AI 中文总结
本文提出一种先验引导的可移动天线多路径感知框架，通过一次方向调整和两次线性扫描，在降低控制开销与延迟的同时，实现接近单路径基准的到达角与到达时间估计精度。
AI 中文摘要
多路径感知旨在提取多条传播路径的几何属性，有望成为6G的关键功能。可移动天线（MA）可通过机械运动合成孔径实现该功能，但现有基于MA的感知方法通常依赖对整个移动区域的穷举扫描，导致显著的控制开销和感知延迟，限制了其在敏捷感知中的实用性。为应对这一挑战，本文提出一种利用弱先到达角（AoA）统计作为辅助信息的先验引导敏捷多路径感知框架，该框架包含两个关键步骤：第一步，基于费舍尔信息分析，仅优化一次移动板的三维方向，以配置机械可行的扫描区域，该区域可增强路径可见性，同时保持路径间的可区分性；第二步，在最优板方向下，MA仅执行两次线性扫描，将其非共线空间相位投影与先AoA统计通过基于最大后验概率（MAP）的估计器融合，以恢复多条路径的仰角和方位角AoA；随后利用估计的AoA提取到达时间（ToA），方法是增强目标路径分量，同时抑制其他路径的干扰。仅通过一次方向调整和两次线性扫描，所提框架可实现敏捷多路径感知，大幅降低控制开销和延迟，同时达到接近单路径基准的AoA和ToA估计精度。
英文摘要
Multi-path sensing, which aims to extract the geometric attributes of multiple propagation paths, is expected to be a key functionality of 6G. A movable antenna (MA) can enable this functionality by synthesizing an aperture through mechanical motion. However, existing MA-based sensing methods typically rely on exhaustive scanning over the entire movable region, resulting in significant control overhead and sensing latency, which limit their practicality for agile sensing. To address this challenge, this paper develops a prior-guided agile multi-path sensing framework that leverages weak prior angle-of-arrival (AoA) statistics as side information. The proposed framework is built on two key steps. First, the movable plate's three-dimensional orientation is optimized only once to configure a mechanically feasible scan region that enhances path visibility while preserving inter-path discriminability, guided by Fisher information analysis. Second, given the optimal plate orientation, the MA performs only two linear scans, whose non-collinear spatial phase projections are fused with the prior AoA statistics through a maximum a posteriori (MAP)-based estimator to recover the elevation and azimuth AoAs of multiple paths. The estimated AoAs are subsequently used to extract the times-of-arrival (ToAs) by enhancing the target path component while suppressing interference from other paths. With only one orientation adjustment and two linear scans, the proposed framework enables agile multi-path sensing with significantly reduced control overhead and latency, while achieving AoA and ToA estimation accuracy close to the single-path benchmark.
