Airborne Magnetic Anomaly Navigation with Neural-Network-Augmented Online Calibration

Airborne Magnetic Anomaly Navigation (MagNav) provides a jamming-resistant and robust alternative to satellite navigation but requires the real-time compensation of the aircraft platform’s large and dynamic magnetic interference. State-of-the-art solutions often rely on extensive offline calibration flights or pretraining, creating a logistical barrier to operational deployment. This paper presents a fully adaptive MagNav architecture featuring a cold-start capability that identifies and compensates for the aircraft’s magnetic signature entirely in flight. The proposed method uses an extended Kalman filter with an augmented state vector that simultaneously estimates the aircraft’s kinematic states as well as the coefficients of the physics-based Tolles–Lawson calibration model and the parameters of a neural network to model aircraft interferences. The Kalman filter update is mathematically equivalent to an online natural gradient descent, integrating superior convergence and data efficiency of state-of-the-art second-order optimization directly into the navigation filter. To enhance operational robustness, the neural network is constrained to a residual learning role, modeling only the nonlinearities uncorrected by the explainable physics-based calibration baseline. Validated on the MagNav Challenge data set, this paper’s framework effectively bounds inertial drift using a magnetometer-only feature set. The results demonstrate navigation accuracy comparable to state-of-the-art models trained offline, without requiring prior calibration flights or dedicated maneuvers.