RIS赋能ISAC系统中的协作目标定位
Cooperative Target Localization in RIS-Enabled ISAC Systems
- Sharif University of Technology（谢里夫理工大学）
- Sapienza University of Rome（罗马第一大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出RIS赋能ISAC系统协作定位框架，推导FIM并分析相位对齐增益，通过SCA优化波束成形最小化PEB，融合ToA/AoA测量实现接近CRB的定位性能。
AI中文摘要：
本文开发了一种协作式集成感知与通信（ISAC）框架，其中多天线基站（BS）同时定位目标并为多个通信用户提供服务，其中一部分用户配备了可重构智能表面（RIS）。推导了用于目标定位的Fisher信息矩阵（FIM），明确刻画了其与BS波束成形系数、RIS相位配置以及双基地感知几何的依赖关系。在所陈述的缩放假设下，相干RIS相位对齐产生的Fisher信息增益随RIS单元数量的平方增长，而独立随机相位在期望上提供线性增益。我们提出了一个以感知为中心的联合主动与被动波束成形问题，在满足每用户信干噪比（SINR）和发射功率约束的条件下最小化位置误差界（PEB）。所得到的非凸问题通过迭代连续凸近似（SCA）过程解决，该过程求解一系列凸子问题。我们进一步开发了一种目标定位估计器，融合了一个直接到达时间（ToA）测量、一个到达角（AoA）测量以及多个RIS辅助的间接ToA测量。在小测量误差和渐近高效的第一阶段ToA/AoA估计下，估计器协方差一阶逼近Cramér--Rao界（CRB）。数值结果验证了分析缩放定律，并展示了协作式配备RIS用户所带来的定位增益。
英文摘要：
This paper develops a cooperative integrated sensing and communication (ISAC) framework in which a multi-antenna base station (BS) simultaneously localizes a target and serves multiple communication users, a subset of which is equipped with reconfigurable intelligent surfaces (RISs). The Fisher information matrix (FIM) for target positioning is derived, explicitly characterizing its dependence on the BS beamforming coefficients, RIS phase profiles, and bistatic sensing geometry. Under the stated scaling assumptions, coherent RIS phase alignment yields a Fisher-information gain that scales quadratically with the number of RIS elements, whereas independent random phases provide a linear gain in expectation. We formulate a sensing-centric joint active and passive beamforming problem that minimizes the position error bound (PEB) subject to per-user signal-to-interference-plus-noise ratio (SINR) and transmit-power constraints. The resulting non-convex problem is addressed through an iterative successive convex approximation (SCA) procedure that solves a sequence of convex subproblems. We further develop a target localization estimator that fuses one direct time-of-arrival (ToA) measurement, one angle-of-arrival (AoA) measurement, and multiple RIS-assisted indirect ToA measurements. Under small measurement errors and asymptotically efficient first-stage ToA/AoA estimation, the estimator covariance approaches the Cramér--Rao bound (CRB) to first order. Numerical results validate the analytical scaling laws and demonstrate the localization gains enabled by cooperative RIS-equipped users.
