极限学习机方法在经纬仪空间配准中的应用

Application of extreme learning machine method in space registration to theodolite

针对光电经纬仪测量中多传感器的空间配准问题,提出了一种基于极限学习机(ELM)的空间配准建模方法。首先介绍了ELM算法和ELM空间配准模型的建立步骤,然后使用星体测量数据建立ELM空间配准模型,最后将该模型与单项差修正模型、球谐函数修正模型进行了对比验证。实验结果表明:ELM空间配准模型可以使光电经纬仪的测量精度从17″左右提高到1″以内,与单项差修正模型、球谐函数修正模型相比精度提高35%以上。由此可见,与单项差修正模型和球谐函数修正模型相比,采用ELM算法所建立的光电经纬仪空间配准模型具有更高的精度和更强的泛化能力。

In order to solve the space registration problems of multi-sensor in the photoelectric theodolite measurement, a space registration model based on the extreme learning machine （ELM） algorithm was proposed in this paper. Firstly, the ELM theory and the modeling steps of ELM space registration model were introduced. Then, the star measurement data was used to build ELM space registration model. Finally, the ELM space registration model was compared with single error correction model and spherical harmonics correction model. Experimental results indicate that ELM space registration method can improve the measuring precision of photoelectrical theodolite from about 17″ to less than 1″; the accuracy of the ELM space registration model is improved by more than 35% than single error correction model and spherical harmonics correction model. The results indicate that compare with the single error correction model and spherical harmonics correction model, space registration model based on ELM algorithm has higher prediction accuracy and stronger generalization capability.