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基于闪烁谱拟合的不均匀体参数最大似然估计

Maximum likelihood estimation of irregularity parameters with spectrum fitting
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摘要 获得闪烁相关电离层不均匀体参数是建立闪烁预测模型的基础.针对长期历史数据为单站观测的情况,提出利用闪烁谱拟合方法,通过最大似然估计(maximum likelihood estimation,MLE)获得多个闪烁相关参数的最优估计,并利用Monte Carlo方法对谱参数MLE结果进行分析.结果表明:利用单指数谱模型可以获得谱参数的无偏最优估计,估计参数的90%置信度范围可以覆盖真实值;利用双指数谱模型可以获得谱参数的近似无偏最优估计,绝大部分估计参数的90%置信度范围可以覆盖真实值;两种模型估计的统计结果均符合预定分布.利用实测数据对本文方法进行进一步验证,估计参数的统计分析结果符合预期分布,验证了该方法的有效性;仿真数据和实测数据分析结果同时还表明,该方法可适用于中等及强闪烁情况.本文方法为利用长期历史数据建立闪烁预测模型提供了重要实现途径. Parameters on ionospheric irregularity are essential for scintillation modeling.As scintillation is generally observed on a single site basis,it had been a hard work to retrieve various irregularity parameters.However,development of scintillation theory model provides a possible solution to this difficulty.Here we present a new method to estimate irregularity parameters with maximum likelihood estimation(MLE)based on scintillation spectrum fitting.Quality of the method is verified with Monte Carlo simulation.It shows that an unbiased optimal estimation could be achieved for single spectral index model with all results located within the 90%confidence interval,while a near unbiased optimization could be reached for double spectral index model with most of results in the 90%confidence interval.Statistic distribution of estimation results follows the theoretical one well,showing the viability of estimation.Test is also conducted with real measurements and the result is promising.Two kind of data tests show the method could work efficiently under modest and strong scintillation conditions.This would pave the way for scintillation modeling with commonly observation on single site.
作者 陈涛 刘钝 於晓 郝艺 郭珊 朱庆林 CHEN Tao;LIU Dun;YU Xiao;HAO Yi;GUO Shan;ZHU Qinglin(Unit 91198 of the PLA,Beihai 536100,China;China Research Institute of Radiowave Propagation,Qingdao 266107,China)
出处 《电波科学学报》 CSCD 北大核心 2023年第5期807-815,共9页 Chinese Journal of Radio Science
基金 国家重点研发计划(2020YFB0505603)。
关键词 电离层闪烁 功率谱 最大似然估计(MLE) 全球导航卫星系统 ionospheric scintillation intensity spectrum maximum likelihood estimation(MLE) GNSS
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