Future railway systems will need the support of more precise and reliable information on train motion during operation, to fully exploit the potential of new technologies. Train motion information are here intended as time series of train state characteristics, that is, position, speed, and acceleration. This paper investigates the use of two different filtering formulations for better online estimations of train state along the track. Specifically, extended Kalman filters (EKFs) and particle filters (PFs) are used to fuse kinematic measurements collected by means of Global Navigation Satellite Systems (GNSSs) with information collected by the trains on the used tractive power. The EKFs need linearity assumptions and are based on statistical evaluations of the train state, the PFs are instead conceived for nonlinear models and they are based on probability functions related to train state measurements and estimates. A set of experiments with real data of trains operating on a Swiss line is presented to analyze the capabilities of these two filters when applied to rail operation in non‐urban environments. We discuss the extent by which the proposed filtering approaches match the latest technical requirements for implementation. We expect such an enhanced train motion estimation to enable reliable and continuous positioning, available at train level and at the traffic control center, while trackside equipment will be gradually reduced.

中文翻译：

---

带有车载功率测量的在线列车运动估计的滤波方法

未来的铁路系统将需要在运营过程中提供有关火车运动的更精确和可靠的信息，以充分利用新技术的潜力。列车运动信息在此旨在作为列车状态特性的时间序列，即位置，速度和加速度。本文研究了使用两种不同的过滤公式来更好地在线估算沿线火车状态。具体而言，扩展的卡尔曼滤波器（EKF）和粒子滤波器（PF）用于将通过全球导航卫星系统（GNSS）收集的运动学测量值与火车所收集的有关牵引功率的信息融合在一起。EKF需要线性假设，并基于对火车状态的统计评估，相反，PF是为非线性模型而构想的，它们基于与列车状态测量和估计有关的概率函数。提出了一组使用在瑞士线上运行的火车的真实数据的实验，以分析这两个过滤器在非城市环境中应用于铁路运营时的功能。我们讨论了所提出的过滤方法与实施的最新技术要求相匹配的程度。我们期望这种增强的列车运动估计能够实现可靠且连续的定位，可在列车级别和交通控制中心使用，而轨道旁的设备将逐渐减少。提出了一组使用在瑞士线上运行的火车的真实数据的实验，以分析这两个过滤器在非城市环境中应用于铁路运营时的功能。我们讨论所提出的过滤方法与实施的最新技术要求相匹配的程度。我们希望这种增强的火车运动估计能够实现可靠且连续的定位，可在火车级别和交通控制中心使用，而轨道旁的设备将逐渐减少。提出了一组使用在瑞士线上运行的火车的真实数据的实验，以分析这两个过滤器在非城市环境中应用于铁路运营时的功能。我们讨论所提出的过滤方法与实施的最新技术要求相匹配的程度。我们期望这种增强的列车运动估计能够实现可靠且连续的定位，可在列车级别和交通控制中心使用，而轨道旁的设备将逐渐减少。