论文标题
通过扩展的Kalman滤波器进行V2I通信的联合车辆跟踪和RSU选择
Joint Vehicle Tracking and RSU Selection for V2I Communications with Extended Kalman Filter
论文作者
论文摘要
我们开发了适用于车辆到基础设施（V2I）通信的联合车辆跟踪和道路侧单元（RSU）选择算法。我们首先设计了一个分析框架，用于根据扩展的卡尔曼过滤器评估车辆跟踪系统。量化车辆跟踪性能的简单但有效的度量是根据主要空间频率的角度导数得出的。其次，提出了RSU选择算法来选择可提高车辆跟踪性能的合适RSU。还开发了一种联合车辆跟踪算法，以通过考虑多个RSU的样品，同时最大程度地减少样品交换量，从而最大程度地提高跟踪性能。数值结果验证了所提出的车辆跟踪算法比传统的基于信噪比的跟踪系统具有更好的性能。
We develop joint vehicle tracking and road side unit (RSU) selection algorithms suitable for vehicle-to-infrastructure (V2I) communications. We first design an analytical framework for evaluating vehicle tracking systems based on the extended Kalman filter. A simple, yet effective, metric that quantifies the vehicle tracking performance is derived in terms of the angular derivative of a dominant spatial frequency. Second, an RSU selection algorithm is proposed to select a proper RSU that enhances the vehicle tracking performance. A joint vehicle tracking algorithm is also developed to maximize the tracking performance by considering sounding samples at multiple RSUs while minimizing the amount of sample exchange. The numerical results verify that the proposed vehicle tracking algorithms give better performance than conventional signal-to-noise ratio-based tracking systems.
