联合半参数与优化BiLSTM的BDS-3钟差超短期预报

Ultra-short-term prediction method for BDS-3 clock offset by combined semi-parametric and improved BiLSTM models

针对半参数钟差预报模型受核函数和窗宽参数选择影响较大以及双向长短期记忆神经网络(BiLSTM)易发生收敛速度慢、预报结果不稳定的问题,提出了一种联合半参数与优化BiLSTM的北斗3号(BDS-3)钟差超短期预报算法。引入莱维飞行算法对BiLSTM超参数进行优化,将半参数模型和优化后的BiLSTM模型组合,构建组合模型(Semi-LFA-BiLSTM)并应用到BDS-3钟差超短期预报。实验结果表明:在12 h预报中,所提组合模型对氢原子钟的预报精度达到0.2 ns左右,铷原子钟的预报结果均能保持在亚纳秒级,其平均精度相较于二次多项式模型、谱分析模型和BiLSTM模型分别提高81.00%、65.29%和44.74%,在钟差超短期预报中表现出较高性能。

In addressing the challenges posed by the significant influence of kernel function and window width parameter selection on semi-parametric clock difference prediction models,as well as the issues of slow convergence and unstable forecasting results in bidirectional long short-term memory neural network(BiLSTM),an ultra-short-term prediction method for Beidou navigation satellite system 3(BDS-3)clock offset by combined semi-parametric and improved BiLSTM models is proposed.The Levy flight algorithm is introduced for optimizing the hyperparameters of BiLSTM.By combining the semi-parametric model with the optimized BiLSTM model,the enhanced composite model(Semi-LFA-BiLSTM)is applied to the short-term clock offset prediction of BDS-3.Experimental results demonstrate that the prediction accuracy of the proposed combined model reaches about 0.2 ns for all orbital hydrogen atomic clocks in the 12-hour prediction,and the prediction results for rubidium atomic clocks can be maintained at the sub-nanosecond level,with the average accuracy improved by 81.00%,65.29%and 44.74%,respectively,compared with the quadratic polynomial model,the spectral analysis model and the BiLSTM model.It shows high performance in clock offset ultra-short-term forecast.