Compensation of Platform Magnetic Interference via Coil-Calibrated Physical Model and Attention Network

Abstract

Accurate compensation of magnetic interference generated by moving platforms is essential for reliable geomagnetic navigation. Conventional Tolles–Lawson (T–L) methods require dedicated high-altitude maneuvers under a clean magnetic background and bandpass filtering, which introduce systematic errors and are impractical for platforms lacking maneuverability. To address these limitations, this article proposes a hierarchical compensation framework that synergistically combines physical model calibration with data-driven refinement. First, a Helmholtz coil generates a known true field, enabling direct extraction of platform interference without filtering. An Adaptive transient-aliasing-outlier (ATAO) suppression algorithm automatically removes coil transient effects, performs antialiasing decimation, and eliminates outliers. A truth-guided ridge regression (TGRR) estimator exploits the known true field to select the optimal regularization parameter deterministically, yielding an unbiased and stable T–L parameters. The remaining nonlinear residuals—which the linear T–L model cannot capture—are then compensated by a deep neural network (DNN) with wavelet decomposition and an attention mechanism. Experimental results on an uncrewed ground vehicle demonstrate that coil-based calibration improves parameter accuracy by approximately 48.0% compared to conventional methods. The complete hierarchical method achieves final compensation errors of $16.55~\pm ~1.69$ nT (Route 1, training), and $20.24~\pm ~1.12$ nT, $3.62~\pm ~0.29$ nT, and $40.13~\pm ~3.13$ nT (Routes 2–4, testing), respectively. These results verify the performance and generalization ability of the proposed method.