AI 中文总结
本文针对经典Tolles-Lawson框架的不足，提出两阶段磁补偿框架，利用增广线性化模型和无地图KFF算法，在公开数据集上将平均补偿误差降至12.3nT，提升了航空磁补偿性能。
AI 中文摘要
机载平台的航空磁补偿对于实时跟踪不受平台干扰的动态外部磁场至关重要，可实现稳健的磁导航（MagNav）、磁异常检测（MAD）及其他地球物理应用。经典的Tolles-Lawson（TL）框架重点关注补偿模型参数的估计，但对动态外部磁场的实时估计关注较少。为弥合这一差距，本文提出一种两阶段校准与补偿框架：第一，开发用于基于传感器的校准参数估计的增广线性化模型，避免经典带通滤波（BPF）固有的信息损失；第二，利用这些预估计参数，开发无地图卡尔曼滤波融合（KFF）算法，直接从受平台干扰的多传感器测量值中动态估计外部磁场并补偿平台干扰，该步骤无需参考位置数据（这与MagNav的目的相悖）或先验异常地图即可实现实时联合动态估计与多传感器补偿。在公开DAF-MIT MagNav数据集上验证，该框架克服了现有方法的非因果限制，将平均补偿误差从359.2nT降至12.3nT，同时对严重损坏的传感器通道保持出色的稳健性。
英文摘要
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.
Comments16 pages, 10 figures, submitted to Journal of Advances in Information Fusion (JAIF) on 3rd Aug 2026
