# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 1 Introduction
Considering the lack of computational accuracy and generalization ability of linear regression methods, people began to explore new aeromagnetic compensation algorithms through neural networks. [PERSON] successfully established an aeromagnetic compensation model based on a neural network for the first time, but his model had the problem of overfitting [21]. [PERSON] proposes a new compensation method that used a one-dimensional convolutional neural network to perform secondary compensation on the data that were compensated by the T-L model to eliminate the influence of tail boom swing, which has a significant compensation effect on aeromagnetic noise [22]. [PERSON] proposed a dual estimation method for aeromagnetic compensation, combining a linear model with a neural network to improve the accuracy of magnetic compensation [23]. Although the above two methods improved the accuracy of aeromagnetic compensation, there were also problems, such as difficult parameters selection and complex network structures in design. [PERSON] et al. proposed an aeromagnetic compensation algorithm based on deep autoencoder (DAE) [24], which reduced the multicollinearity between variables in the T-L equation, but it was not perfect for the feature extraction of high-dimensional complex data in the training process of the autoencoder network, and the local minimum problem easily occurred. They then proposed to use a generalized regression neural network (GRNN) to establish an aeromagnetic compensation model, which had a fast calculation speed, high compensation accuracy, and no backpropagation [25]. Although they solved the problem of over fitting, the problem of gradient disappearance was not considered. The aeromagnetic compensation method based on a neural network still has some problems to be solved, such as local minimum problems in backpropagation, difficult parameters selection, and complex network structures. In order to further improve the accuracy of aeromagnetic compensation, this paper proposed, for the first time, a magnetic compensation method based on BRF-ANN, which is widely used in function approximation, pattern recognition, and signal processing [26]; it is also widely used in aerospace, such as the longitudinal channel flight control of small UAVs [27], the navigation of UAVs [28], issues related to the optimization of UAV [29], and so on. The hidden nodes of RBF-ANN adopted the distance between the input mode and the center vector (such as the Euclidean distance) as the independent variable of the function and used the radial basis function (such as the Gaussian function) as the activation function, which is a local approximation network with better generalization ability and a simple network design. The paper is divided into five chapters. The first and last chapters present the introduction and conclusion. The second chapter discusses compensation models and methods, introducing the T-L model and the principles of BP-ANN and RBF-ANN, including the characteristics of RBF-ANN. The third chapter introduces data simulation and testing, and the fourth chapter shows the testing of the measured data; the results are displayed in graphs and tables and a method effectiveness analysis was also carried out. The application and analysis of theoretical synthetic data and real measured aeromagnetic compensation data showed that the proposed method effectively solved the problem of high-precision compensation of aeromagnetic survey data based on rotary wing UAV platform, and greatly improved the error compensation accuracy of aeromagnetic dynamic measurement data.