Magnetometer Calibration Method Based on Double Objective Function Particle Swarm Optimization

Abstract

The invention relates to a magnetometer correction method based on double objective function particle swarm optimization, which belongs to the field of sensor calibration. Including analyzing the various errors of the magnetometer to establish a magnetometer error model; establishing the dual objective function in the particle swarm optimization algorithm; calculating the magnetometer error parameters according to the particle swarm optimization algorithm based on von Neumann topology. The invention solves the problem of low correct rate caused by the lack of constraints on the three-axis magnetic field components when the traditional particle swarm algorithm is used for magnetometer calibration; the invention adopts the Von Neumann topology, which effectively improves the particle's tendency to fall into local optimum The situation that the parameters cannot be estimated enhances the global search ability of the algorithm; the present invention does not require a large amount of data, good initial value conditions and noise distribution, and is easy to operate. It has been proved by simulation experiments that the correct rate of corrected parameters can reach more than 95%. And in the case of a large error in the gyroscope, the algorithm is simple, has high engineering practicability, and has broad application prospects.

Description

technical field [0001] The invention relates to a magnetometer correction method based on double objective function particle swarm optimization, which belongs to the field of sensor calibration. Background technique [0002] The magnetometer can measure the magnetic field components on the three axes of the geomagnetic field according to the inherent geomagnetic field information of the earth, so as to calculate the total geomagnetic field strength and heading angle information, so it is widely used in the attitude reference system. However, in the manufacturing process of the magnetometer, it is difficult to ignore its own errors, and it is extremely susceptible to the influence of the surrounding soft and hard magnets. These factors will affect the measurement accuracy of the magnetometer and the calculation accuracy of the heading angle, which requires compensation and correction before the magnetometer is used. [0003] For the problem of magnetometer compensation and co...

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Problems solved by technology

The above-mentioned algorithms have complex operations, many sampling points, and high initial value requirements. For these problems, the literature (Wu Z, Wu Y, Hu X, et al. Calibration of three-axis strapdown magnetometers using particle swarm optimization algorithm[C] / / Robotic and Sensors Environments (ROSE), 2011IEEE International Symposium on.IEEE, 2011:160-165.) proposed to apply the particle swarm optimization algorithm PSO (Particle Swarm Optimization) to the magnetometer calibration. The algorithm selects the appropriate target Function solves the problem of finding the global optimum

Literature (Wu Z, Wu Y, Hu X, etal.Calibration of three-axis magnetometer using stretching particle swarmoptimization algorithm[J].IEEE Transactions on Instrumentation and Measurement,2013,62(2):281-292.) for particles easy to fall into For the local optimal problem, an enhanced particle swarm optimization algorithm SPSO (Stretching Particle Swarm Optimization) is proposed to enhance the robustness of the algorithm, but there are many sampling points and a large amount of calculation

At present, the particle swarm optimization algorithm is used for magnetometer calibration. Most of the objective functions that constrain the total magnetic field are selected, and the constraints on the three-axis magnetic field components are ignored, resulting in the error parameter estimation error.

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