摘要
我国已经启动了火星探测计划,中国VLBI网(CVN)将肩负起相关探测器的测轨与定位任务。CVN已组织了数次对欧空局火星快车(MEX)卫星的跟踪观测,以演练、检验与完善软硬件系统。本文基于嫦娥一号探月工程中开发的定位分析软件,对CVN于2007年5月30日的MEX卫星跟踪数据进行了定位归算。结果表明,ps/s精度的时延率的定位效果明显低于ns精度的时延的定位效果。若采用时延和时延率联合定位则需要同时解算速度修正,否则,速度采用值误差将直接影响定位参数的解算精度。此次观测的时延精度约2ns至5ns,对MEX卫星的定位精度仅约0.1as。为此有必要积极尝试差分VLBI、同波束VLBI等观测方式,进一步消除观测资料的系统误差和压缩噪声水平,以提高CVN对火星卫星的定位精度。
China has already set up the Mars exploration project,and the Chinese VLBI Network （CVN） will take on the measurements of the related satellite orbit and position. The CVN has tracked the Mars Express （MEX）,a Martian satellite of the European Space Agency （ESA）,for several times in order to train,test and improve the software and hardware systems. Based on the positioning reduction software developed during the Chang＇E-1 lunar exploration project,the tracking data of CVN on the 30 May 2007 has been processed to determine the coordinates of MEX satellite in this paper. Results show that the positioning of the delays in accuracy of nanoseconds used in the satellite positioning reduction is more effective than that of the delay rates in accuracy of picoseconds per second. If the delays and rates are jointly used in the positioning reduction,the correction to the a priori velocity ought to be solved simultaneously with the position parameters. Otherwise the error in the a priori velocity would directly degrade the positioning accuracy. In this tracking of MEX the observation noise of the delay is about levels of 2ns to 5ns,and the corresponding positioning accuracy is about 0.1as. In order to improve the positioning accuracy of the Martian satellite,it is urgent for CVN to actively practise the differential VLBI,same beam VLBI and so on,to further reduce the systematic errors and to compress the noise level of observations.
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2010年第7期1718-1723,共6页
Journal of Astronautics
基金
国家自然科学基金(10973030
10778635)
探月工程(嫦娥一号)
上海市科学技术委员会(06DZ22101)
国家高技术研究发展计划(2008AA12A209
2008AA12A210)
作者简介
李金岭（1964-），男，研究员，博导，主要从事射电天体测量、空间大地测量和天文参考系研究工作。通信地址：上海市南丹路80号（200030）电话：13004150764E-mail：JLL@SHAO．AC．CN
