摘要
针对北斗卫星导航系统(BDS)钟差预报产品无法满足高精度快速服务需求的现状,提出了一种基于BDS-2/BDS-3联合估计的超快速卫星钟差预报优化策略。区别于传统两步法预报模型,利用稀疏建模方法一步求解所有模型项,并通过BDS-2/BDS-3星间相关性实现了模型系数解的增强;为进一步降低模型残差的影响,基于残差序列时空相关性,利用半变异函数重构了模型估计的权阵。为验证提出的钟差预报模型,设计了12套对比方案,实验结果表明:基于稀疏建模的钟差模型参数一步估计可略微提高钟差预报精度;通过引入星间相关性对随机模型进行精化,钟差序列一步建模可分别将BDS-2与BDS-3卫星钟差18h预报精度提升28.6%与27.2%;基于半变异函数建模的模型残差相关性提取,可实现BDS-2与BDS-3预报钟差精度8.0%与11.1%的提升。因此,提出的优化策略对当前北斗超快速卫星钟差预报产品精化具有重要意义。
It is presented that the BDS predicted clock offsets products cannot obtain the requirement of rapidly high-accurate services.In this paper,an optimization strategy of ultra-rapid satellite clock offsets prediction based on BDS-2/3 joint estimation is proposed.Different from the traditional two-step prediction model,this paper uses the sparse modeling method to solve all model items in one step,and enhances the model coefficient solution through BDS-2/3 inter satellite correlation.The temporal correlations of model residuals series are used to reconstruct the weight matrix by variogram method.According to the results of predicted clock offset experiments,it is indicated that the one-step estimation of clock offset model by sparse modeling can slightly increase the accuracy of prediction.By introducing inter satellite correlation to refine the stochastic model,one-step clock offsets sequence modeling can improve the 18 hour prediction accuracy of BDS-2 and BDS-3 satellites by 28.6%and 27.2%respectively.After the introduction of variogram model in updating the weight matrix,the clock offset prediction model is further refined with 8.0%and 11.1%for BDS-2 and BDS-3.Therefore,the proposed improved strategies for BDS ultra-rapid satellites clock offsets prediction using BDS-2 and BDS-3 integrated processing are meaningful for the current BDS ultra-rapid satellites clock offsets prediction products.
作者
马昌忠
王潜心
胡超
闵扬海
王泽杰
MA Chang-zhong;WANG Qian-xin;HU Chao;MIN Yang-hai;WANG Ze-jie(Key Laboratory of Land Environment and Disaster Monitoring,MNR,China University of Mining and Technology,Xuzhou 221116,China;School of Environment Science and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China)
出处
《导航定位与授时》
2020年第5期28-36,共9页
Navigation Positioning and Timing
基金
国家自然科学基金(41874039)
江苏省自然科学基金(BK20191342)。