基于验证传播模型的RIS近场位置和速度估计
RIS Nearfield Position and Velocity Estimation Using a Validated Propagation Model
AI总结:
本文提出了一种改进的三步算法,用于基于验证传播模型的RIS近场位置和速度估计,实现了7毫米的位置误差和0.12米/秒的速度误差。
摘要:
我们研究了可重构智能表面(RISs)在非视距(NLOS)室内场景中用于位置和速度估计的任务,使用基于快照的多步估计算法。我们评估了一个由四个RIS模块组成的复合RIS结构原型,每个RIS单元格具有1位相位控制。考虑天线图案的数值模拟结果展示了3米x3米的感兴趣区域。我们证明了使用远场假设的初始网格搜索步骤对于RIS中心附近的小距离不够稳健,并提出了一种更稳健的算法。此外,我们展示了天线图案效应导致位置和速度误差增加。我们的改进三步算法在现实数值传播模型下,在距离RIS中心2米时实现了7毫米的位置误差和0.12米/秒的速度误差。
英文摘要:
We investigate reconfigurable intelligent surfaces (RISs) for the task of position and velocity estimation in non-LOS (NLOS) indoor scenarios, using a snapshot based multi-step estimation algorithm. We evaluate a compound RIS structure prototype composed of four RIS tiles with 1-bit phase control per RIS unit cell. Numerical simulation results taking the antenna patterns into account are presented for an 3 m x 3 m area of interest. We demonstrate that the initial grid search step using the far field assumption is not robust enough for small distances to the RIS center and propose a more robust algorithm. Furthermore, we show that the effect of the antenna pattern causes an increased position and velocity error. Our modified three-step algorithm achieves a position error of 7 mm and a velocity error of 0.12 m/s at a distance of 2 m to the RIS center under a realistic numerical propagation model.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。