Abstract
The fluxgate magnetometer has the advantages of having a small volume and low power consumption and being light weight and is commonly used to detect weak magnetic targets, including ferrous metals, unexploded bombs (UXOs), and underground corrosion pipelines. However, the detection accuracy of the fluxgate magnetometer is affected by its own error. To obtain more accurate detection data, the sensor must be error-corrected before application. Previous researchers easily fell into the local minimum when solving error parameters. In this paper, the error correction method was proposed to tackle the problem, which combines the Dragonfly algorithm (DA) and the Levenberg–Marquardt (LM) algorithm, thereby solving the problem of the LM algorithm and improving the accuracy of solving error parameters. Firstly, we analyzed the error sources of the three-axis magnetic sensor and established the error model. Then, the error parameters were solved by using the LM algorithm and DA–LM algorithm, respectively. In addition, by comparing the results of the two methods, we found that the error parameters solved by using the DA–LM algorithm were more accurate. Finally, the magnetic measurement data were corrected. The simulation results show that the DA–LM algorithm can accurately solve the error parameters of the triaxial magnetic sensor, proving the effectiveness of the proposed algorithm. The experimental results show that the difference between the corrected and the ideal total value was decreased from 300 nT to 5 nT, which further verified the effectiveness of the DA–LM algorithm.
Highlights
Magnetic anomaly detection can be used to detect and locate magnetic targets based on magnetic anomaly, which is a passive detection method based on basic physical phenomena
The magnetic core of the three-axis magnetic sensor is made of s rial with high permeability and low coercivity, which reaches its satu where L x refers to the sensitivity factor of the x axis, Ly stands for the sensitivity factor of the y axis, Lz represents the sensitivity factor of the z axis, and L is the error matrix of the sensitivity factor
In solving the error parameters, we found that the number of least squares solutions were limited, while the LM algorithm can be used to solve multiple parameters simultaneously
Summary
The Earth’s magnetic field is its inherent physical field. it cannot be seen or felt, it is always there and closely related to human life [1].Magnetic objects or ferromagnetic materials magnetized by the Earth’s magnetic field and moving conductors cutting through the Earth’s magnetic field generate the eddy current magnetic field and cause disturbances to the Earth’s magnetic field, which is called magnetic anomalies. There are two common error correction methods for magnetic sensors, that is, the vector correction and the scalar correction The former is first to compare with the known magnetic field vector [10,11] and makes correction to the measured data. In [15], the use of a genetic algorithm was proposed to solve the parameters in the error model, and good results were achieved. The above algorithms were able to achieve good correction effects, the least square method for solving parameters in [12,13] had a great impact on the parameter compensation of abnormal points in the sensor sampling process.
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