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 creating a synthetic aperture through sequential mechanical motion. However, existing MA-based sensing methods typically rely on exhaustive scanning over the entire movable plate, resulting in significant control overhead and sensing latency, which limits 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 comprises two steps. First, the movable plate's three-dimensional orientation is optimized only once to maximize path visibility while preserving path discriminability, both induced from Fisher information analysis. Second, only two predetermined linear MA scans are made on the tilted plate to estimate the elevation and azimuth AoAs from the resulting sequence of received signals. By incorporating the prior AoA statistics, a maximum a posteriori (MAP)-based AoA estimation algorithm is developed. With only one orientation control and two linear scans, the proposed framework enables agile multi-path sensing with significantly reduced control overhead and latency, while achieving AoA estimation accuracy approaching that of the single-path benchmark.

翻译：多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）通过顺序机械运动创建合成孔径，从而能够实现这一功能。然而，现有基于MA的感知方法通常依赖对整个可移动板的穷举扫描，导致显著的控制开销和感知延迟，限制了其在敏捷感知场景中的实用性。为此，本文提出了一种先验引导的敏捷多径感知框架，该框架将弱先验到达角（AoA）统计信息作为辅助信息。所提框架包含两个步骤：首先，基于Fisher信息分析，仅需一次优化便确定可移动板的三维朝向，以最大化路径可见性并保持路径可区分性；其次，仅在倾斜板上执行两次预定的线性MA扫描，并从接收信号序列中估计仰角和方位角AoA。通过结合先验AoA统计信息，开发了基于最大后验概率（MAP）的AoA估计算法。该框架仅需一次朝向控制与两次线性扫描即可实现敏捷多径感知，显著降低了控制开销与延迟，同时达到接近单径基准的AoA估计精度。