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Joint Delay and Doppler Frequency Estimation for Scatterer Localization in Railway Environments

机译:铁路环境中散射体定位的联合时延和多普勒频率估计

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Autonomous driving vehicles shall increase the efficiency of passenger and goods transportation. Connecting these vehicles and ensuring the reliable exchange of safety critical data is one of the biggest challenges nowadays. The basis of reliable communication between vehicles is a fundamental understanding of the propagation mechanism and the resulting channel models. For the communication between moving vehicles geometry-based stochastic channel models (GSCMs) are widely used to model the non-stationary channel processes. To understand the underlying geometry between transmitter, receiver and scatterers, we propose a joint delay and Doppler frequency estimation to localize scatterer. We use train-to-train (T2T) measurement data and estimate the delay and the Doppler frequency for each measurement of each received signal. The probability density function (PDF) of the joint delay and Doppler frequency estimation is transformed to the Cartesian domain and plotted on a geo-referenced satellite image. In this way, the estimated scatterer position and the related propagation characteristics can be assigned to real objects.
机译:自动驾驶车辆应提高旅客和货物运输的效率。连接这些车辆并确保安全关键数据的可靠交换是当今最大的挑战之一。车辆之间可靠通信的基础是对传播机制和所得通道模型的基本理解。对于移动车辆之间的通信,基于几何的随机通道模型(GSCM)被广泛用于对非平稳通道过程进行建模。为了了解发射器,接收器和散射体之间的基本几何形状,我们提出了联合延迟和多普勒频率估计来定位散射体。我们使用火车到火车(T2T)的测量数据,并为每个接收信号的每次测量估计延迟和多普勒频率。将联合延迟和多普勒频率估计的概率密度函数(PDF)转换为笛卡尔域,并绘制在地理参考卫星图像上。这样,可以将估计的散射体位置和相关的传播特性分配给实际对象。

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