基于EM算法优化相关向量机的BDS-3超快速钟差预报

提出一种基于EM算法优化相关向量机(RVM)的BDS-3超快速钟差预报算法。首先，利用组合MAD法预处理钟差数据，并进行一次差分计算；然后，利用钟差一次差分数据对RVM模型进行训练，通过EM算法迭代求取模型的超参数；最后，利用优化后的RVM模型进行数据预测，将钟差一次差分预测值还原，得到钟差预报值。采用iGMAS中心提供的实测BDS-3超快速钟差数据进行预报实验，并将本文模型与QP模型、SA模型及iGMAS超快速钟差预报产品(ISU-P)结果进行对比分析。结果表明，对于6 h、12 h和24 h预报，本文模型预报BDS-3卫星钟差数据的平均精度均优于0.61 ns；与ISU-P、QP模型和SA模型相比，本文模型预报24 h时精度分别提升64.1%、50.0%和49.2%。

BDS-3 Ultra-Rapid Clock Offset Prediction Based on EM Algorithm Optimized Relevance Vector Machine

We propose a BDS-3 ultra-rapid clock offset prediction algorithm based on EM algorithm optimized relevance vector machine. First, we use the combined MAD method to preprocess the clock data and perform one time difference. Then, we use the one time difference data to train the RVM model, we use the EM algorithm to iteratively obtain the hyperparameters of the model, and finally we use the optimized RVM model to predict. We then restore the one-time difference prediction value of the clock offset to obtain the prediction value of the clock offset. The prediction test is carried out with the measured BDS-3 ultra-rapid clock offset data provided by iGMAS, and the prediction results of this method are compared with the QP model, the SA model and the ultra-rapid clock offset prediction product (ISU-P) of iGMAS. The results show that the mean accuracy of the BDS-3 satellite clock offset data prediction by the RVM model is better than 0.61 ns regardless of whether the forecast is 6 h, 12 h or 24 h. Compared with ISU-P, QP model, SA model, the prediction accuracy of the 24 h BDS-3 satellite clock offset has been improved by 64.1%, 50.0%, 49.2%,respectively.