A method and system for online compensation of vector magnetic interference of rotary-wing UAV

Abstract

The present invention provides a method and system for online compensation of vector magnetic interference of a rotary-wing UAV, belonging to the field of geomagnetic vector measurement technology. In order to solve the problem that the magnetic vector measurement value is affected by the magnetometer noise and attitude measurement noise at the same time, resulting in the accuracy of geomagnetic vector calculation being limited and the magnetic compensation effect being limited; and the problem that the maneuvering attitude of the rotary-wing UAV is limited and the multicollinearity of the measurement data is serious, resulting in difficulty in solving the model parameters. In the recursive process, the present invention uses an adaptive exponentially weighted moving average noise covariance estimator to quickly estimate the noise, adjust the noise covariance matrix of the recursive total least squares method in real time, reduce the influence of noise, and improve the compensation accuracy under the influence of geomagnetic vector error. At the same time, in the recursive process of the recursive total least squares method, the covariance matrix is ​​adaptively regularized to improve the compensation parameter estimation accuracy under the influence of multicollinearity.

Description

Technical Field

[0001] The present invention relates to the field of geomagnetic vector measurement technology, and in particular to an online compensation method and system for vector magnetic interference of a rotary-wing unmanned aerial vehicle. Background Art

[0002] Geomagnetic navigation is a positioning and navigation method based on the Earth's magnetic field, widely used in drone navigation, aerospace, and other fields. The Earth's magnetic field is a globally distributed vector field that is relatively stable, with unique magnetic field characteristics at each geographic location. Geomagnetic navigation systems exploit this characteristic by measuring and analyzing the strength and direction of the Earth's magnetic field to infer an object's position, heading, or attitude. Therefore, accurate measurement of the Earth's magnetic field vector information directly affects positioning accuracy during geomagnetic navigation and is the foundation for achieving high-precision navigation.

[0003] However, in practical applications, magnetometers often need to be fixedly connected to carriers such as drones and are relatively close to the carriers. Magnetic interference generated by the ferromagnetic structure of the carrier and the equipment on board leads to errors in the measurement data, which greatly limits the application of geomagnetic vector navigation. Therefore, it is necessary to compensate for the magnetic interference generated by the carrier to improve the accuracy of geomagnetic measurements. Traditional magnetic interference compensation algorithms require that the carrier be fixedly connected to the magnetometer, and then perform compensation maneuvers to obtain measurement data in multiple different attitudes to solve the compensation model. However, the magnetic vector measurement value is affected by both magnetometer noise and attitude measurement noise, resulting in limited accuracy of geomagnetic vector calculations and limited magnetic compensation effect. At the same time, the maneuverability of commonly used rotorcraft drones is limited, and the measurement data has severe multicollinearity, which makes it difficult to solve model parameters. Summary of the Invention

[0004] The technical problems to be solved by the present invention are:

[0005] In order to solve the problem that the magnetic vector measurement value is affected by both magnetometer noise and attitude measurement noise, which leads to limited accuracy of geomagnetic vector calculation and limited magnetic compensation effect; and the rotor UAV maneuvering attitude is limited, the measurement data has serious multicollinearity, which makes it difficult to solve the model parameters.

[0006] The present invention is to solve the above technical problems using the following technical solutions:

[0007] The present invention provides an online compensation method for vector magnetic interference of a rotary-wing UAV, comprising the following steps:

[0008] S100, obtaining a geomagnetic scalar reference value through a scalar magnetometer, and obtaining a geomagnetic vector measurement value and an attitude measurement value through a vector magnetometer and an inertial measurement unit respectively;

[0009] S200, constructing a body coordinate system and a geographic coordinate system, and calculating a reference value of a geomagnetic vector in the body coordinate system using a coordinate rotation matrix and an international geomagnetic reference field model;

[0010] S300, performing real-time compensation for magnetic interference on the geomagnetic vector reference value obtained in step S200 based on a recursive total least squares method to obtain a compensated geomagnetic vector parameter estimate;

[0011] S400, estimating the noise covariance matrix of the measurement value by an adaptive exponentially weighted moving average noise covariance estimator for rapid estimation of the noise level;

[0012] S500. Adaptively adjust the regularization parameter of the recursive total least squares method based on the current residual of the covariance matrix obtained in step S400 to improve the estimation accuracy of the compensation parameters under the influence of multicollinearity; perform the above operation until a stop instruction is received from the user, otherwise return to step S100 to re-compensate.

