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.

翻译：永磁体的无线定位能够为医疗介入提供无遮挡的引导，然而其实际精度从根本上受限于两个相互关联的挑战：传统平面传感器阵列的可观测性不足，以及基于学习的估计器存在的仿真到现实（Sim-to-Real）差距。针对这些问题，本文提出了一个统一框架，将信息论驱动的传感器几何优化与物理感知深度学习相结合。首先，建立了基于严格Fisher信息矩阵（FIM）的评估框架，以量化几何因素导致的可观测性限制。结果表明，交错分列式阵列拓扑能为定位提供显著更强的可观测性基础，同时保持与外部实际部署的兼容性。其次，基于这一优化的感知配置，我们提出了Phy-GAANet——一种完全基于硬件感知合成数据训练的免校准估计器。通过引入用于饱和建模的物理信息特征（PIF）和用于保持跨层向量结构的几何感知注意力（GAA），该网络有效弥合了仿真到现实的差距。大量真实世界实验展示了其最先进的性能，在超过270 Hz的刷新率下实现了1.84 mm的位置误差和3.18度的姿态误差。所提出方法始终优于经典的Levenberg-Marquardt求解器和通用卷积基线，特别是在抑制灾难性离群值及在具有挑战性的近场边界区域保持鲁棒性方面。除了所提出的网络，FIM引导的分析也为实际部署约束下的磁定位系统传感器几何设计提供了框架。