基于模糊聚类分析法的BDS长基线模糊度解算

为探求BDS长基线模糊度解算的最优组合观测量，基于模糊聚类分析法分别选取2个BDS最优超宽巷组合和1个最优窄巷组合共同构建TCAR模型，分别采用GF_TCAR、GB_TCAR和GIF_TCAR模型对实测BDS长基线进行模糊度解算。实验表明，3种模型解算超宽巷组合模糊度的结果基本一致； GF_TCAR模型解算模糊聚类分析法所得最优窄巷组合(3,0,-2)的相邻历元组合模糊度差值优于常用的(4,2,-5)组合；GB_TCAR模型解算IGSO卫星时，窄巷组合(3,0,-2)的结果优于(4,2,-5)组合，而解算GEO、MEO卫星时2种窄巷组合结果一致；GIF_TCAR模型解算窄巷组合(3,0,-2)的结果略逊于(4,2,-5)组合。

Ambiguity Resolution of BDS Long Baseline Based on Fuzzy Clustering Analysis Method

In order to explore the optimal combinations for BDS long-baseline ambiguity resolution, we select two optimal combinations of extra-wide lanes and an optimal combination of narrow lanes of BDS based on fuzzy clustering analysis to jointly construct the TCAR model, and use three models of GF_TCAR, GB_TCAR and GIF_TCAR to resolve the ambiguity of the measured long baseline for BDS. The experimental results show that all three models equally solve the combination ambiguity results of extra-wide lanes. Using GF_TCAR to calculate the adjacent epoch ambiguity difference of the optimal narrow-lane combination (3,0,2) obtained by fuzzy clustering analysis method is better than that of the commonly used combination (4,2,5). When using GB_TCAR to solve IGSO satellites, the narrow-lane combination (3,0,2) is superior to the combination (4,2,5), but the results of the two narrow lanes are consistent when solving GEO and MEO satellites. The narrow-lane combination (3,0,2) calculated by GIF_TCAR is slightly inferior to the combination (4,2,5).