卡尔曼滤波器
跟踪(教育)
计算机科学
车辆跟踪系统
选择(遗传算法)
跟踪系统
公制(单位)
实时计算
算法
控制理论(社会学)
人工智能
工程类
心理学
教育学
运营管理
控制(管理)
作者
Jung-Hwan Song,Seong-Hwan Hyun,Jong-Ho Lee,Jeongsik Choi,Seong-Cheol Kim
出处
期刊:Cornell University - arXiv
日期:2022-01-01
标识
DOI:10.48550/arxiv.2201.00138
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI