摘要
研究从大量低精度的GPS轨迹数据融合生成高精度GPS数据的快速有效算法,并产生若干关键数据点以简化描述GPS轨迹,具有提高数据精度、降低测量成本和减少数据存储空间的重要意义。本文依据K段主曲线、二分法、优化等理论方法,提出3种多GPS轨迹数据融合算法:最大距离融合算法MDA、数据分块算法DPA、自适应半径数据融合算法ARA。算法的仿真实验结果表明,ARA算法在数据精度、曲线光滑程度等方面明显优于其他两种算法。将3种算法应用于青藏铁路实测多轨迹GPS数据的融合,验证结果表明,ARA算法性能最优,平均横向误差在0.2m左右,约简率为2.01%,有效节约了数据的存储空间,能够较好地完成高精度轨迹的生成。
Research on the fast and efficient algorithm that generates high precision GPS data from low precision GPS trajectory data fusion,where some key data points are generated to simplify the description of GPS trajectory,is significant in improving the data accuracy,reducing measurement costs,and reducing data storage space.Based on the theory methods of the K principal curves,dichotomy,and optimization,three GPS trajectory data fusion algorithms are proposed：Maximum Distance Algorithm,Data Partition Algorithm and Adaptive Radius Algorithm.Simulation experiment results show that Adaptive Radius Algorithm（ARA）is superior to the other two algorithms in data accuracy and rate of curve smoothness.After the three algorithms are applied to multiple GPS trajectory data fusion measured from the Qinghai-Tibet Railway（QTR）,the experimental results show that ARA achieved best performance with mean lateral error of about 0.2mand reduction rate of 2.01%,thereby effectively saving data storage space and accomplishing the generation of high precision trajectory.
出处
《铁道学报》
EI
CAS
CSCD
北大核心
2015年第2期46-51,共6页
Journal of the China Railway Society
基金
教育部基本科研业务费(2012JBM016)
北京市自然科学基金(4142044)
国家重点实验室自主课题(RCS2014ZZ02)
关键词
GPS
数据融合
二分法
优化
自适应半径
GPS
data fusion
dichotomy
optimization
adaptive radius method