[0013] Furthermore, in step S200, o is taken as the origin of the body coordinate system and the geographic coordinate system of the rotorcraft. In the body coordinate system, the front, right, and bottom of the rotorcraft are the positive directions of the mx axis, my axis, and mz axis respectively; in the geographic coordinate system, the geographic north, east, and bottom are the positive directions of the gx axis, gy axis, and gz axis respectively.

[0014] Furthermore, in step S200, let the geomagnetic vector is the geomagnetic field vector value in the aircraft body coordinate system, let the transformation matrix is the rotation matrix of the body coordinate system relative to the geographic coordinate system; the value of the geomagnetic vector in the geographic coordinate system and The relationship is calculated using the following formula:

[0015]

[0016] Assume that the roll angle, pitch angle, and heading angle measured by the aircraft's inertial navigation system are , θ and ,but Calculated by the following formula:

[0017]

[0018]

[0019]

[0020]

[0021] Geomagnetic measurement vector with errors As shown below:

[0022]

[0023]

[0024]

[0025]

[0026] in, is the identity matrix, is the actual magnetic field vector; is the scale factor error matrix, 、 、 is the scale factor of each axis; is the three-axis non-orthogonal error matrix, 、 、 is a non-orthogonal angle; is the zero point bias, 、 、 is the offset of each axis;

[0027] Since the magnetometer is installed on the rotor UAV and is subject to both soft and hard magnetic interference, the magnetic vector with interference measured by the magnetometer is Expressed as:

[0028]

[0029]

[0030]

[0031] in, is the identity matrix, is the actual magnetic field vector; is the soft magnetic interference matrix, - is the soft magnetic interference parameter; For hard magnetic interference, 、 、 is the error of each axis;

[0032] The magnetometer measurement value in the drone's own coordinate system is The relationship with the true geomagnetic value in the geographic coordinate system is as follows:

[0033]

[0034]

[0035]

[0036] The interference compensation parameters of the vector magnetometer are obtained by the following formula:

[0037]

[0038]

[0039]

[0040] in, Represents transpose.

[0041] Furthermore, in step S300, the magnetic interference compensation algorithm based on the recursive total least squares method is as follows:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] in, 、 k moments respectively and Measured data; For the forgetting factor; is the covariance matrix at time k; is the estimated value of the compensation parameter at time k; is the noise covariance matrix; is the time k Matrix and Combination of vectors; is the gain of the recursive total least squares method at time k; is the eigenvector at time k; for Columns 1-12 of represent 13th column.

[0049] Furthermore, in step S400, in order to obtain the matrix in real time during the recursive process , an adaptive exponentially weighted moving average noise covariance estimator is used to estimate the noise level when acquiring new data:

[0050]

[0051]

[0052]

[0053]

[0054] in, is the smoothing factor; 、 k moments respectively or Measurements and corresponding exponentially weighted moving average error estimates; is the estimated value of the noise covariance matrix at time k.

[0055] Furthermore, in step S500, in order to improve the magnetic interference compensation effect in the case of insufficient posture, the formula After computing , regularization is added to the recursive total least squares method:

[0056]

[0057] in, is the covariance matrix at time k after regularization; η is the regularization factor;

[0058] In the formula After calculation, based on the residual at time k Adaptively adjust η:

[0059]

[0060]

[0061]

[0062] in, is the smoothed historical residual; is the historical residual smoothing factor; is the adaptive regularization factor at time k; 、 are the upper and lower limits of η respectively; is a small constant used to avoid the denominator being 0.

[0063] Furthermore, the forgetting factor is a constant between 0.95 and 1; Take a constant .

