Robust Filter Method for SINS/DVL/USBL Tight Integrated Navigation System
Robust Filter Method for SINS/DVL/USBL Tight Integrated Navigation System
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DOI：
10.1109/jsen.2023.3264755
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发表时间：
2023-05
影响因子：
4.3
通讯作者：
Di Wang;Bin Wang;Haoqian Huang;Yiqing Yao;Xiang Xu
中科院分区：
文献类型：
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作者：
Di Wang;Bin Wang;Haoqian Huang;Yiqing Yao;Xiang Xu
针对复杂水下环境下捷联惯导/多普勒计程仪/超短基线系统的干扰问题，提出了一种异步序贯鲁棒滤波方法。引入了方位角、斜距和高度角的USBL原始信息作为测量信息。通过引入异步序贯滤波技术，解决了多传感器信息融合测量维数高所带来的计算量大的问题。同时，提出了一种基于马氏距离的鲁棒卡尔曼滤波算法。基于统计特征设计了离群点检测算法。最后通过仿真和河工试验验证了本文方法的有效性。实验结果表明，本文提出的方法在减少计算量的同时，能有效地剔除野值，提高导航精度。
In order to solve the problem of strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)/ultrashort baseline (USBL) system interference in complex underwater environment, an asynchronous sequential robust filter method is proposed in this article. The USBL original information of azimuth, slant range and altitude are introduced as the measurement information. The problem of large amount of computation caused by the high measurement dimension of multisensor information fusion is solved by introducing asynchronous sequential filter technology. Meanwhile, a robust Kalman filter (KF) algorithm is proposed, which is based on the Mahalanobis distance. The outlier detection is designed based on statistical characteristics. Finally, the effectiveness of the method proposed in this article is verified by simulation and River experiments. The experimental results show that the method proposed in this article can effectively eliminate outliers and improve the navigation accuracy while reducing the amount of computation.