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首页> 外文期刊>Radar, Sonar & Navigation, IET >Adaptive multisensor data fusion technique for train localisation and detection of accidental train parting
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Adaptive multisensor data fusion technique for train localisation and detection of accidental train parting

机译：自适应多传感器数据融合技术在列车定位和列车意外分离中的检测

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摘要

This study proposes a bilinear recursive least square based adaptive multisensor data fusion technique for the precise localisation of railway vehicles and detection of an accidental train parting to be used with the train collision avoidance system (TCAS) in Indian railways. The accurate localisation of railway vehicles during the absence of global positioning system (GPS) is a challenging task for the TCAS. One of the reliable solutions for this task may be the augmentation of GPS with the onboard multisensor system. A bilinear recursive least square adaptive filter is used here to estimate and compensate the position error of the onboard multisensor system. The impact of slack in coupling is considered for the analysis of parting detection. The performance of the proposed technique is compared with the observation error based approach, bounded offset based approach and pseudo-measurement state constraining technique. The simulation results indicate that the proposed technique is superior in terms of positional accuracy and for the detection of an accidental train parting with a minimum parting distance.
机译：这项研究提出了一种基于双线性递归最小二乘的自适应多传感器数据融合技术，用于铁路车辆的精确定位和检测与火车防撞系统（TCAS）一起使用的印度铁路意外火车分离。在没有全球定位系统（GPS）的情况下，铁路车辆的准确定位对于TCAS而言是一项艰巨的任务。用于此任务的可靠解决方案之一可能是使用车载多传感器系统增强GPS。这里使用双线性递归最小二乘自适应滤波器来估计和补偿机载多传感器系统的位置误差。在分离检测的分析中考虑了联轴器松弛的影响。将该技术的性能与基于观测误差的方法，基于有界偏移的方法和伪测量状态约束技术进行了比较。仿真结果表明，所提出的技术在定位精度上和用于以最小的分离距离检测火车意外分离方面具有优越性。

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