Rotor unmanned aerial vehicle vector magnetic interference online compensation method and system

Abstract

The invention provides a rotor unmanned aerial vehicle vector magnetic interference online compensation method and system, and belongs to the technical field of geomagnetic vector measurement. In order to solve the problems that a magnetic vector measurement value is influenced by magnetometer noise and attitude measurement noise at the same time, the precision of geomagnetic vector calculation is limited, and the magnetic compensation effect is limited; and the problem that model parameters are difficult to solve due to the fact that the maneuvering attitude of the rotor unmanned aerial vehicle is limited and multiple collinearity of measurement data is serious is solved. According to the method, the noise is quickly estimated through the adaptive exponentially weighted moving average noise covariance estimator in the recursion process, the noise covariance matrix of the recursion total least square method is adjusted in real time, the influence of the noise is reduced, the compensation precision under the influence of geomagnetic vector errors is improved, and the accuracy of geomagnetic vector error compensation is improved. And meanwhile, adaptive regularization is carried out on a covariance matrix in the recursion process of the recursion total least square method, so that the compensation parameter estimation precision under the influence of multiple collinearity is improved.

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 method of positioning and navigation based on the Earth's magnetic field. It is widely used in drone navigation, aerospace and other fields. The geomagnetic field is a vector field distributed globally. It is relatively stable, and the magnetic field characteristics of each geographical location are unique. The geomagnetic navigation system uses this feature to measure and analyze the strength and direction of the Earth's magnetic field to infer the position, heading or attitude of an object. Therefore, when performing geomagnetic navigation, the accurate measurement of the geomagnetic field vector information directly affects the accuracy of positioning and is the basis 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. Due to the magnetic interference generated by the ferromagnetic structure of the carrier and the equipment on board, there are 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 measurement. The traditional magnetic interference compensation algorithm requires that the carrier be fixedly connected to the magnetometer and a compensation maneuver be performed to obtain measurement data in multiple different attitudes for solving the compensation model. However, the magnetic vector measurement value is affected by both magnetometer noise and attitude measurement noise, which limits the accuracy of geomagnetic vector calculation and the magnetic compensation effect. At the same time, the commonly used rotorcraft drones have limited maneuvering attitudes, and the measurement data have serious multicollinearity, which makes it difficult to solve the 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 the magnetometer noise and the attitude measurement noise, which leads to the limitation of the accuracy of geomagnetic vector calculation and the magnetic compensation effect; and the maneuvering attitude of the rotorcraft is limited, the multicollinearity of the measurement data is serious, which makes it difficult to solve the model parameters.

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

[0007] The present invention provides a method for online compensation of 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 through 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 the recursive total least squares method to obtain a compensated geomagnetic vector parameter estimate;

[0011] S400, estimating the noise covariance matrix of the measured 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 from the user is received, otherwise return to step S100 to re-compensate.

[0013] Further, in step S200, o is taken as the origin of the body coordinate system and the geographic coordinate system of the rotorcraft UAV. In the body coordinate system, the front, right, and bottom of the rotorcraft UAV 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] Further, in step S200, let the geomagnetic vector is the value of the geomagnetic field vector in the aircraft body coordinate system, let the transformation matrix R be the rotation matrix of the aircraft 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 by the following formula:

[0015]

[0016] Assuming that the roll angle, pitch angle, and heading angle measured by the aircraft's inertial navigation system are φ, θ, and ψ, respectively, R is calculated by the following formula:

[0017]

[0018] R=R z (ψ)·R y (θ)·R x (φ) (5)

[0019] Geomagnetic measurement vector with errors As shown below:

[0020]

[0021]

[0022] Where I is the identity matrix, is the actual magnetic field vector; S is the scale factor error matrix, s x 、s y 、s z is the scale factor of each axis; P is the three-axis non-orthogonal error matrix, α, β, γ are non-orthogonal angles; b0 is the zero point offset, b x 、b y 、b z is the offset of each axis;

[0023] Since the magnetometer is installed on the rotorcraft drone and is subject to both soft and hard magnetic interference, the magnetic vector measured by the magnetometer is interfered with. It is expressed as:

[0024]

[0025] Where I is the identity matrix, is the actual magnetic field vector; H soft is the soft magnetic interference matrix, a1-a9 are the soft magnetic interference parameters; H hard is hard magnetic interference, h x 、h y 、h z is the error of each axis;

[0026] Then 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:

[0027]

[0028]

[0029] The interference compensation parameters of the vector magnetometer are obtained by the following formula:

[0030]

[0031] T=[k1 k2 k3 k4 k5 k6 k7 k8 k9 b1 b2 b3] · (18)

[0032] Here, · represents transpose.

