摘要
针对5G定位和捷联惯性导航单一定位方式的可靠性和定位精度较差的问题,本文以扩展卡尔曼滤波为基础,提出了融合5G信号到达时间和信号离开角的5G/SINS紧组合导航算法。该算法首先利用惯性传感器输出信息解算用户的位置、速度和姿态,在此基础上利用已知的基站坐标反算出一组虚拟的5G观测值,然后使用该观测值和实际的5G测量值建立统一的观测方程进行滤波解算。仿真试验结果表明,5G/SINS紧组合的定位成功率可达99%以上,且能够有效改善惯导航位推算的发散问题,其定位精度相比单纯的5G定位有了大幅提高,相比5G/SINS松组合受基站数量和基站几何分布的影响较小。融合TOA/AOD的5G/SINS紧组合导航的定位结果有超过99%的历元在3 m以内。在5G观测值中存在系统误差时,5G/SINS紧组合的定位表现优于5G定位和5G/SINS松组合导航。
For the problem that the poor reliability and positioning accuracy of 5G positioning or strapdown inertial navigation system(SINS),this paper proposed a 5G/SINS tightly coupled navigation algorithm integrating time of arrival(TOA)and angle of departure(AOD)based on extended Kalman filter.Firstly,the algorithm uses the output information of the inertial sensor to calculate the position,velocity,and attitude of the terminal.On this basis,a set of virtual 5G measurements are inverted by using the known coordinates of the base station.Then,a unified observation equation is established using the measurements and the actual 5G measurements for filtering.Simulation results showed that the success rate of 5G/SINS tightly coupled navigation could reach more than 99%,and the divergence problem of inertial navigation calculation can be effectively improved.Compared with simple 5G positioning,the positioning accuracy of 5G/SINS tightly coupled navigation is greatly improved,and the influence of base station number and base station geometry distribution is less than that of 5G/SINS loosely coupled navigation.More than 99%of the positioning results of 5G/SINS tightly coupled navigation integrated with TOA/AOD are within 3 m.When there are systematic errors in 5G observations,the positioning performance of 5G/SINS tight coupled navigation is better than that of 5G and 5G/SINS loosely coupled navigation.
作者
郭文飞
齐书峰
邓玥
郭迟
GUO Wenfei;QI Shufeng;DENG Yue;GUO Chi(GNSS Research Center,Wuhan University,Wuhan 430079,China;School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China)
出处
《测绘学报》
EI
CSCD
北大核心
2023年第3期367-374,共8页
Acta Geodaetica et Cartographica Sinica
基金
国家重点研发计划(2018YFC0809804)
国家自然科学基金(41974038)
中国工程科技发展战略湖北研究院重点咨询研究项目(HB2020B13)。
