基于线性样条和CNN-LSTM的北斗卫星缺失数据处理方法

针对北斗某星辐射剂量探测数据缺失问题,提出了一种基于线性样条和CNN-LSTM神经网络模型的处理方法.在对数据特性分析的基础上,将原始数据分解为线性趋势项和季节波动项.对于线性趋势项,采用基于线性样条的缺失值处理方法;对于季节波动项,根据其时空变化特性,设计CNN和LSTM组合神经网络结构,完成季节波动项的缺失值处理....

Linear Spline and CNN-LSTM for Missing Values Imputation of Beidou Satellite Radiation Dose Data

Aiming at the problem of missing radiation dose detection data of a certain Beidou satellites, a processing method based on linear spline and CNN-LSTM is proposed. First, analyze the characteristics of the data and decompose it into long-term trend items, a linear spline method is used to complete the missing value processing. For the seasonal fluctuation items, the temporal and spatial change rules are analyzed, and on this basis, the CNN and LSTM fusion neural network structure is designed to complete the processing of the seasonal fluctuation items. Experiments show that, comapred with the linear interplation method and the Fourier transform method, our approach performs better in the prediction with an average relative error of 0.008 and a correlation coefficient of 0.855. At the same time, the prediction accuracies of our approach and the single LSTM and single CNN network models are compared, similarly the predictions of our approach is more consistent with the observed data, and has a smaller deviation. The results show that the approach in this paper can better solve the problem of continuous missing values of Beidou satellites observations, which lays a foundation for the further scientific research based on the dataset.