Abstract:

Aiming at the poor performance of short term prediction of navigation satellite clock error, a method based on the least square support vector machine (LS-SVM) and improved particle swarm optimization (PSO) is proposed for prediction of satellite clock error. Adaptive inertia weigh and learning factor are introduced to improve the ability of PSO to find the best swarm. Then it is used to choose the parameters of LS-SVM, for avoiding the man made blindness and enhancing the efficiency of online forecasting. The four typical GPS satellites clock data of IGS are chosen and respectively used in three models to predict short term clock error. The results show that the accuracy of LS-SVM model is superior to the other models, and the work provides a new way for short term prediction of navigation satellite clock error.

Key words: PSO, Inertia weigh, Learning factor, LS-SVM, Satellite clock error