摘要
基于北斗ICD文件的钟差预报二次多项式模型,利用北斗卫星导航系统(BDS)发播的钟差参数对卫星钟差进行预报,采用IGS数据分析中心的钟差产品作为评价参考来分析BDS星钟预报误差;采用主控站多模接收机采集到的伪距观测数据,以及IGS的精密轨道和钟差产品,进行精密单点定位数据处理,得到BDT与IGST之间的时间偏差;利用中国科学院国家授时中心北斗监测型接收机实测数据,结合广播钟差数据和精密钟差产品来进一步分析广播钟差的预报误差对卫星导航单向授时性能的影响。试验结果表明:采用广播钟差的预报结果相对于精密钟差的均方根误差(RMS)小于3 ns,采用广播钟差的单向授时结果和精密钟差的单向授时结果,存在±5 ns的偏差。试验结果为进一步提高授时精度提供重要的参考。
Based on the clock difference prediction model of Beidou ICD, the clock difference parameters broadcasted by Beidou satellite navigation system are used to predict the satellite clock difference. The clock difference products of IGS data analysis center are used as reference to analyze BDS satellite clock prediction error. The Pseudo-range observations of multimode receiver of the master station, the precise orbit and clock difference products oflGS are used to get the time difference between BDT and IGST by using the precise point positioning method. The data measured with Beidou monitoring receiver at NTSC, combined with thebroadcasted clock difference data and precise clock difference products, are used to analyze the impact of the prediction errors of broadcast clock difference on satellite navigation one-way timing performance. The experimental results show that the root mean square (RMS) of prediction error of the broadcast clock difference is less than 3 ns with respect to the precise clock difference. The differences between the one-way timing results based on the broadcast clock difference and the one-way timing results based on precise clock difference is within ± 5 ns. The test results provide important reference for further improving the timing accuracy.
作者
魏亚静
袁海波
董绍武
广伟
高喆
WEI Ya-jing YUAN Hai-bo DONG Shao-wu GUANG Wei GAO Zhe(National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China University of Chinese Academy of Sciences, Beijing 100049, China Key Laboratory of Time and Frequency Primary Standards, National Time Service Center, Chinese Academy of Sciences, Xi'an 710600, China)
出处
《时间频率学报》
CSCD
2016年第4期301-307,共7页
Journal of Time and Frequency
基金
国家自然科学基金资助项目(11303032)