基于鲁棒马氏距离统计量的多源融合抗差估计方法

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。

Robust Mahalanobis distance statistic-based multi-sensorintegration robust estimation method

To effectively overcome the interferences of GNSS signals and enhance the reliability of multi-sensor integration positioning incomplex urban environments, a robust Mahalanobis distance statistic-based multi-sensor integration robust estimation method isproposed. With the basis of faulty measurements evaluation and typical model of variance inflation robust estimation, the robustMahalanobis distance statistic is constructed based on the adjacent innovation sequences. The introduction of past innovation contributesto the observation redundancy. Meanwhile, the robustness of anomaly detection statistics can be improved by interacting betweeninnovations from different measurements. According to the statistical property of this robust distance, the critical thresholds are ensuredand then the measurement noise covariance can be adjusted adaptively with two traditional weighted strategies. Some experiments havebeen implemented on the INS / GNSS / LiDAR/ VINS vehicle positioning system in an urban canyon environment. It shows that comparedwith existing methods, the 3D positioning error root-mean-square of proposed method is limited within 3. 37 m. The superiority of ourmethod is further validated by analyzing the positioning results with different significances.