发表机构
Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出可移动天线赋能的多目标无线感知方案，通过位置优化抑制旁瓣干扰，实现高精度且稳健的目标参数估计。
AI 中文摘要
可移动天线（MA）已成为一种有前景的技术，可为无线感知系统解锁空间自由度。然而，由于阵列几何形状与未知目标方向之间的复杂耦合，以及严重的旁瓣干扰，优化用于多目标感知的MA位置仍然具有挑战性。在本文中，我们提出了一种MA赋能的多目标无线感知方案，以实现高精度且稳健的目标参数估计。为了在保持可处理性的同时表征感知性能，我们引入了两个互补的度量，即克拉美-罗界（CRB）的下界（LB）和导向矢量相关性（SVC）。具体而言，通过研究Fisher信息矩阵的偏序关系，我们推导了多目标CRB的闭式LB，该LB解耦了未知目标方向。同时，采用SVC来表征不同方向上感知信号之间的空间相关性和旁瓣引起的干扰。基于这两个度量，我们构建了一个MA位置优化问题，该问题在将LB限制在预定义阈值以下的同时最小化SVC。为了使由此产生的非凸问题可处理，我们利用连续SVC函数的空间对称性并对旁瓣区域进行离散化，从而将空间极小极大目标转化为一组有限约束。同时，LB约束被等价地重构为MA位置上的空间方差约束。基于这些变换，我们在交替优化（AO）框架内开发了一种连续凸近似（SCA）算法，以迭代优化水平和垂直MA坐标。
英文摘要
Movable antenna (MA) has emerged as a promising technology to unlock spatial degrees of freedom for wireless sensing systems. However, optimizing MA positions for multi-target sensing remains challenging due to the complicated coupling between the array geometry and unknown target directions, as well as severe sidelobe interference. In this paper, we propose an MA-enabled multi-target wireless sensing scheme to achieve both high-precision and robust target parameter estimation. To characterize the sensing performance while maintaining tractability, we introduce two complementary metrics, namely a lower bound (LB) of the Cramér-Rao bound (CRB) and the steering vector correlation (SVC). Specifically, by investigating the partial order relationship of the Fisher information matrix, we derive a closed-form LB of the multi-target CRB, which decouples the unknown target directions. Meanwhile, the SVC is employed to characterize the spatial correlation and sidelobe-induced interference among sensing signals over different directions. Based on these two metrics, we formulate an MA position optimization problem that minimizes the SVC while constraining the LB below a predefined threshold. To render the resulting non-convex problem tractable, we exploit the spatial symmetry of the continuous SVC function and discretize the sidelobe region, thereby transforming the spatial minimax objective into a finite set of constraints. Meanwhile, the LB constraint is equivalently reformulated as a spatial variance constraint on the MA positions. Building on these transformations, we develop a successive convex approximation (SCA) algorithm within an alternating optimization (AO) framework to iteratively optimize the horizontal and vertical MA coordinates.
