Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.

中文翻译：

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异步感知车载网络中的杂波抑制、时频同步和传感参数关联


在依赖新兴集成传感和通信（ISAC）技术的实用感知车辆网络（PVN）中实现精确定位和速度估计仍然存在重大挑战。首先，复杂的无线传播环境会产生不需要的杂波，从而降低车辆传感性能并增加计算复杂度。其次，在实际PVN中，单独估计的多种类型的参数与特定车辆没有很好的关联，这可能会导致多车辆定位中的误差传播。第三，PVN 中的无线电收发器本质上是异步的，这导致车辆传感中的范围和速度存在很大的模糊性。为了克服这些挑战，在本文中1）我们介绍了一种基于运动目标指示（MTI）的联合杂波抑制和感知算法，并分析了其杂波抑制性能和成对的距离-速度的Cramér-Rao下界（CRLB）使用所提出的杂波抑制算法进行估计； 2）我们设计了一种算法（及其低复杂度版本），用于将个体到达方向（DOA）估计与基于“域变换”的成对距离-速度估计相关联； 3）我们提出了第一个可行的载波频率偏移（CFO）和时间偏移（TO）估计算法，支持非视距（NLOS）环境中的被动车辆传感。该算法将静态物体反射的信号的延迟多普勒频谱视为特定于环境的“指纹频谱”，该频谱在改变 CFO 和/或 TO 时表现出循环移位特性。 然后，通过获取循环移位数来有效估计 CFO 和 TO，并且我们还分析了所提出的时频同步算法的均方误差（MSE）性能。最后，仿真结果证明了我们的算法在不同配置下的性能优势，同时证实了理论分析。