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Navigation is a precondition for ocean space vehicles to work safely in polar regions. The traditional polar algorithms employ the grid strapdown inertial navigation system (SINS) as the backbone and Doppler velocity log (DVL) output velocity as measurements to constitute the integrated navigation system, of which, however, the position errors still accumulate with time. The ultra-short baseline (USBL) position system can provide position information that can be used to improve the performance of the SINS/DVL integrated system. Therefore, a grid SINS/DVL/USBL integrated algorithm for polar navigation is proposed in this paper. In order to extend the availability of the USBL and improve integration accuracy in polar regions, the USBL observation model is established based on the relative position measurement firstly. Then, a grid SINS/DVL/USBL integrated algorithm is proposed to fuse the information of these sensors with a modified Kalman filter (MKF) dealing with the sparse USBL output. Finally, a vector fault detection method, which takes the measurements as detection objects instead of the filter, is designed to locate the measurement fault and can be employed by the centralized filter to improve the fault-tolerant. Simulation and experiment results show that the proposed grid SINS/DVL/USBL integrated navigation system can further restrain SINS errors especially the position errors effectively. Meanwhile, the vector fault detection method can detect and isolate the fault measurements of centralized filter immediately and accurately. Therefore, the proposed fault-tolerant grid SINS/DVL/USBL integrated navigation algorithm can improve the reliability and accuracy of polar navigation for ocean space application.

A Fault-Tolerant Polar Grid SINS/DVL/USBL Integrated Navigation Algorithm Based on the Centralized Filter and Relative Position Measurement