一种基于模糊聚类分析的新型RAIM算法
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  • 英文篇名:A new RAIM algorithm based on fuzzy clustering analysis
  • 作者:谷守周 ; 刘一 ; 施闯 ; 秘金钟 ; 党亚民 ; 张龙平
  • 英文作者:GU Shouzhou;LIU Yi;SHI Chuang;BEI Jinzhong;DANG Yamin;ZHANG Long-ping;GNSS Research Center,Wuhan University;Chinese Academic of Surveying and Mapping;College of Geomatics,Shandong University of Science and Technology;
  • 关键词:完备性 ; 接收机自主完备性监测 ; 模糊c均值 ; 单点定位
  • 英文关键词:integrity;;RAIM;;FCM;;single point positioning
  • 中文刊名:CHKD
  • 英文刊名:Science of Surveying and Mapping
  • 机构:武汉大学卫星导航定位技术研究中心;中国测绘科学研究院;山东科技大学测绘科学与工程学院;
  • 出版日期:2018-04-18 17:09
  • 出版单位:测绘科学
  • 年:2018
  • 期:v.43;No.244
  • 基金:国家重点研发计划课题项目(2016YFB0501801,2016YFB0502105,2016YFB0501405);; 全球连续监测评估系统项目(GFZX0301040308-06);; 国家基础测绘科技项目(2017KJ0205);; 中国测绘科学研究院基本科研业务费项目(7771730,7771707,7771717)
  • 语种:中文;
  • 页:CHKD201810001
  • 页数:6
  • CN:10
  • ISSN:11-4415/P
  • 分类号:5-10
摘要
针对接收机自主完备性监测(RAIM)中多粗差的探测识别难题,该文在研究了模糊聚类分析原理和特点的基础上,利用QR检校法构建单点定位的全设计矩阵,提出了基于模糊c均值(FCM)的模糊聚类分析的新型RAIM算法,以实际观测数据对比分析了硬聚类分析和模糊聚类分析在单个粗差和两个粗差下的探测识别性能。由试验结果可知,该文方法可有效实现多个粗差的探测识别问题,解决了单点定位中的质量控制问题,可有效保障单点定位结果的可靠性。
        To solve the problem of checking multiple gross errors of receiver autonomous integrity monitoring(RAIM),this paper designed full matrix of single point positioning by QR calibration,and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means(FCM).And on the condition of single or multiple gross errors,the performance of hard or fuzzy clustering analysis were compared.As the results of the experiments,the fuzzy clustering method based on FCM principle could detect multiple gross error effectively,also achieved the quality control of single point positioning and ensured better reliability results.
引文
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