[0064] A rotary-wing UAV vector magnetic interference online compensation system is provided. The system has a program module corresponding to the above steps and executes the steps of the rotary-wing UAV vector magnetic interference online compensation method during operation.

[0065] A computer-readable storage medium stores a computer program, wherein the computer program is configured to implement the steps of an online compensation method for vector magnetic interference of a rotary-wing unmanned aerial vehicle when called by a processor.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention discloses an online compensation method and system for vector magnetic interference of a rotary-wing unmanned aerial vehicle (UAV). The method applies RTLS to the vector magnetic interference compensation, thereby avoiding the problem of reduced compensation effect caused by bias in traditional calibration methods when the influence of measurement errors is included in the system matrix. At the same time, the RTLS only needs to process the measurement data at the current moment and does not need to repeatedly perform SVD decomposition on continuously accumulated data, thereby improving the real-time performance of the compensation. During the recursive process, the noise is quickly estimated through the EWMA-NCE, and the noise covariance matrix of the RTLS is adjusted in real time to reduce the influence of the noise and improve the compensation accuracy under the influence of the geomagnetic vector error. In addition, during the RTLS recursive process, the covariance matrix is ​​adaptively regularized to solve the problem of difficulty in solving compensation parameters due to insufficient attitude of the rotary-wing UAV, reduce the compensation error caused by inappropriate selection of initial regularization parameters, and improve the compensation parameter estimation accuracy under the influence of multicollinearity. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of an online compensation method for vector magnetic interference of a rotary-wing UAV according to an embodiment of the present invention;

[0069] Figure 2 Schematic diagram of the body coordinate system and the geographic coordinate system in an embodiment of the present invention;

[0070] Figure 3 This is a comparison chart of error standard deviations in the simulation experiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0072] Specific implementation plan 1: Combined Figure 1 and Figure 2 As shown, the present invention provides an online compensation method for vector magnetic interference of a rotary-wing UAV, comprising the following steps:

[0073] S100, obtaining a geomagnetic scalar reference value through a scalar magnetometer, and obtaining a geomagnetic vector measurement value and an attitude measurement value through a vector magnetometer and an inertial measurement unit (IMU);

[0074] The principle of geomagnetic navigation is to measure the geomagnetic field information of the carrier's operating area and match it with a pre-established geomagnetic reference map to achieve carrier positioning and navigation. During the process of UAV geomagnetic navigation, the geomagnetic sensor often needs to be relatively fixedly connected to the UAV carrier, so the geomagnetic vector measurement value is usually measured relative to the coordinate system of the aircraft itself.

[0075] S200, constructing a body coordinate system and a geographic coordinate system, and calculating a reference value of a geomagnetic vector in the body coordinate system through a coordinate rotation matrix and an International Geomagnetic Reference Field (IGRF) model;

[0076] Due to the change of attitude during flight, the geomagnetic vector measurement value will change at the same point; therefore, we usually need to convert it to the geographic coordinate system for actual navigation calculation and analysis; combined with Figure 2 As shown, assuming that o is the origin of the rotary-wing UAV, the front, right, and bottom are the positive directions of the mx axis, my axis, and mz axis respectively, and the geographic north, east, and bottom are the positive directions of the gx axis, gy axis, and gz axis respectively;

[0077] Assuming the geomagnetic vector It is the geomagnetic field vector value in the aircraft body coordinate system. There is a known transformation matrix between the aircraft body coordinate system and the geographic coordinate system. ,in is a The rotation matrix represents the rotation relationship between the body coordinate system and the geographic coordinate system; then, the value of the geomagnetic vector in the geographic coordinate system is and The relationship can be calculated by the following formula:

[0078]

[0079] Assume that the roll angle, pitch angle, and heading angle measured by the aircraft's inertial navigation system are , θ and ,but It can be calculated by the following formula:

[0080]

[0081]

[0082]

[0083]

[0084] In geomagnetic navigation, three-axis vector fluxgate magnetometer is often used to measure magnetic field. However, due to the influence of manufacturing process and processing accuracy, the three-axis fluxgate magnetometer inevitably has errors such as three-axis non-orthogonality, inconsistent three-axis scale factors and three-axis zero offset. The geomagnetic measurement vector with errors The calculation is as follows:

