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.

A magnetic sensor array is usually used for weak magnetic target detection. This paper presents a vector second-order gradiometer that is arranged in a vertical structure. The measurement accuracy of the gradiometer is affected by the error of each three-axis magnetometer (TAM) and the misalignment error between TAMs. By analyzing the error mechanism of the magnetic gradiometer test system, the corresponding error model for the correction of the gradient of the component is established. The error parameters of the gradiometer were estimated by the proposed method, and the gradient error of the magnetic field and the component gradient error of the magnetic field were corrected. After correction, the gradiometer is used to localize the magnetic target. Simulation results illustrate that the localization error of magnetic targets is reduced by two orders. Thus, this proposed correction method can effectively improve the locating performance of gradiometer.