毛宁, 李安, 许江宁, 覃方君, 李方能. 惯性/重力组合导航可观测性分析及鲁棒滤波方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230075
引用本文: 毛宁, 李安, 许江宁, 覃方君, 李方能. 惯性/重力组合导航可观测性分析及鲁棒滤波方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230075
MAO Ning, LI An, XU Jiangning, QIN Fangjun, LI Fangneng. Observability analysis and robust fusion algorithms of INS/Gravity integrated navigation[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230075
Citation: MAO Ning, LI An, XU Jiangning, QIN Fangjun, LI Fangneng. Observability analysis and robust fusion algorithms of INS/Gravity integrated navigation[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230075

惯性/重力组合导航可观测性分析及鲁棒滤波方法

Observability analysis and robust fusion algorithms of INS/Gravity integrated navigation

  • 摘要: 为了满足水下航行器对于长航时、高精度和高隐蔽性的导航定位需求,提出了一种基于自适应鲁棒SITAN算法的惯性/重力组合导航方法。首先建立了惯性/重力组合导航系统的数学模型,然后对系统的可观测组合状态进行了分析,研究可用于SITAN匹配算法的系统状态变量,最后通过比较滤波过程中新息协方差矩阵的递推值与计算值之间的差异,设计了一种新型的补偿因子,并提出了自适应鲁棒的SITAN匹配算法。分别选取了三片不同海域进行长航时仿真试验,结果表明:传统SITAN算法均无法完成长航时稳定的匹配导航;与基于Sage-Husa自适应滤波的SITAN算法相比,所提改进算法位置误差的均值和标准差平均提高15.2%和41.4%,提高系统定位精度的同时,增强了系统的鲁棒自适应能力。

     

    Abstract: Objectives: INS/gravity integrated navigation is an important research direction for autonomous navigation of underwater vehicles, and it is also an important part of the construction of underwater Positioning, Navigation and Timing (PNT) system. To satisfy the needs of underwater vehicles for long endurance, high accuracy and high stealth navigation and positioning, an ins/gravity matching navigation algorithm based on the adaptive robust SITAN algorithm was proposed. Methods: The mathematical model of the ins/gravity matching navigation system is first developed, then the observable combined states are analysed and the state variables that can be used in the SITAN algorithm are investigated. Finally, a new compensation factor is designed by comparing the difference between recursive and calculated values of the innovation covariance matrix in the filtering process, and an adaptive robust SITAN algorithm is proposed. Results: Three different sea areas are selected for the longendurance simulation test. The results show that the traditional SITAN algorithm cannot accomplish stable matching navigation at long navigation time, and compared with the SITAN algorithm based on Sage-Husa adaptive filtering, the proposed improved algorithm has an average increase of 15.2% and 41.4% in the mean and standard deviation of position errors. Conclusions: By adding a new compensation factor, the adaptive robust SITAN algorithm can adjust the measurement noise covariance and filter gain at the same time, which enhances the robust adaptive capability of the system while improving the positioning accuracy. Moreover, this method does not need to introduce external auxiliary information, which is of great significance to the long-term autonomous navigation of underwater vehicles.

     

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