基于导向矢量相关性与CRB优化的可移动天线增强无线感知
Movable Antenna Enhanced Wireless Sensing via Steering Vector Correlation and CRB Optimization
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中文总结 AI 辅助
针对可移动天线无线感知的到达角估计问题，本文融合导向矢量相关性旁瓣与克拉美罗下界构建联合优化，提出逐次凸近似的位置优化算法，仿真验证其到达角估计性能优异。
中文摘要 AI 辅助
本文研究配备可移动天线（MA）的无线感知系统的到达角（AoA）估计问题。为实现高估计性能与精度，我们构建了融合导向矢量相关性（SVC）旁瓣与克拉美罗下界（CRB）的联合优化问题。首先，将SVC与CRB数学转化为可处理的目标函数：引入代理变量并采用离散网格搜索策略，解决目标角度未知时SVC优化的难处理问题；同时推导CRB的广义下界，得到MA位置的标量函数。基于转化后的目标，提出基于逐次凸近似的位置优化算法，该算法通过在定义的信赖域内使用一阶泰勒展开处理非凸项，使MA位置在每次迭代中逐步更新。仿真结果表明，所提算法可实现优异的AoA估计性能。
英文摘要
In this paper, we investigate the angle-of-arrival (AoA) estimation problem for wireless sensing systems equipped with movable antennas (MA). To achieve high estimation performance and accuracy, we formulate a joint optimization problem integrating the sidelobes of steering vector correlation (SVC) and the Cramér-Rao bound (CRB). We first mathematically transform the SVC and the CRB into tractable objective functions. Specifically, we introduce a proxy variable and apply a discrete grid search strategy to overcome the intractability of optimizing the SVC with unknown target angles. Concurrently, we derive a generalized lower bound for the CRB, which yields a scalar function of the MA positions. Guided by the transformed objective, we propose a successive convex approximation-based position optimization algorithm. The proposed algorithm handles the non-convex terms by employing first order Taylor expansions within a defined trust region, which allows the MA positions to be updated incrementally in each iteration. Simulation results demonstrate that the proposed algorithm achieves superior AoA estimation performance.
