摘要
为了有效抵御复杂多变城市环境下的全球卫星导航系统(GNSS)信号干扰、增强多源融合定位可靠性,提出一种基于鲁棒马氏距离的多源融合抗差估计方法。该方法在分析观测值故障传播特点以及典型方差膨胀抗差估计模型基础上,基于相邻新息序列构造鲁棒马氏距离检验统计量。历史新息的引入能够提高系统观测冗余,同时不同观测量间的新息交互增强了异常检验统计量的鲁棒性。根据鲁棒马氏距离的统计特性,给出抗差关键门限取值规则并分别结合两种典型加权策略自适应调节观测值噪声矩阵。利用典型城市峡谷环境下惯性导航系统(INS)/GNSS/激光雷达(LiDAR)/VINS多源融合车载数据进行相关实验,与现有方法相较,所提方法能够将三维均方根定位误差最低限制在3.37 m。通过对比不同组显著性水平下的定位结果,进一步说明所提方法在城市峡谷环境下定位的优越性。
To effectively overcome the interferences of GNSS signals and enhance the reliability of multi-sensor integration positioning in complex urban environments,a robust Mahalanobis distance statistic-based multi-sensor integration robust estimation method is proposed.With the basis of faulty measurements evaluation and typical model of variance inflation robust estimation,the robust Mahalanobis distance statistic is constructed based on the adjacent innovation sequences.The introduction of past innovation contributes to the observation redundancy.Meanwhile,the robustness of anomaly detection statistics can be improved by interacting between innovations from different measurements.According to the statistical property of this robust distance,the critical thresholds are ensured and then the measurement noise covariance can be adjusted adaptively with two traditional weighted strategies.Some experiments have been implemented on the INS/GNSS/LiDAR/VINS vehicle positioning system in an urban canyon environment.It shows that compared with existing methods,the 3D positioning error root-mean-square of proposed method is limited within 3.37 m.The superiority of our method is further validated by analyzing the positioning results with different significances.
作者
姜颖颖
潘树国
孟骞
高旺
Jiang Yingying;Pan Shuguo;Meng Qian;Gao Wang(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;Key Laboratory of Micro-Inertial Instruments and Advanced Navigation Technology,Ministry of Education,Southeast University,Nanjing 210096,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2024年第2期252-262,共11页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(62203111)
国家重点研发计划(2021YFB3900804)
江苏省自然基金(BK20231434)项目资助。
关键词
多源融合
城市环境
马氏距离
自适应权因子
可靠性
multi-sensor integration
urban environment
Mahalanobis distance
adaptive weight factor
reliability