轨道几何
磁道(磁盘驱动器)
计算机科学
扩展卡尔曼滤波器
滤波器(信号处理)
卡尔曼滤波器
钥匙(锁)
过程(计算)
弹道
推论
贝叶斯概率
算法
人工智能
计算机视觉
计算机安全
天文
操作系统
物理
作者
Hanno Winter,Volker Willert,Jürgen Adamy
标识
DOI:10.1109/itsc.2018.8569456
摘要
Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a track-selective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter.
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