[0033] Further, in step S300, the magnetic interference compensation algorithm process based on the recursive total least squares method is as follows:

[0034]

[0035] Among them, X k, are X and The measured data; λ is the forgetting factor; P k is the covariance matrix at time k; T k is the estimated value of compensation parameter at time k; is the noise covariance matrix; Z k is the X matrix at time k and Combination of vectors; K k is the gain of the recursive total least squares method at time k; V k is the eigenvector at time k; V 1:12,k V k Columns 1-12 of V 13,k V k 13th column.

[0036] Furthermore, in step S400, in order to obtain the matrix in real time during the recursive process Use an adaptive exponentially weighted moving average noise covariance estimator to estimate the noise level when acquiring new data:

[0037]

[0038] Among them, ε e is the smoothing factor; x k , are respectively X or Measurements and corresponding exponentially weighted moving average error estimates; is the estimated value of the noise covariance matrix at time k.

[0039] Furthermore, in step S500, in order to improve the magnetic interference compensation effect in the case of insufficient posture, after calculating formula (21), regularization is added to the recursive total least squares method:

[0040] P k '=P k (I+ηP k ) -1 (29)

[0041] Among them, P k ' is the covariance matrix at time k after regularization; η is the regularization factor;

[0042] After calculating formula (24), the residual σ at time k is k Adaptively adjust η:

[0043]

[0044] Among them, S k is the smoothed historical residual; εs is the historical residual smoothing factor; η k is the adaptive regularization factor at time k; η max , η min are the upper and lower limits of η respectively; κ is a small constant used to avoid the denominator being zero.

[0045] Furthermore, the forgetting factor λ is a constant between 0.95 and 1; κ Take the constant 10 -12 .

[0046] 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 in the above rotary-wing UAV vector magnetic interference online compensation method during operation.

[0047] 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.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention discloses an online compensation method and system for vector magnetic interference of a rotary-wing unmanned aerial vehicle. RTLS is applied to the vector magnetic interference compensation, thereby avoiding the problem that when the influence of measurement error is included in the system matrix, the traditional calibration method is biased, resulting in reduced compensation effect. Meanwhile, RTLS only needs to process the measurement data at the current moment, and does not need to perform repeated SVD decomposition on the continuously accumulated data, thereby improving the real-time performance of the compensation. In the recursive process, the noise is quickly estimated through EWMA-NCE, and the noise covariance matrix of RTLS is adjusted in real time to reduce the influence of noise and improve the compensation accuracy under the influence of geomagnetic vector error. In addition, in the recursive process of RTLS, the covariance matrix is ​​adaptively regularized to solve the problem that the compensation parameter is difficult to solve due to insufficient attitude of the rotary-wing unmanned aerial vehicle, thereby reducing the compensation error caused by inappropriate selection of initial regularization parameters and improving the compensation parameter estimation accuracy under the influence of multicollinearity. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of an online compensation method for vector magnetic interference of a rotary-wing UAV in an embodiment of the present invention;

[0051] Figure 2 Schematic diagram of a body coordinate system and a geographic coordinate system in an embodiment of the present invention;

[0052] Figure 3 This is a comparison chart of error standard deviations in the simulation experiment of the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] Specific implementation plan 1: Combine Figure 1 and Figure 2 As shown, the present invention provides a method for online compensation of vector magnetic interference of a rotary-wing UAV, comprising the following steps:

[0055] 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);

[0056] The principle of geomagnetic navigation is to measure the geomagnetic field information of the carrier's operating area and match it with the pre-established geomagnetic reference map to achieve carrier positioning and navigation. In 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.

[0057] 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;

[0058] Due to the change of attitude during flight, the measured value of geomagnetic vector will change at the same point; therefore, we usually need to convert it to geographic coordinate system for actual navigation calculation and analysis; combined with Figure 2 As shown, assuming that o is the origin of the rotorcraft, 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;

[0059] Assuming the geomagnetic vector is the value of the geomagnetic field vector in the aircraft body coordinate system. There is a known transformation matrix R between the aircraft body coordinate system and the geographic coordinate system, where R is a 3×3 rotation matrix, which represents the rotation relationship between the aircraft body coordinate system and the geographic coordinate system; then, the value of the geomagnetic vector in the geographic coordinate system and The relationship can be calculated by the following formula:

[0060]

[0061] Assuming that the roll angle, pitch angle, and heading angle measured by the aircraft's inertial navigation system are φ, θ, and ψ, respectively, R can be calculated by the following formula:

[0062]

