摘要
复杂观测环境下卫星信号易受遮挡干扰,观测值异常频发且存在强相关性,故障检测和隔离方法容易错误识别故障位置,导致定位性能急剧下降。针对该问题提出了一种顾及卫星相关性的抗差定位方法,分别通过卡方全局检验和w局部检验对故障进行探测和识别,当卫星之间相关性弱时剔除识别出的故障并完成定位解算,当卫星之间相关性强时采用抗差定位方法。实验结果表明:当卫星之间相关性强时,错误定位故障位置的概率高,传统的故障检测和隔离(FDE)方法会导致定位结果出现极大偏差,顾及卫星相关性的抗差定位方法能够有效降低复杂环境下强相关性的异常观测值带来的影响,显著提升定位精度。
In complex observation environment,satellite signals are prone to occlusion and interference,observation data are frequently abnormal and have strong correlation,and fault detection and excluded methods are prone to misidentify fault location,which leads to a significant decline in positioning performance.To solve this problem,a robust positioning method considering correlation between satellites is proposed.The faults are detected and identified by chi-square global test and w local test,respectively.When the correlation between satellites is weak,the identified faults are excluded and the position solution is completed.When the correlation between satellites is strong,the robust method is used to solve the results.The experimental results show that the strong correlation between satellites leads to a higher misdetection of the fault location,the traditional fault detection and excluded(FDE)method causes positioning result in great deviation,the robust positioning method based on correlation between satellites effectively reduces the influence of abnormal observations with the strong correlation in the complex environment and significantly improve positioning accuracy.
作者
吴有龙
陈帅
姚文进
殷婷婷
陈维娜
Wu Youlong;Chen Shuai;Yao Wenjin;Yin Tingting;Chen Weina(College of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,China;School of Automation;ZNDY Ministerial Key Laboratory,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《南京理工大学学报》
CAS
CSCD
北大核心
2024年第2期209-217,共9页
Journal of Nanjing University of Science and Technology
基金
国家自然科学基金(61803188)
江苏省博士后科研资助基金(2021K463C)
江苏省高等学校自然科学研究项目(19KJB510028)
基础加强计划技术领域基金(2020-JCJQ-JJ-409)。
关键词
卫星导航
抗差定位
相关性
故障检测和隔离
检验
satellite navigation
robust positioning
correlation
fault detection and excluded
tests