# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (G.F.)  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (G.F.)  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, No. regression method was proposed to improve the accuracy of the estimated coefficients. [PERSON] [16; 17] analyzed the stability of the aeromagnetic compensation algorithm and provided a method to evaluate the reliability of calibration flight results. [PERSON] et al. [18; 19] applied the wavelet analysis and recursive least squares methods to aeromagnetic compensation. The capability and the limitation of the two methods were also discussed. [PERSON] and [PERSON] et al. [20; 21] have also conducted in-depth research on aeromagnetic data preprocessing, compensation methods, and the influence of fluxgate on the compensation performance. In addition, many scholars have proposed methods to improve the accuracy of aeromagnetic compensation, for example, an extended aeromagnetic compensation model was proposed in [22], which takes the non-maneuvering interference into account, and its effectiveness was verified by a series of experiments. [PERSON] et al. [23] proposed a multi-model aeromagnetic compensation method to mitigate the impact of multicollinearity and improve the quality of the measurements. A higher improvement ratio was obtained by the proposed method compared to the other two conventional methods, which means that this method has a better effect of interference suppression. An improved neural network method for aeromagnetic compensation was proposed by [PERSON] et al. [24] that was based on the linear regression equations and the generalized regression neural network (GRNN). The generalization of the GRNN-based compensation model was improved, and the effect of overfitting was weakened by using proper smoothing factors and multiple training sets. Some researchers have studied magnetometer calibration methods based on the unmanned aerial vehicle (UAV) platforms, for example, [PERSON] [25] proposed a method to improve the accuracy of heading angle estimation by using cooperative flight, and the determination of the magnetic biases' problem was formulated as a set of non-linear equations. An improved magnetometer calibration and compensation method based on the Levenberg-Marquardt algorithm for multi-rotor UAV was proposed in [26], and the experimental results indicated that the accuracy of the magnetometer in the yaw angle estimation had been greatly enhanced compared to the traditional ellipsoid fitting method. The total magnetic field, measured by an optically pumped magnetometer (OPM), includes the geomagnetic field and the magnetic interference field. For the traditional compensation method, the geomagnetic gradient is usually considered a constant value, which is negligent after bandpass filtering. However, the geomagnetic field is a function of the spatial position and time, as described by the International Geomagnetic Reference Field (IGRF) model [27]. Based on the latest 13 th generation IGRF model published by the International Association of Geomagnetism and Aeronomy (IAGA), the global geomagnetic intensity is shown in Figure 1. According to the requirements of standard calibration flight, the distance of profiles in each direction ranges from a few kilometers to more than ten kilometers, resulting in a geomagnetic gradient of several nanoTeslas or even hundreds of nanoTeslas. For the high-precision magnetic survey system, this is interference that cannot be ignored.