Clustering Optimization for Triple-Frequency Combined Observations of BDS-3 Based on Improved PSO-FCM Algorithm

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve performance navigation services. Due large number performances, combinatorial clustering optimization very important, efficiency manual screening low. Firstly, based on basic model, objective equations are derived. Secondly, fuzzy c-means (FCM) algorithm, three improved PSO-FCM algorithms proposed, namely S-PSO-FCM L-PSO-FCM LOG-PSO-FCM algorithm. Thirdly, according characteristics, two datasets whose combined coefficients sum 0 1 emphatically discussed. Finally, effectiveness studied public dataset measured BeiDou-3 satellite system (BDS-3) data short baseline, long ultra-long baseline. results show that proposed algorithm better than FCM especially in baseline cases.