基于MEA-BP神经网络的卫星钟差预报

卫星钟差是影响导航定位精度的重要因素之一,建立高精度的钟差预报模型对高精度定位有重要意义。针对常用模型卫星钟差在短期预报中随时间增加误差积累,以及传统BP神经网络不稳定,容易出现过拟合等问题,本文提出一种基于思维进化算法(MEA)优化的BP神经网络钟差预报模型和算法。首先对原始钟差数据进行一次差处理;然后利用思维进化算法对BP神经网络的初始权值和阈值进行优化,给出该模型进行钟差预报的具体步骤;选用IGS站提供的多天GPS精密钟差产品数据进行试验分析,使用GPS一天中前12 h数据建模,进行2、3、6和12 h的钟差预报。结果表明:利用MEA-BP模型得到的上述4种时段的预报精度分别优于0.36、0.38、0.62和1.56 ns,预报误差曲线变化起伏较小,说明新模型的预报性能优于3种传统模型,新模型在钟差预报短期预报中的实用性及稳定性是较佳的。

Prediction of the satellite clock bias based on MEA-BP neural network

The satellite clock bias is one of the important factors that affect the accuracy of navigation and positioning, so establishing a high-precision clock bias prediction model is of great significance to high-precision positioning. Aiming at the problem that satellite clock bias error accumulates by common models over time in short-term prediction, and the easy overfitting and instability of the traditional BP neural network, this paper proposed a model and algorithm of clock bias prediction based on BP neural network optimized by the mind evolutionary algorithm(MEA). First, original clock bias data made once difference to obtain the corresponding once difference sequences. Then, the initial weights and thresholds of the BP neural network were optimized by the mind evolutionary algorithm, the specific steps of using this model for the clock bias prediction were given. The multi-day GPS precision clock bias product data provided by the IGS station is used for experimental analysis. The article used the GPS data for the first 12 h of the day for modeling were listed, and made short-term clock bias prediction within 2, 3, 6 and 12 h. The results showed that the above four periods of prediction precision obtained by using the MEA-BP model were better than 0.36, 0.38, 0.62 and 1.56 ns, respectively. The fluctuation of the prediction error curve was small, and the prediction performance of the new model was better than the three traditional models, which showed the new model is better in practicability and stability in the short-term prediction of clock bias.