情報理論的幾何最適化と物理認識学習によるキャリブレーションフリー磁気位置推定
Information-Theoretic Geometry Optimization and Physics-Aware Learning for Calibration-Free Magnetic Localization
永久磁石のワイヤレス位置推定の精度を向上させるため、センサ配置の情報理論的最適化と物理認識深層学習を組み合わせた統一フレームワークを提案し、実世界実験で高精度・高速な位置推定を実現した。
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著者: Wenxuan Xie, Yuelin Zhang, Qingpeng Ding, Jianghua Chen, Jiewen Tan, Jiwei Shan, Shing Shin Cheng
分類: cs.RO
原文アブストラクト
Wireless localization of permanent magnets enables occlusion-free guidance for medical interventions, yet its practical accuracy is fundamentally limited by two coupled challenges: the poor observability of conventional planar sensor arrays and the simulation-to-reality (Sim-to-Real) gap of learning-based estimators. To address these issues, this article presents a unified framework that combines information-theoretic sensor geometry optimization with physics-aware deep learning. First, a rigorous Fisher Information Matrix (FIM)-based evaluation framework is established to quantify geometry-induced observability limitations. The results show that a staggered split-array topology provides a substantially stronger observability foundation for localization while remaining compatible with practical external deployment. Second, building on this optimized sensing configuration, we propose Phy-GAANet, a calibration-free estimator trained entirely on hardware-aware synthetic data. By incorporating Physics-Informed Features (PIF) for saturation modeling and Geometry-Aware Attention (GAA) for preserving cross-layer vector structure, the network effectively bridges the Sim-to-Real gap. Extensive real-world experiments demonstrate state-of-the-art performance, achieving a position error of 1.84 mm and an orientation error of 3.18 degrees at a refresh rate exceeding 270 Hz. The proposed method consistently outperforms classical Levenberg--Marquardt solvers and generic convolutional baselines, particularly in suppressing catastrophic outliers and maintaining robustness in challenging near-field boundary regions. Beyond the proposed network, the FIM-guided analysis also provides a framework for sensor geometry design in magnetic localization systems under practical deployment constraints.
