Abstract
In order to improvethe yaw angle accuracy of multi-rotor unmanned aerial vehicle and meet the requirement of autonomous flight, a new calibration and compensation method for magnetometer based on Levenberg–Marquardt algorithm is proposed in this paper. A novel mathematical calibration model with clear physical meaning is established. “Hard iron” error and “Soft iron” error of magnetometer which affect the yaw accuracy of unmanned aerial vehicle are compensated. Initially, Levenberg–Marquardt algorithm is applied to the process of sphere fitting for the original magnetometer data; the optimal estimation of sphere radius and initial “Hard iron” error are obtained. Then, the ellipsoid fitting is performed, and the optimal estimation of “Hard iron” error and “Soft iron” error are obtained. Finally, the calibration parameters are used to compensate for the magnetometer’s output during unmanned aerial vehicle flight. Traditional ellipsoid fitting based on least squares algorithm is taken as reference to prove the effectiveness of the proposed algorithm. Semi-physical simulation experiment proves that the proposed magnetometer calibration method significantly enhances the accuracy of magnetometer. Static test shows that the yaw angle error is reduced from 1.2° to 0.4° when using the proposed calibration model to calibrate magnetometers. In dynamic tests, the sensor MTi’s output is used as reference. The data fusion of magnetometer compensated by the proposed new calibration model based on Levenberg–Marquardt algorithm can accurately track the desired attitude angle. Experimental results indicate that the accuracy of magnetometer in the yaw angle estimation has been greatly enhanced. In the process of attitude estimated, the compensation magnetometer data given by this new method have faster convergence speed, higher accuracy, and better performance than the compensation magnetometer data given by traditional ellipsoid fitting based on least squares algorithm.
Highlights
In recent years, with the development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in various fields
It shows that traditional ellipsoid fitting based on the least squares (L-S) algorithm assumes that the magnetic material fixed on the UAV distorts the magnetic field greatly, while the algorithm proposed in this paper does
This paper proposes an improved magnetometer calibration method based on the L-M algorithm
Summary
With the development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in various fields. According to the magnetic field characteristics of UAV, this paper innovatively combines the traditional sphere fitting and ellipsoid fitting algorithm, and proposes a new magnetometer calibration model, which can be used to compensate the ‘‘Hard iron’’ error and ‘‘Soft iron’’ error of magnetometer on multi-rotor UAV. The L-M algorithm is used to fit the original magnetometer data into a sphere, and the optimal estimation of sphere radius and initial ‘‘Hard iron’’ error are obtained. The L-M algorithm is applied to the process of the ellipsoid fitting to calculate the optimal estimation of ‘‘Hard iron’’ error and ‘‘Soft iron’’ error. Of squares of functions.[18,19] This algorithm is applied to the sphere and the ellipsoid fitting of magnetometer measurement data to obtain ‘‘Hard iron’’ error and ‘‘Soft iron’’ error.
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