[0063] In geomagnetic navigation, three-axis vector fluxgate magnetometers are often used to measure magnetic fields. However, due to the influence of manufacturing process and processing accuracy, three-axis fluxgate magnetometers inevitably have errors such as three-axis non-orthogonality, inconsistent three-axis scale factors, and three-axis zero offset. The calculation of is as follows:

[0064]

[0065] Where I is the identity matrix, is the actual magnetic field vector; S is the scale factor error matrix, s x 、s y 、s z is the scale factor of each axis; P is the three-axis non-orthogonal error matrix, α, β, γ are non-orthogonal angles; b0 is the zero point offset, b x 、b y 、b z is the offset of each axis;

[0066] Assuming that the magnetometer is fixedly connected to the rotorcraft, it is subject to 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 It can be expressed as:

[0067]

[0068] Where I is the identity matrix, is the actual magnetic field vector; H soft is the soft magnetic interference matrix, a1-a9 are the soft magnetic interference parameters; H hard is hard magnetic interference, h x 、h y 、h z is the error of each axis;

[0069] 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:

[0070]

[0071] The interference compensation parameters of the vector magnetometer can be obtained by the following formula:

[0072]

[0073] T=[k1 k2 k3 k4 k5 k6 k7 k8 k9 b1 b2 b3]· (18)

[0074] where · represents transpose;

[0075] S300, real-time compensation of magnetic interference based on RTLS;

[0076] In order to solve the compensation parameters in real time, the recursive least squares method (RLS) is often used for parameter estimation in current research; however, this algorithm assumes that the error only exists in From formula (17), we can see that the system matrix X contains the actual magnetic field vector The acquisition of this vector usually uses 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 calibration method based on RLS may be biased, resulting in a significant reduction in the compensation effect;

[0077] Total least squares (TLS) is a feasible solution. However, the geomagnetic interference compensation scheme based on TLS also requires repeated SVD decomposition of the continuously accumulated data, which greatly reduces the real-time performance. Therefore, the present invention proposes to use recursive total least squares (RTLS) to perform real-time interference compensation. The magnetic interference compensation algorithm based on RTLS is as follows:

[0078]

[0079] Where X k , are X and The measured data; λ is the forgetting factor, which is usually set to a constant between 0.95 and 1; P k is the covariance matrix at time k; T k is the estimated value of compensation parameter at time k; is the noise covariance matrix; Z k is the X matrix at time k and Combination of vectors; K k is the gain of RTLS at time k; V k is the eigenvector at time k; V 1:12,k V k Columns 1-12 of V 13,k V k Column 13 of

[0080] S400, estimating the noise covariance matrix of the measured value by EWMA-NCE;

[0081] In order to obtain the matrix in real time during the recursion process The present invention proposes an adaptive exponentially weighted moving average noise covariance estimator (EWMA-NCE) to quickly estimate the noise level when acquiring new data:

[0082]

[0083] where ε e is the smoothing factor; x k , are respectively X 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;

[0084] S500, adaptively adjusting RTLS regularization parameters based on the current residual;

[0085] In order to solve the problem of difficulty in solving compensation parameters caused by insufficient attitude of rotorcraft UAV, the present invention adaptively regularizes the covariance matrix in the RTLS recursive process to improve the estimation accuracy of compensation parameters under the influence of multicollinearity; the details are as follows:

[0086] Due to the motion characteristics of the rotorcraft UAV carrier, the flight attitude changes are limited during compensation, and the changes in its roll angle and pitch angle are generally within ±20°; this will lead to multicollinearity problems in the system and affect the estimation accuracy of the compensation parameters;

[0087] In order to improve the magnetic interference compensation effect in the case of insufficient posture, the present invention proposes to add regularization to the RTLS algorithm after calculating equation (21):

[0088] P k '=P k (I+ηP k ) -1 (29)

[0089] Among them, P k ' is the covariance matrix at time k after regularization; η is the regularization factor;

[0090] At the same time, after calculating formula (24), the present invention uses the residual σ at time k as k Adaptively adjust η:

[0091]

[0092] Where S k is the smoothed historical residual; ε s is the smoothing factor; η k is the adaptive regularization factor at time k; η max , η minare the upper and lower limits of η; κ is a small constant to avoid the denominator being 0;

[0093] The above steps S100 to S500 are executed until a stop instruction from the user is received, otherwise the steps S100 to S500 are looped.

[0094] Specific implementation method 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.

[0095] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0096] Specific implementation method 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.

[0097] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0098] Simulation experiment

[0099] 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.

