论文标题
双静态传感近场RIS定位
Bi-Static Sensing for Near-Field RIS Localization
论文作者
论文摘要
我们使用固定发射器和固定接收器之间的双静态传感来解决单输输出多载波系统的可重构智能表面（RIS）的定位。由于RIS的部署具有较大的维度，因此很可能会发生近场（NF）方案，尤其是对于室内应用程序，并且是这项工作的重点。我们首先在RIS位置和方向的估计误差以及对路径变送器 - RIS接收器的到达时间（TOA）的估计误差上得出CRAMER-RAO边界（CRB）。我们为RIS定位目的提出了一个多阶段的低复杂度估计器。在此提议的估计器中，我们首先执行线路搜索以估算TOA。然后，我们使用NF信号模型的远场近似来隐式估计RIS中心的到达角度和出发角度。最后，RIS位置和取向估计值是通过准Newton方法来完善的。仿真结果表明，所提出的估计器可以达到CRB。我们还研究了几个影响因素对提出的估计器准确性的影响，例如RIS大小，传输功率，系统带宽以及RIS位置和方向。
We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multi-carrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramer-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.
