An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network

Aeromagnetic compensation is the main method for eliminating magnetic interference on flight platforms. With the improved sensitivity of aeromagnetic sensors, the generalization and compensation accuracy of existing aeromagnetic compensation methods have become insufficient to meet the needs of current aeromagnetic survey applications. In this article, we propose an aeromagnetic compensation method based on temporal convolutional networks to improve both generalization and compensation accuracy. In the proposed method, a neural network based on separable convolution with a residual connection is employed to improve the compensation accuracy and convergence stability, and a gradient correction loss function based on the Tolles–Lawson model is used to improve the generalization ability. We conducted experiments based on both simulated and real datasets and compared typical neural network compensation methods proposed by previous researchers. The results indicate that the method proposed in this article can achieve a lower standard deviation of residual magnetic interference than other neural network methods, demonstrating a better compensation performance and generalization ability.