A high-precision short-term prediction method with stable performance for satellite clock bias

Abstract

Real-time users cannot carry out real-time precise point positioning (RT-PPP) because they cannot receive real-time service (RTS) products from the international GPS service (IGS) in the case of interrupted communication. We address this issue by introducing a stable particle swarm optimized wavelet neural network (PSOWNN) to predict the short-term satellite clock bias in real time accurately. The predicted sequences of the new model are compared with those of the conventional linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Kalman filter models. The results show that the accuracy of the proposed model is better than that of these four models. The average prediction accuracy of the 30-min and 60-min forecasting has improved by approximately (79.3, 82.4, 79.1, 97.4) % and (97.4, 82.9, 87.7, 98.9) % and is better than 0.3 ns during 30-min and 1-h forecasting. The RTS products can thus be replaced with the short-term clock products predicted by the PSOWNN model to meet the precision requirements of RT-PPP.