数据融合模型选取对工业测量整体平差结果的影响分析

Influence on Selection of Data Fusion Model to Overall Adjustment in Industrial Measurement

工业测量中,传统移站测量法的点位测量误差会随着移站次数的增加不断累积增加,导致最终的测量精度不可控。讨论了基于原始观测值和基于坐标观测值的两种整体平差数据融合方法,与传统移站测量法相比,这两种方法解算的点位精度较高且稳定。实验表明,即使融合的函数模型和随机模型等价,采用不同的融合方法,得到的融合结果并不完全等价。在实际应用中需根据具体情况选择不同的融合方法,从而得到解算精度可靠的待测点坐标。

In industrial measurement,the coordinate measurement error of the traditional moving station measurement will accumulate constantly with the increase of number of stations,which leads to the uncontrollability of the measurement accuracy.Based on the original observations and the coordinate observations respectively,this paper proposes two methods of data fusion of the overall adjustment in which the point precision is both higher and more stable than the traditional moving station measurement.Experiments show that the fusion results won’t be completely equivalent within different fusion methods even if the mathematical model and stochastic model are equivalent.In order to get more reliable accuracy of coordinates of unknown points,we should choose different fusion methods based on the specific situation in practical application.