自适应卡尔曼滤波的电离层TEC预测模型改进

摘要

Aiming at the problems that it is vulnerable to bias for the prediction model with the total number of electrons (TEC)directly obtained from IGS,and it is liable to data divergence for traditional Kalman filtering in the preprocess of large amounts of data,which lead to reduce the accuracy of predition model for ionospheric TEC,the paper proposed an improve-ment method using self-adaptive Kalman filtering:the self-adaptive Kalman filtering with variance compensation was used to preprocess the original data,and the wavelet neural network was used to accomplish the prediction,finally the prediction accu-racy of the model was given.Experimental result showed that the average prediciton accuracy of the method would be higher both than that of modeling directly using original data and that of traditional Kalman filtering.%针对直接使用IGS公布的电离层总电子数进行建模会导致预测模型建立存在偏差,以及使用传统卡尔曼滤波在对大量数据进行预处理时容易导致数据发散,进而降低电离层TEC模型预测精度的问题,提出一种利用自适应卡尔曼滤波的改进方法,使用方差补偿自适应卡尔曼滤波对原始数据进行预处理,再利用小波神经网络完成预测,最后分析模型预报的精准度.实验结果表明,此方法的预测平均精度相对直接使用原始数据建模和传统卡尔曼滤波都有不同程度的提高.