基于卡尔曼滤波的时钟异步通信系统中的感知
Kalman Filter-based Sensing in Communication Systems with Clock Asynchronism
AI总结：
针对时钟异步导致上行感知误差大的问题，提出基于卡尔曼滤波的JCAS方案，无需UE先验位置，通过空间滤波与KF增强抑制TO，显著降低定位误差，较基准低约20 dB。
摘要：
本文提出了一种新颖的基于卡尔曼滤波（KF）的上行链路（UL）联合通信与感知（JCAS）方案，该方案能够显著降低由基站（BS）与用户设备（UE）之间的时钟异步引起的距离和位置估计误差。时钟异步会导致时变的时间偏移（TO）和载波频率偏移（CFO），给上行感知带来重大挑战。与现有技术不同，我们的方案无需预先知道UE的位置，并保持了感知参数估计问题的线性性。我们首先估计多径的到达角（AoAs），并利用它们对CSI进行空间滤波。然后，我们提出了一种基于KF的CSI增强器，利用多普勒估计和CFO作为先验信息，显著抑制空间滤波后CSI中时变的类噪声TO项。随后，我们基于KF增强的CSI估计UE和散射体的精确距离。最后，我们识别UE的AoA和距离估计以定位UE，然后利用双基地系统定位哑散射体。仿真结果验证了所提方案的有效性。所提方法的定位均方根误差比基准方案低约20 dB。
英文摘要：
In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE's AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme.
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。