[0100] Experimental scenario: The positioning method of the present invention is applied to the simulation of the magnetic interference compensation scenario of the rotorcraft UAV, and compared with the commonly used real-time compensation algorithm RLS. Simulation of the three-axis vector of the geomagnetic field in the environment Set to (29062nT, -4023nT, 41542nT), the UAV carrier error parameters are set as follows:

[0101]

[0102] Assume that during compensation, the drone hovers in place and rotates around its own z-axis for two degrees, while randomly changing the roll angle and pitch angle within the range of ±15 degrees. During operation, the geomagnetic vector measurement value and attitude measurement value are obtained in real time, and Gaussian noise with variances of 10nT and 0.5 degrees are added. A total of 1000 data points are obtained during the compensation process.

[0103] In order 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.

[0104] In this embodiment, the RTLS forgetting factor λ is 0.999, and the EWMA smoothing factor ε e Take 0.1, historical residual smoothing factor εs The regularization parameter η0 is taken as 0.002 at the initial moment; η max , η min Take 10 respectively -4 , 10 -1 κ is 10 -12 .

[0105] Experimental environment: CPU: 12th Gen Intel(R)Core(TM)i5-1240P 1.70GHz, 16G RAM, Windows11.

[0106] 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.

[0107] Table 1

[0108]

[0109] Depend on Figure 3 As shown in Table 1, the use of the compensation algorithm based on RLS can effectively reduce magnetic interference, but the calculated result still has a large error. 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.

[0110] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art 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 protection scope 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 through 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 the recursive total least squares method to obtain a compensated geomagnetic vector parameter estimate; S400, estimating the noise covariance matrix of the measured 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 from the user is received, 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 UAV. In the body coordinate system, the front, right, and bottom of the rotorcraft UAV 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 is characterized in that: In step S200, let the geomagnetic vector is the value of the geomagnetic field vector in the aircraft body coordinate system, let the transformation matrix R be the rotation matrix of the aircraft 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 by the following formula: Assuming that the roll angle, pitch angle, and heading angle measured by the aircraft's inertial navigation system are φ, θ, and ψ, respectively, R is calculated by the following formula: R=R z (ψ)·R y (θ)·R x (f) (5) Geomagnetic measurement vector with errors As shown below: Where I is the identity matrix, is the actual magnetic field vector; S is the scale factor error matrix, s x 、s y 、s z is the scale factor of each axis; P is the three-axis non-orthogonal error matrix, α, β, γ are non-orthogonal angles; b0 is the zero point offset, b x 、b y 、b z is the offset of each axis; Since the magnetometer is installed on the rotorcraft drone and is subject to both soft and hard magnetic interference, the magnetic vector measured by the magnetometer is interfered with. It is expressed as: Where I is the identity matrix, is the actual magnetic field vector; H soft is the soft magnetic interference matrix, a1-a9 are the soft magnetic interference parameters; H hard is hard magnetic interference, h x 、h y 、h z is the error of each axis; Then 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: T=[k1 k2 k3 k4 k5 k6 k7 k8 k9 b1 b2 b3] (18) Here, · 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: Among them, X k , are X and The measured data; λ is the forgetting factor; P k is the covariance matrix at time k; T k is the estimated value of compensation parameter at time k; is the noise covariance matrix; Z k is the X matrix at time k and Combination of vectors; K k is the gain of the recursive total least squares method at time k; V k is the eigenvector at time k; V 1:12,k V k Columns 1-12 of V 13,k V k 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 Use an adaptive exponentially weighted moving average noise covariance estimator to estimate the noise level when acquiring new data: Among them, ε e is the smoothing factor; x k , are respectively X 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 is characterized in that: In step S500, in order to improve the magnetic interference compensation effect in the case of insufficient posture, regularization is added to the recursive total least squares method after calculating formula (21): P k '=P k (I+ηP k ) -1 (29) Among them, P k ' is the covariance matrix at time k after regularization; η is the regularization factor; After calculating formula (24), the residual σ at time k is k Adaptively adjust η: Among them, S k is the smoothed historical residual; ε s is the historical residual smoothing factor; η k is the adaptive regularization factor at time k; η max , η min are the upper and lower limits of η respectively; κ is a small constant used to avoid the denominator being zero.

7. The online compensation method for vector magnetic interference of a rotary-wing UAV according to claim 6 is characterized in that: The forgetting factor λ is a constant between 0.95 and 1; κ Take the constant 10 -12 .

8. A rotary wing UAV vector magnetic interference online compensation system, characterized by: The system has a program module corresponding to the steps of any one of claims 1 to 8 above, and executes the steps in the above-mentioned online compensation method for vector magnetic interference of 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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