基于扩展卡尔曼滤波器的V2I通信中车辆跟踪与RSU选择联合算法
Joint Vehicle Tracking and RSU Selection for V2I Communications with Extended Kalman Filter
摘要：
我们开发联合车辆跟踪和路边单元（RSU）选择算法，适用于车辆到基础设施（V2I）通信。我们首先设计了一个分析框架，用于评估车辆跟踪系统的基础上扩展的卡尔曼滤波器。一个简单的，但有效的，量化的车辆跟踪性能的度量导出的主导空间频率的角度导数。其次，提出了一种RSU选择算法来选择合适的RSU，以提高车辆跟踪性能。一个联合车辆跟踪算法也被开发，以最大限度地提高跟踪性能，考虑在多个RSU探测样本，同时最大限度地减少样本交换量。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。
英文摘要：
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.
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