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 third-generation Beidou satellite navigation system (BDS-3). The proposed model takes advantage of group learning strategy to optimize the initialization parameters of the BP neural network and obtains globally optimized parameters. In order 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. The proposed BSO-BP model has the best accuracy and stability when predicting the 1 h SCB based on the same data. 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 PHM clock and Rb-II clock improved by more than 47%, as compared with that of the Rb clock. Therefore, the overall performance of the atomic clock based on BDS-3 is better than BDS-2. The positioning accuracy of the BSO-BP model can reach the centimeter level in east, north, and up directions.

中文翻译：

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基于 BSO 优化的 BP 神经网络增强北斗三号卫星时钟偏差的短期预测

卫星钟差（SCB）预测在高精度、实时导航定位中发挥着重要作用。在预测SCB时，由于初始参数不准确，BP神经网络的性能受到局部最优的影响。因此，我们提出了一种基于甲虫群优化（BSO-BP）算法的改进BP神经网络，以提高第三代北斗卫星导航系统（BDS-3）中SCB预测的性能。该模型利用组学习策略优化BP神经网络的初始化参数，获得全局优化参数。为了验证所提出的BSO-BP模型，对15颗BDS卫星的SCB预测精度和稳定性进行了分析。实验结果表明，在基于12小时SCB数据预测1小时SCB时，BSO-BP模型的预测精度最好，平均精度为0.064 ns。与 LP、QP 和 GM 模型相比，所提出的 BSO-BP 模型的平均预测精度分别提高了约 72.6%、43.4% 和 86%。随着预测时间的增加，初始参数不准确对SCB预测的影响逐渐减小，预测精度提高。所提出的 BSO-BP 模型在基于相同数据预测 1 h SCB 时具有最佳的准确性和稳定性。与 LP、QP 和 GM 模型相比，所提出的 BSO-BP 模型的预测稳定性提高了 36% 以上。此外，PHM时钟和Rb-II时钟的预测精度提高了47%以上，与 Rb 时钟相比。因此，基于北斗三号的原子钟整体性能优于北斗二号。BSO-BP模型的东、北、上定位精度可达厘米级。