摘要
针对寿命末期大型航天器无控再入大气层前的轨道衰降问题,提出一种经卡尔曼滤波误差分析与参数辨识气动一体化计算改进的轨道摄动模型。基于参数辨识方法生成两行根数(TLE)格式的初始轨道元参数;考虑航天器在低轨道大气环境受到的稀薄跨流域空气动力作用,采用气动特性当地化统一算法计算目标航天器不同轨道高度的气动阻力系数,并解算相应轨道高度下简化根数摄动模型(SGP4)中的未知阻力项;最后基于无损卡尔曼滤波(UKF)方法和外测轨道星历数据对TLE+SGP4模型进行中长期轨道摄动外推计算,分别针对350~300 km和300~150 km高度范围内的大型航天器轨道衰降演化进行跟踪仿真,预报趋势与目标航天器外测轨道星历观测数据结果相符。研究证实寿命末期大型航天器无控再入气动一体化算法结合轨道摄动模型的改进发展,能大幅提高中长期轨道预报模拟能力,推动近地轨道空间目标气动融合轨道数值预报领域发展。
Focusing on the orbit decay problem of large-scale uncontrolled spacecraft at the end of its life, the improved orbit perturbation model was developed with the use of Kalman filter error analysis method as well as the parameter identification aerodynamics unified algorithm. Based on the parameter identification method, the initial orbital elements of two-line elements(TLE) were generated. Considering the rarefied aerodynamics effect covering various flow regimes in low earth orbit(LEO) environment, the drag coefficient was computed by the aerodynamics local unified algorithm covering various altitudes, and the unknown drag term for SGP4 was also calculated correspondingly. Finally, the unscented Kalman filter(UKF) method was addressed with the observed ephemeris data into the TLE+SGP4 model for medium and long term orbital propagation. And altitudes range from 350~300 km and 300~150 km of large-scale spacecraft’s orbit decay evolutions were simulated;the prediction trend of orbit was consistent with the observed ephemeris data of target spacecraft. The research in this paper validates the improvement of orbit perturbation model combined with the aerodynamics unified algorithm for large-scale spacecraft during uncontrolled entry at the end of its life, which can significantly improve the medium and long term orbit prediction ability and promote the development of orbit numerical predication field combined with the aerodynamics for LEO space objects.
作者
高兴龙
陈钦
李志辉
丁娣
GAO Xinglong;CHEN Qin;LI Zhihui;DING Di(State Key Laboratory of Aerodynamics,CARDC,Mianyang 621000,China;Facility Design and Instrumentation Institute,CARDC,Mianyang 621000,China;Hypervelocity Aerodynamics Institute,CARDC,Mianyang 621000,China;National Laboratory of Computational Fluid Dynamics,Beijing 100191,China;Computational Aerodynamics Institute,CARDC,Mianyang 621000,China)
出处
《飞行力学》
CSCD
北大核心
2020年第6期70-76,共7页
Flight Dynamics
基金
国家重点基础研究发展计划(2014CB744100)
国家自然科学基金资助(11902331)。
关键词
无控航天器
轨道预报
跨流域空气动力学
无损卡尔曼滤波
SGP4
uncontrolled spacecraft
orbit prediction
aerodynamics covering various flow regimes
unscented Kalman filter
SGP4
