Fusion Based Dynamic Platform Magnetic Compensation Beyond the Tolles Lawson Approach

Abstract

Aeromagnetic compensation for airborne platforms is essential for real-time tracking of the dynamic external magnetic field free from platform interference, enabling robust magnetic navigation (MagNav), magnetic anomaly detection (MAD), and other geophysics applications. While the classical Tolles-Lawson (TL) framework focuses extensively on estimating compensation model parameters, it pays less attention to the real-time estimation of the dynamic external magnetic field. To bridge this gap, this paper proposes a two-stage calibration and compensation framework. First, an augmented linearized model is developed for sensor-based calibration parameter estimation, avoiding the information loss inherent in classical band-pass filtering (BPF). Second, utilizing these pre-estimated parameters, a map-less Kalman Filter Fusion (KFF) algorithm is developed to dynamically estimate the external field and compensate the platform interference directly from multi-sensor measurements corrupted by platform interference. The second step enables real-time joint dynamic estimation and multi-sensor compensation without requiring reference position data (which runs counter to the MagNav purpose) or prior anomaly maps. Validated on the public DAF-MIT MagNav dataset, the framework overcomes the non-causal limitations of existing approaches, reducing average compensation error from 359.2~nT to 12.3~nT while maintaining exceptional robustness against heavily corrupted sensor channels.