利用二维核回归估计的大气密度模式修正

传统经验大气密度模式预测大气密度存在的较大误差会引起低轨卫星轨道预报误差,对卫星的再入轨、控制计划、碰撞规避及精密定轨造成不利影响.利用天宫一号卫星探测数据,针对大气NRLMSISE-00模式计算的误差特点,在地磁相对平静(Ap ≤ 30)的时间段内,对相近地方时和纬度的模式误差分布进行分析发现,相近地方时和纬度的模式误差分布基本相同.利用二维核回归估计方法,对与预测点相近地方时和纬度的样本误差进行加权,估计预测点处的模式误差,进而按距离预测日期天数的长短,采用加权修正法对模式预测结果进行修正,修正后大气模式误差的均方差(RMS)由14.09%降至4.05%.研究结果表明,该修正方法可以显著提高大气密度预报精度.

Atmospheric Density Model Calibration Using 2-dimension Kernel Regression Method

The errors of traditional empirical thermospheric density models often translate into orbit errors of the Low Earth Orbit (LEO) satellites,adversely affect applications such as re-entry operations,manoeuver planning,collision avoidance and precise orbit determination for geodetic missions.By using the data detected by Tiangong-1,the features of NRLMSISE-00 model's density errors is analyzed,and it is found that the errors of model at the similar local time and latitude can be considered approximately identical during the quiet geomagnetic field (Ap ≤ 30).In this paper, 2-dimension kernel regression method is used to estimate model's error based on the sample error data at the similar station.Finally,weighted calibration method is developed for the calibration of model's density according to the duration of time from the sample date to the prediction data,and the RMS level of the error could be reduced from 14.09% to 4.05% through calibration.The results indicate that thermosphere densities can be obtained in high precision with this method.