北斗MEO卫星辐射剂量探测数据缺失值处理方法

针对北斗MEO卫星辐射剂量探测数据出现连续性缺失的问题，开展缺失值处理方法研究.提出一种叠加正弦波的线性样条回归方法，即引入样条函数，对各数据连续缺失的时间段进行分段处理，每段样条采用叠加正弦波的线性方程填充缺失值.结果表明:利用该方法处理缺失值，每段样条中填充曲线与探测曲线在增长趋势、周期性波动等方面具有较高的一致性；相比前向插值法和线性插值法，其填充值与真实值误差更小，关联性更高.该方法较好地解决了数据连续缺失的问题，形成了完整性好、准确性高的北斗MEO卫星辐射剂量数据集，为后续数据的发布、建模和可视化展示等奠定了基础. 

A Missing Values Imputation Method for Radiation Data from Beidou MEO Satellite Sensors

Radiation dose refers to the sum of ionization energy deposited by various incident high-energy particles in unit mass material. The radiation dose data detected by Beidou MEO satellites suffers from the problem of missing values that tend to occur continuously and there is little difference in the radiation dose detection values in the three directions in the satellite cabin. Firstly, the detector 3 data with strong regularity is selected as the research object. In order to fill the missing values, a sine-adjusted linear spline regression method is proposed, in which each continuous data-missing time period is individually processed in a spline and the missing values are filled with the sine-adjusted linear equation. The experiment shows that with our approach the filling curve is highly consistent with the true curve in terms of growth trend and periodic fluctuation. And our method performs significantly better with respect to errors and correlation coefficient between true and filled values, than alternative methods such as forward interpolation and linear interpolation. This paper selects the data of different time periods to analyze the filling effect, and draws the above conclusions. The proposed method properly solves the problem of continuous data-missing, leading to a data set with good completeness and high accuracy, which lays a foundation for the subsequent tasks like data release, modeling and visualization.