Interference Model Guided Neural Network for Aeromagnetic Compensation

Magnetic interference of the aircraft platform is one of the primary restrictions for aeromagnetic missions, like magnetic anomaly detection (MAD) and geomagnetic navigation. In recent years, two main paradigms have emerged for aeromagnetic compensation: model-driven methods based on the linear Tolles-Lawson (T-L) model and data-driven methods utilizing neural networks. However, model-driven methods struggle with nonlinear interference compensation, while data-driven methods exhibit a high dependence on the quality and quantity of available data. To address the limitations of existing paradigms, this article introduces a novel approach: an interference model-guided neural network for aeromagnetic compensation. This method leverages the strengths of both model-driven and data-driven techniques. The synergy is achieved through the development of a network initialization method based on T-L estimation and the incorporation of an interference model-embedded loss function. With the proposed method, aeromagnetic compensation can consider both linear and nonlinear interferences, and eliminate them even with limited and noisy data without significant accuracy degradation. The proposed method’s efficacy was validated through real flight tests. Results demonstrate that the proposed method outperforms both the model-driven and data-driven methods, with standard deviations (STDs) reaching 0.0550 nT, compared to 0.0812 and 0.1306 nT, respectively.