[0085]

[0086]

[0087]

[0088]

[0089] in, is the identity matrix, is the actual magnetic field vector; is the scale factor error matrix, 、 、 is the scale factor of each axis; is the three-axis non-orthogonal error matrix, 、 、 is a non-orthogonal angle; is the zero point bias, 、 、 is the offset of each axis;

[0090] Assume that when the magnetometer is fixedly connected to the rotorcraft, it is subject to both soft magnetic interference (interference caused by the magnetization of the soft magnetic material on the carrier by the external magnetic field) and hard magnetic interference (residual magnetic interference caused by the long-term magnetization of the hard magnetic material in the carrier structure (such as high carbon steel, aluminum nickel alloy) by the external magnetic field). The magnetic vector with interference measured by the magnetometer is It can be expressed as:

[0091]

[0092]

[0093]

[0094] in is the identity matrix, is the actual magnetic field vector; is the soft magnetic interference matrix, - is the soft magnetic interference parameter; For hard magnetic interference, 、 、 is the error of each axis;

[0095] In summary, the magnetometer measurement value in the drone's own coordinate system The relationship with the true geomagnetic value in the geographic coordinate system is as follows:

[0096]

[0097]

[0098]

[0099] The interference compensation parameters of the vector magnetometer can be obtained by the following formula:

[0100]

[0101]

[0102]

[0103] in, stands for transpose;

[0104] S300, real-time compensation for magnetic interference based on RTLS;

[0105] In order to solve the compensation parameters in real time, the recursive least squares method (RLS) is often used in current research to estimate the parameters; however, this algorithm assumes that the error only exists in In formula (17), we know that the system matrix The actual magnetic field vector is included The vector is usually obtained using a scalar magnetometer and the International Geomagnetic Reference Field (IGRF) model, which is also affected by errors. Therefore, this problem does not conform to the assumptions of RLS. When the measurement error is not negligible, the solution of the RLS-based calibration method may be biased, resulting in a significant reduction in the compensation effect.

[0106] Total least squares (TLS) is a feasible solution. However, the TLS-based geomagnetic interference compensation solution requires repeated SVD decomposition of continuously accumulated data, which greatly reduces real-time performance. Therefore, the present invention proposes the use of recursive total least squares (RTLS) for real-time interference compensation. The RTLS-based magnetic interference compensation algorithm process is as follows:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in 、 k moments respectively and Measured data; is the forgetting factor, which is usually set to a constant between 0.95 and 1; is the covariance matrix at time k; is the estimated value of the compensation parameter at time k; is the noise covariance matrix; is at time k Matrix and Combination of vectors; is the gain of RTLS at time k; is the eigenvector at time k; represent Columns 1-12 of represent Column 13;

[0114] S400, estimating the noise covariance matrix of the measurement value by EWMA-NCE;

[0115] In order to obtain the matrix in real time during the recursion process , this paper proposes an adaptive exponentially weighted moving average noise covariance estimator (EWMA-NCE) to quickly estimate the noise level when acquiring new data:

[0116]

[0117]

[0118]

[0119]

[0120] in is the smoothing factor; 、 k moments respectively or The error measured by the sensor and the corresponding exponentially weighted moving average (EWMA) error estimate; is the estimated value of the noise covariance matrix at time k;

[0121] S500, adaptively adjusting RTLS regularization parameters based on the current residual;

[0122] In order to solve the problem of difficulty in solving compensation parameters caused by insufficient attitude of rotary-wing UAV, the present invention adaptively regularizes the covariance matrix during the RTLS recursive process to improve the accuracy of compensation parameter estimation under the influence of multicollinearity; the details are as follows:

[0123] Due to the motion characteristics of the rotorcraft, the flight attitude changes during compensation are limited, with the roll and pitch angles generally varying within ±20°. This can lead to multicollinearity in the system, affecting the accuracy of compensation parameter estimation.

