Robust Magnetic Anomaly Navigation under Changing Magnetic Field Gradients

Magnetic Anomaly Navigation (MagNav) is a promising navigation method that uses the Earth’s crustal magnetic field as a position reference, offering a resilient alter-native for aircraft positioning in environments where satellite navigation is not available. A key challenge for MagNav is performance degradation and potential navigation filter divergence when traversing magnetically flat regions (small or non-existent anomalies) where position observability is weak. This paper presents a robustified navigation algorithm based on an error-state Kalman filter on the subgroup of the special Euclidean group of three (SE2(3)). We compare static and innovation-based measurement covariance tuning strategies with a novel gradient-adaptive scheme that dynamically adjusts the assumed magnetometer measurement’s covariance based on the local magnetic field variability, which is known a priori from the magnetic anomaly map. Through Monte Carlo simulations, we demonstrate that while an optimistic static tuning gives the best results for a high-observability scenario, it fails in challenging low-gradient, low-velocity scenarios. In contrast, both dynamic noise adaptation methods and conservative static noise could improve convergence rates. A conservative fixed-measurement-noise assumption consistently showed superior robustness and lower position error. These insights are paving the way for more resilient full-scale deployments of airborne MagNav systems.