Predicting Satellite Clock Errors Using Grey Model Optimized by Adaptive TS-IPSO

This paper proposes a combination prediction model based on improved particle swarm optimization algorithm by two subgroups (TS-IPSO) and grey model. We check the smoothness of clock bias sequence, and log it if the smoothness is not satisfied. To avoid getting stuck at local optimization and turning premature convergence, we established a mechanism so that the main particle swarm and assistant swarm search synergistically, so the inertia weight decreases nonlinearly. We use TS-IPSO to optimize development obscure number and endogenous control obscure number, thus the improved grey model can adapt and gain higher precision. Satellite data from four different clocks are selected and calculated, the results show that the improved model is superior to the conventional model, in precision and stability, for 6-hour and 24-hour prediction. Especially, in the Cs clock, it achieves 6-hour prediction errors of less than 1.60 ns, and 24-hour prediction errors of less than 5.71 ns.