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.

翻译：本文提出一种基于卡尔曼滤波的上行联合通信与感知方案，可显著降低基站与用户设备间时钟异步导致的距离与位置估计误差。时钟异步会引发时变时间偏移与载波频率偏移，给上行感知带来重大挑战。与现有技术不同，本方案无需预先获知用户设备位置，且能保持感知参数估计问题的线性特性。我们首先估计多径到达角并据此对信道状态信息进行空间滤波，随后提出基于卡尔曼滤波的信道状态信息增强器，利用含载波频率偏移的多普勒估计作为先验信息，有效抑制空间滤波后信道状态信息中的时变类噪声时间偏移项。基于卡尔曼滤波增强后的信道状态信息，可精确估计用户设备与散射体的距离，最终通过识别用户设备的到达角与距离估计值实现定位，并利用双基地系统定位无源散射体。仿真结果验证了所提方案的有效性，其定位均方根误差较基准方案降低约20分贝。