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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. [ABSTRACT FROM AUTHOR]

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.

The non-orthogonal error exists in a three-axis magnetic sensor due to the limitations of the machining and installation processes. A non-orthogonal error model of three-axis magnetic sensor is established to correct the error and obtain accurate triaxial magnetic data, and the model is solved by using the least squares method. Four intelligent optimization algorithms are used to solve the least-squares problem, and the invasive weed algorithm is improved. Simulated and experimental results show that the least squares error correction method based on the improved invasive weed optimization （IWO）algorithm can be used to effectively correct the non-orthogonal errors of three-axis magnetic sensors by reducing the motion noise of 85.42% and 86.5%, respectively. [ABSTRACT FROM AUTHOR]

The differential magnetic gradient tensor system is usually constructed from the three-axis magnetic sensor array, thus making the misalignment error between the sensors become the main source of the measurement error. In addition, the sensor axial biases, scale factors, non-orthogonality, and external magnetic distortion fields severely affect the tensor measurement accuracy simultaneously when the system is applied as a strapdown device. Considering the systematic error and the magnetic interferences, the integrated error coefficient matrix and the integrated bias vector is constructed according to the magnetic field mathematical model of the hard and soft ferromagnetic material, and the integrated compensation model of the sensor is established based on the least-squares (LS) ellipsoid fitting. The rotation matrixes of the roll, pitch and yaw conversion of the sensors are constructed, and the misalignment error calibration model of the sensor array is obtained. The two linear equations that rotate only about one axis are extracted and the misaligned angles are estimated by the LS method to calibrate the tensor system. Simulation and experimental results show that the proposed method compensates the integrated error of magnetic sensor effectively, and requires only a few measurement data to align the sensor array accurately, which efficiently improves the measurement accuracy of the magnetic gradient tensor system. [ABSTRACT FROM AUTHOR]