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

TL;DR

This work presents a fully adaptive MagNav architecture featuring a "cold-start" capability that identifies and compensates for the aircraft's magnetic signature entirely in-flight, without requiring prior calibration flights or dedicated maneuvers.

Abstract

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 pre-training, creating a logistical barrier to operational deployment. We present 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 utilizes 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 dataset, our 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.

Figures (32)

• Figure 1: Hybrid MagNav Architecture with NN-augmented calibration model.
• Figure 2: Mean position error distribution and average runtimes from 100 Monte Carlo simulations for input parameter sets $\bm{\phi}_{\text{m}}$, $\bm{\phi}_{\text{mv}}$, and $\bm{\phi}_{\text{all}}$ for different numbers of hidden neurons.
• Figure 3: Magnetic-Anomaly aided odometry with NN-online-learning based platform interference compensation for four trajectories ($\bm{\phi} = \bm{\phi}_{\text{m}}$, $N_h = 8$).
• Figure 4: NN-EKF performance on the lawnmower trajectory ($\bm{\phi}_{\text{m}}$) for $N_h = 2$ and $N_h = 8$ showing position RMSE improvement compared to odometry in (a) and platform interference approximation in (b).
• Figure 5: EKF-based online adaptation of the NN parameters $\bm{x}_{\text{NN}}$ for $\bm{\phi} = \bm{\phi}_{\text{m}}$, $N_h = 8$ for the (a) Figure Eight, (b) Irregular, (c) Lawnmower, and (d) Spiral trajectories.