The XVIII International Conference on Mine Surveying

314 / 2023-10-13 15:22:15

Improving Clock Bias Prediction for BDS Satellites Based with BSO-BP model

satellite clock bias (SCB), Beidou satellite navigation system (BDS), beetle swarm optimization (BSO), BP neural network, precise point positioning (PPP)

The satellite clock bias (SCB) prediction plays an important role in high-accuracy and real-time navigation and positioning. When predicting the SCB, the performance of the BP neural network is affected by the local optimum due to inaccurate initial parameters. Therefore, we propose an improved BP neural network based on the beetle swarm optimization (BSO-BP) algorithm to improve the performance of SCB prediction in Beidou satellite navigation system (BDS). Due to the impact of SCB data quality on the accuracy of SCB prediction, this paper first analyzes the SCB data of BDS for one year, then uses the BSO-BP model for SCB prediction research, and finally applies the predicted SCB to precise point positioning (PPP) to analyze the positioning accuracy. The experimental results indicated: (1) BDS-3 exhibits superior atomic clock performance compared to BDS-2, and The stability and frequency drift of an Inclined Geosynchronous Orbit (IGSO) satellite can show observable periodic fluctuations. In addition, the SCB of the BDS atomic clock has two main period terms, which are closely related to the orbital period of the satellite. (2) To verify the proposed BSO-BP model, 15 BDS satellites are analyzed in terms of prediction accuracy and stability of SCB. The experimental results show that when predicting 1 hour SCB based on a 12 hours SCB data, the prediction accuracy of the BSO-BP model is the best, with an average accuracy of 0.064 ns. As compared with the LP, QP, and GM models, the average prediction accuracy of the proposed BSO-BP model increases by about 72.6%, 43.4%, and 86%, respectively. As the prediction time increases, the influence of the inaccurate initial parameters on SCB prediction gradually decreases, and the prediction accuracy improves. Based on the same data, the proposed BSO-BP model has the best accuracy and stability when predicting the 1 h SCB. The prediction stability of the proposed BSO-BP model improves by more than 36% as compared with LP, QP, and GM models. In addition, the prediction accuracies of the hydrogen maser (PHM) clock and new rubidium (Rb-II) clock improved by more than 47%, as compared with that of the rubidium (Rb) clock. (3) The convergence rate of the BSO model is the highest among the four models. In addition, compared with LP, QP and GM models, the positioning accuracy of BSO-BP model in the East (E) direction is improved by 43.2%, 44.2% and 28.1%, respectively, and in North (N) direction is improved by 28.4%, 50.2% and 45%, respectively, and in Up (U) direction is improved by 14.1%, 0.8% and 36.2%, respectively.