基于改进的粒子群优化的北斗卫星钟差组合预报方法  

An improved particle swarm optimization method for predicting ultra-fast clock errors of BeiDou satellites

作　　者：王树魁 雷朝锋 崔蓓 张喜梅 Wang Shukui;Lei Chaofeng;Cui Bei;Zhang Ximei(Shangqiu Normal University,Shangqiu 476000,China;Henan First Geological Brigade Co.,Ltd.,Zhengzhou 455000,China;Nanjing Land Resources Information Center,Nanjing 210005,China;Jilin Qianyuan Geological Engineering Co.,Ltd.,Changchun 130051,China;Anhui Vocational School of Special Education,Hefei 230012,China)

机构地区：[1]商丘师范学院,河南商丘476000 [2]河南省第一地质大队有限公司,郑州455000 [3]南京市国土资源信息中心,南京210005 [4]吉林省乾元地质工程有限公司,长春130051 [5]安徽省特殊教育中专学校,合肥230012

出　　处：《工程勘察》2024年第10期50-56,共7页Geotechnical Investigation & Surveying

基　　金：住房和城乡建设部科学技术计划项目(2020-S-034).

摘　　要：针对现有卫星钟差预测模型预报精度不高的问题,本文构建一种将集合经验模态分解和改进的粒子群优化的随机森林预测算法来预测卫星钟差数据,并采用GPS、北斗钟差数据验证算法的正确性。利用改进的粒子群优化的随机森林模型对集合经验模态分解后的各分量进行预测,并将各分量预测的结果叠加为最终预测结果。最终通过北斗钟差数据取得优良的预测结果,与ISU-P的数据比较,精度提升幅度在12.7%~21.5%之间。In view of the low accuracy of the existing satellite clock error prediction model,this paper constructs a random forest prediction algorithm based on the set empirical mode decomposition and improved particle swarm optimization to predict the clock errors of BeiDou satellite,and uses the GPS clock error to verify the accuracy of the algorithm.The improved method is used to predict the components after the set empirical mode decomposition,and the prediction results of each component are superimposed into the final prediction results.Finally,good prediction results were obtained through the BeiDou clock difference data.Compared with the ISU-P data,the accuracy was improved by 12.7%~21.5%.

关 键 词：集合经验模态分解 改进的粒子群优化算法 随机森林回归算法 北斗超快速钟差 

分 类 号：P228[天文地球—大地测量学与测量工程]