[0124] In order to improve the magnetic interference compensation effect under the condition of insufficient posture, the present invention proposes to After the calculation of , regularization is added to the RTLS algorithm:

[0125]

[0126] in, is the covariance matrix at time k after regularization; is the regularization factor;

[0127] At the same time, the present invention is in progress After calculation, based on the residual at time k right Make adaptive adjustments:

[0128]

[0129]

[0130]

[0131] in is the smoothed historical residual; is the smoothing factor; is the adaptive regularization factor at time k; 、 for Upper and lower limits; is a smaller constant to avoid the denominator being 0;

[0132] The above steps S100 to S500 are executed until a stop instruction is received from the user, otherwise the above steps S100 to S500 are looped.

[0133] Specific embodiment 2: The present invention provides a rotary-wing UAV vector magnetic interference online compensation system, which has a program module corresponding to the above steps and executes the steps in the above rotary-wing UAV vector magnetic interference online compensation method during operation.

[0134] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0135] Specific embodiment three: The present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of an online compensation method for vector magnetic interference of a rotary-wing UAV when called by a processor.

[0136] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0137] Simulation experiment

[0138] In order to verify the accuracy of the method of the present invention, the method of the present invention is described in detail using the following examples.

[0139] Experimental scenario: The positioning method of the present invention is applied to the simulation of the magnetic interference compensation scenario of the rotor UAV and compared with the commonly used real-time compensation algorithm RLS. Set to (29062nT, -4023nT, 41542nT), the UAV carrier error parameters are set as follows:

[0140]

[0141]

[0142] During compensation, the drone hovers in place and rotates two degrees around its z-axis, while randomly varying its roll and pitch angles within a ±15° range. During this operation, geomagnetic vector and attitude measurements are acquired in real time, and Gaussian noise with variances of 10 nT and 0.5°, respectively, is added. A total of 1000 data points are acquired during the compensation process.

[0143] To more clearly illustrate the compensation effect, we simulated and generated another set of verification data. This data assumes that the drone has no attitude restrictions and randomly changes its attitude around the three axes, obtaining a total of 1,000 data points.

[0144] In this embodiment, the RTLS forgetting factor Take 0.999, EWMA smoothing factor Take 0.1, the historical residual smoothing factor Take 0.01; the regularization parameter at the initial moment Take 0.002; 、 Take separately 、 ; Pick .

[0145] Experimental environment: CPU: 12th Gen Intel(R) Core(TM) i5-1240P 1.70 GHz, 16G RAM, Windows 11.

[0146] The simulation measurement values, RLS compensation results, and RTLS compensation results are compared with the simulation reference data, and the error standard deviations are obtained as follows: Figure 3 The corresponding indicators are shown in Table 1.

[0147] Table 1

[0148]

[0149] Depend on Figure 3 As shown in Table 1, the use of the RLS-based compensation algorithm can effectively reduce magnetic interference, but the calculated results still have large errors. After compensation using the algorithm proposed in the present invention, the standard deviation is further reduced, which proves that the algorithm of the present invention is superior to the traditional method in terms of magnetic interference compensation accuracy and can effectively improve the accuracy of magnetic interference compensation.

[0150] Although the present invention is disclosed as above, the scope of protection disclosed by the present invention is not limited thereto. Those skilled in the art of the present invention may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for online compensation of vector magnetic interference of a rotary-wing UAV, characterized in that: The following steps are involved: S100, obtaining a geomagnetic scalar reference value through a scalar magnetometer, and obtaining a geomagnetic vector measurement value and an attitude measurement value through a vector magnetometer and an inertial measurement unit respectively; S200, constructing a body coordinate system and a geographic coordinate system, and calculating a reference value of a geomagnetic vector in the body coordinate system using a coordinate rotation matrix and an international geomagnetic reference field model; S300, performing real-time compensation for magnetic interference on the geomagnetic vector reference value obtained in step S200 based on a recursive total least squares method to obtain a compensated geomagnetic vector parameter estimate; S400, estimating the noise covariance matrix of the measurement value by an adaptive exponentially weighted moving average noise covariance estimator for rapid estimation of the noise level; S500. Adaptively adjust the regularization parameter of the recursive total least squares method based on the current residual of the covariance matrix obtained in step S400 to improve the estimation accuracy of the compensation parameters under the influence of multicollinearity; perform the above operation until a stop instruction is received from the user, otherwise return to step S100 to re-compensate.

2. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 1 is characterized in that: In step S200, o is taken as the origin of the body coordinate system and the geographic coordinate system of the rotorcraft. In the body coordinate system, the front, right, and bottom of the rotorcraft are the positive directions of the mx axis, my axis, and mz axis respectively; in the geographic coordinate system, the geographic north, east, and bottom are the positive directions of the gx axis, gy axis, and gz axis respectively.

3. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 2, characterized in that: In step S200, let the geomagnetic vector is the geomagnetic field vector value in the aircraft body coordinate system, let the transformation matrix is the rotation matrix of the body coordinate system relative to the geographic coordinate system; the value of the geomagnetic vector in the geographic coordinate system and The relationship is calculated using the following formula: ； Assume that the roll angle, pitch angle, and heading angle measured by the aircraft's inertial navigation system are , θ and ,but Calculated by the following formula: ； ； ； ； Geomagnetic measurement vector with errors As shown below: ； ； ； ； in, is the identity matrix, is the actual magnetic field vector; is the scale factor error matrix, 、 、 is the scale factor of each axis; is the three-axis non-orthogonal error matrix, 、 、 is a non-orthogonal angle; is the zero point bias, 、 、 is the offset of each axis; Since the magnetometer is installed on the rotor UAV and is subject to both soft and hard magnetic interference, the magnetic vector with interference measured by the magnetometer is Expressed as: ； ； ； in, is the identity matrix, is the actual magnetic field vector; is the soft magnetic interference matrix, - is the soft magnetic interference parameter; For hard magnetic interference, 、 、 is the error of each axis; The magnetometer measurement value in the drone's own coordinate system is The relationship with the true geomagnetic value in the geographic coordinate system is as follows: ； ； ； The interference compensation parameters of the vector magnetometer are obtained by the following formula: ； ； ； in, Represents transpose.

4. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 3 is characterized in that: In step S300, the magnetic interference compensation algorithm based on the recursive total least squares method is as follows: ； ； ； ； ； ； in, 、 k moments respectively and Measured data; For the forgetting factor; is the covariance matrix at time k; is the estimated value of the compensation parameter at time k; is the noise covariance matrix; is the time k Matrix and Combination of vectors; is the gain of the recursive total least squares method at time k; is the eigenvector at time k; for Columns 1-12 of represent 13th column.

5. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 4 is characterized in that: In step S400, in order to obtain the matrix in real time during the recursive process , an adaptive exponentially weighted moving average noise covariance estimator is used to estimate the noise level when acquiring new data: ； ； ； ； in, is the smoothing factor; 、 k moments respectively or Measurements and corresponding exponentially weighted moving average error estimates; is the estimated value of the noise covariance matrix at time k.

6. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 5, characterized in that: In step S500, in order to improve the magnetic interference compensation effect in the case of insufficient posture, the formula After computing , regularization is added to the recursive total least squares method: ； in, is the covariance matrix at time k after regularization; η is the regularization factor; In the formula After calculation, based on the residual at time k Adaptively adjust η: ； ； ； in, is the smoothed historical residual; is the historical residual smoothing factor; is the adaptive regularization factor at time k; 、 are the upper and lower limits of η respectively; is a small constant used to avoid the denominator being 0.

7. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 6, characterized in that: Forgetting Factor is a constant between 0.95 and 1; Take a constant .

8. An online compensation system for vector magnetic interference of a rotary-wing UAV, characterized by: The system has a program module corresponding to the steps of the online compensation method for vector magnetic interference of a rotary-wing UAV as described in any one of claims 1 to 7, and executes the steps of the online compensation method for vector magnetic interference of a rotary-wing UAV during operation.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the online compensation method for vector magnetic interference of a rotary-wing unmanned aerial vehicle according to any one of claims 1 to 7 when called by a processor.

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