论文标题
智能反射表面辅助操纵目标传感：真实速度估计
Intelligent Reflecting Surface-Aided Maneuvering Target Sensing: True Velocity Estimation
论文作者
论文摘要
操纵目标感应将是未来车辆网络的重要服务，在这种网络中，精确的速度估计是核心任务之一。为此，最近提出的集成传感和通信（ISAC）为实现准确的速度估计提供了一个有希望的平台。但是，使用一个单静态ISAC基站（BS），只能估计真实速度的径向投影，这会导致严重的估计误差。在本文中，我们研究了在智能反射表面（IRS）的协助下，调查了机动目标的真实速度的估计。我们通过利用从BS和IRS到目标的两个观点来提出有效的速度估计算法。我们提出了一个两阶段的方案，其中可以根据BS-Tagget链接和BS-IRS-TARGET链接的多普勒频率回收真正的速度。实验结果验证了可以精确恢复真正的速度并证明添加IRS的优势。
Maneuvering target sensing will be an important service of future vehicular networks, where precise velocity estimation is one of the core tasks. To this end, the recently proposed integrated sensing and communications (ISAC) provides a promising platform for achieving accurate velocity estimation. However, with one mono-static ISAC base station (BS), only the radial projection of the true velocity can be estimated, which causes serious estimation error. In this paper, we investigate the estimation of the true velocity of a maneuvering target with the assistance of an intelligent reflecting surface (IRS). We propose an efficient velocity estimation algorithm by exploiting the two perspectives from the BS and IRS to the target. We propose a two-stage scheme where the true velocity can be recovered based on the Doppler frequency of the BS-target link and BS-IRS-target link. Experimental results validate that the true velocity can be precisely recovered and demonstrate the advantage of adding the IRS.
