To generate the GPS (Global Positioning System) electronic map for train control systems, GPS data with high accuracy are required. However, GPS receivers with high-accuracy cost too much. Therefore, learning high-accuracy GPS data from multiple low-accuracy GPS data measured by the low-cost GPS receiver through the information fusion algorithm is of great use. The existing information fusion algorithm can reach the target, but it is only applicable to simple tracks and the optimization time is too long. Based on the theory of principal curves and the special feature of the fixed high-accuracy points of interest in railways, we proposed the improved algorithm by adding a point in the area whose error is maximal and using local optimization in this paper. The results of the experiments show that the new algorithm can learn high-accuracy GPS data from multiple low-accuracy GPS data and has great advantages in the time of computation and the scope of application.%用于列车控制的GPS(Global Positioning System)电子地图需要高精度的轨道数据.由于采集高精度轨道数据所需要的GPS接收机成本过高,研究融合多个低精度的GPS轨迹自动生成高精度轨迹具有重要的应用价值.已有的多轨迹融合算法存在计算过于复杂、计算时间过长和适用范围窄等问题.在主曲线理论的基础上并具有两个高精度固定端点的特征,本文对多GPS铁道轨迹信息融合算法做了进一步研究,采用在最大误差区域增加顶点和局部优化顶点位置的方法,提出一种改进的融合算法.通过实测铁路轨道GPS数据和复杂形状的模拟数据验证,结果表明,改进的融合算法可以有效地融合多个低精度的GPS轨迹自动生成高精度轨迹,并加快计算速度,减少存储空间,增加了适用范围.
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