validation result confirms the reliability and universality of the proposed compensation framework.

[0103] Table 3 Cross-interference compensation effect of flight circle A and flight circle B

[0104] Compensation method Before compensation (STD) After compensation (STD) Improvement ratio IR A→B 0.2912nT 0.0436nT 6.6789 B→A 0.3264nT 0.0465nT 7.0194

[0105] Two different situations were selected for evaluation in level flight (e.g. Figure 10 ). Path C contains a sharp turn, and path D is a gentle turn. The compensation result is shown as Figure 11 and Figure 12 As shown in the figure, the improved TL model, using the compensation coefficients obtained from calibration circle A and verification circle B, compensates for the magnetic interference on paths C and D with significantly better results than the traditional TL model. Furthermore, the improved TL model effectively eliminates abnormal signals caused by sharp turns in path C and effectively handles interference caused by long turns in path D.

[0106] As shown in Table 4, based on the magnetic data from calibration flight circle A, the traditional TL model achieves magnetic compensation for path C with an improvement ratio of 2.3060, and for path D with an improvement ratio of 1.7517. Using the improved TL model for magnetic compensation of paths C and D, the improvement ratios are 4.1688 and 2.1504, respectively, representing improvements of 80.78% and 22.76%, respectively. Based on the magnetic data from validation flight circle B, the improved TL model achieves magnetic compensation for paths C and D with improvement ratios of 4.0428 and 2.0664, respectively. These results demonstrate that the improved TL model significantly outperforms the traditional TL model in compensation.

[0107] Table 4 Compensation results of path C and path D using different methods

[0108]

[0109] Based on the traditional TL model, this paper develops an enhanced model that expands upon the original one, increasing the number of compensation coefficients from 18 to 57. This model improves compensation accuracy through dynamic adjustment, significantly improving the compensation effect compared to the traditional TL model. Furthermore, given the risks inherent in operating large unmanned aerial vehicles (UAVs), this paper further proposes an improved calibration flight scheme. By eliminating yaw maneuvers and optimizing flight paths, this scheme significantly reduces the risk and complexity of aeromagnetic compensation for large UAVs. Experimental results confirm the effectiveness of this approach. These results demonstrate that this model exhibits excellent magnetic compensation performance for large UAVs.

[0110] In summary, the present invention provides a safer, more effective and efficient framework for aeromagnetic compensation, which is particularly suitable for the operational needs of large unmanned aerial vehicles.

[0111] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts 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, 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 processes in the flowcharts and / or block diagrams. 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.

[0112] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0113] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A large-scale unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation, characterized in that: The following steps are involved: Based on the TL model, the compensation coefficient of the TL model is expanded by deleting some items and adopting a dynamic parameterization method to obtain an improved TL model; The large UAV is controlled to cancel the yaw operation, and the flight path of the large UAV is optimized according to the improved TL model.

2. The large-scale unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation according to claim 1, characterized in that: When the compensation coefficient is expanded, the compensation coefficient is expanded according to the heading angle of the large UAV.

3. The large-scale unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation according to claim 2, characterized in that: In expanding the compensation coefficient, the compensation coefficient is expanded by constructing a first-order Fourier series according to the heading angle based on the static component and the dynamic component corresponding to the fixed coefficient of the TL model.

4. The large-scale unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation according to claim 3 is characterized in that: When optimizing the flight path, the large UAV is controlled to start from a horizontal flight attitude and perform pitch and roll maneuvers in sequence in each segment of the flight path.

5. The large-scale unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation according to claim 4 is characterized in that: When optimizing the flight path, the flight path is optimized into an octagonal trajectory consisting of two overlapping quadrilaterals.

6. The large-scale unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation according to claim 5, characterized in that: When forming an octagonal trajectory, the large drone is controlled to fly in a clockwise direction according to the first quadrilateral of the octagonal trajectory, and then fly in a diamond configuration according to the second quadrilateral of the octagonal trajectory.

7. A large unmanned aerial vehicle flight path optimization system based on enhanced aeromagnetic compensation, used to implement a large unmanned aerial vehicle flight path optimization method based on enhanced aeromagnetic compensation according to any one of claims 1 to 6, characterized in that: include: A model improvement module is used to expand the compensation coefficient of the TL model by deleting some items and adopting a dynamic parameterization method based on the TL model to obtain an improved TL model; The flight optimization module is used to control the large UAV to cancel the yaw operation and optimize the flight path of the large UAV according to the improved TL model.

8. The large-scale unmanned aerial vehicle flight path optimization system based on enhanced aeromagnetic compensation according to claim 7, characterized in that: The model improvement module is further used to expand the compensation coefficient according to the heading angle of the large unmanned aerial vehicle, wherein the compensation coefficient is expanded by constructing a first-order Fourier series based on the static component and dynamic component corresponding to the fixed coefficient of the TL model according to the heading angle.

9. The large-scale unmanned aerial vehicle flight path optimization system based on enhanced aeromagnetic compensation according to claim 8, characterized in that: The flight optimization module is also used to control the large UAV to start from a horizontal flight attitude, perform pitch and roll maneuvers in sequence in each segment of the flight path, and optimize the flight path into an octagonal trajectory consisting of two overlapping quadrilaterals.

10. A large-scale unmanned aerial vehicle flight path optimization system based on enhanced aeromagnetic compensation according to claim 9, characterized in that: The flight optimization module is also used to control the large drone to fly in a clockwise direction according to the first quadrilateral of the octagonal trajectory, and then fly in a diamond configuration according to the second quadrilateral of the octagonal trajectory.

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