| 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, 2024, 49(11): 2113-2121. DOI: 10.13203/j.whugis20230075
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Inertial navigation system (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 Sandia inertial terrain-aided navigation（SITAN） algorithm was proposed.
The mathematical model of the INS/gravity matching navigation system is first developed, then the observable combined states are analyzed 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.
Three different sea areas are selected for the long-endurance simulation test. The results show that 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 value and standard deviation of position errors.
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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