摘要
针对非视距(NLOS)环境下超宽带(UWB)定位精度下降与惯性导航系统(INS)长期定位结果发散的问题,本文提出了一种基于自适应无迹卡尔曼滤波(AUKF)与图优化的UWB/INS组合定位方法。首先,基于改进IGGⅢ函数对UWB实测伪距与INS定位伪距的偏差赋予不同权重进行M估计,实现NLOS鉴别与重构;然后,采用AUKF融合观测器进行UWB/INS组合定位估计,通过引入自适应因子根据新息变化调整卡尔曼增益,提高组合定位估计的精度;最后,采用以INS增量与视距UWB伪距为约束的图优化方法,进一步抑制了组合定位的NLOS误差,提升了定位估计的准确度。定位试验表明,本文算法平均定位精度达到0.14 m,相对于传统组合定位方法提升了约22%,能够保证室内复杂场景下的定位性能。
To address the problems of degraded ultra-wideband(UWB)positioning accuracy in non-line of sight(NLOS)environments and the divergence of long-term Inertial Navigation System(INS)positioning results,a UWB/INS integrated positioning method based on adaptive unscented Kalman filter(AUKF)and graph optimization is proposed.First,based on an improved IGGIII function,M-estimation is performed by assigning different weights to the deviations between UWB-measured pseudoranges and INS-positioning pseudoranges,achieving NLOS identification and reconstruction.Second,AUKF is employed to fuse observations for UWB/INS integrated positioning estimation.By introducing an adaptive factor that adjusts the Kalman gain according to the innovation variation,the accuracy of the integrated positioning estimate is enhanced.Finally,a graph optimization method constrained by INS increments and line-of-sight(LOS)UWB pseudoranges is adopted,further suppressing the NLOS error in the integrated positioning and improving the accuracy of the positioning estimate.Positioning experiments demonstrate that the proposed algorithm achieves an average positioning accuracy of 0.14 m,representing an improvement of approximately 22%compared to traditional integrated positioning methods,and effectively ensures positioning performance in complex indoor scenarios.
作者
李文博
关维国
时永宝
LI Wenbo;GUAN Weiguo;SHI Yongbao(College of Electronic and Information Engineering,Liaoning University of Technology,Jinzhou 121001,China)
出处
《测绘通报》
北大核心
2025年第10期94-99,共6页
Bulletin of Surveying and Mapping
基金
2022年辽宁省“揭榜挂帅”科技计划重点项目(202202061)
2022年辽宁省教育厅项目(LJKFZ20220238)。
关键词
非视距
自适应卡尔曼滤波
M估计
图优化
组合定位
non-line of sight
adaptive Kalman filter
M-estimation
graph optimization
combined positioning
