Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.

The traditional polar rapid transfer alignment (RTA) are generally modeled under small angle misalignment assumption. Those linearized and nonlinear polar RTA error models presented, so far, are not capable of accurately representing the nonlinear properties of the system where the all-orientation large attitude errors happen between the master and slave strap-down inertial navigation systems (SINS). It cannot satisfy the accuracy for the SINS initialization and has a singularity in computation. In this article, the misalignment quaternion in the grid frame was selected as the state and observation. An innovative polar RTA algorithm using the quaternion matching and the augmented unscented Kalman filter (UKF) was then proposed to estimate all-orientation large attitude misalignments in polar regions. Furthermore, the associated error propagation equations were redesigned in the grid frame and with compensations for the lever-arm effect. The augmented UKF model was further adjusted to apply. Simulation and semi-physical experiment results demonstrated that the performance of the innovative polar RTA algorithm proposed here outperformed those via traditional polar RTA techniques, especially with 3-axis large-angle misalignments, and was even robust with the disturbance of lever-arm effect aggravated by the harsh polar environment.

The polar rapid transfer alignment (RTA) is preferred to initialize the moving base. The heading error under the grid frame accumulates with time, and the aid to navigation is required to calibrate the strapdown inertial navigation system (SINS). However, the Arctic has a complex environment where auxiliary navigation devices, customarily used in low and medium latitudes, cannot operate appropriately in polar regions. The SINS attitude reference may contain errors to contaminate the measurement quality of RTA, causing alignment failure. Thus,we introducedpolarized-light navigation to offer the heading and assist attitude matching. However, its heading error is associated with the longitude error amplifiedwith increasing latitudes, and the outputs from systems are not unified into an identical navigation frame. For success in initialization at high latitudes, this work innovatively proposed a polar rapid transfer alignment assisted by the improved polarized-light navigation. Firstly, the grid heading of the polarized-light navigation was redefined under the grid frame, and the proposed weighted-clustering algorithm was used to process the skylight polarization pattern and extract the angle out. Eventually, we established the mathematical model of RTA with disturbances and augmented the grid heading solution from the polarized-light navigation into measurements, solving the low-quality attitude reference of the master SINS. Simulations and semiphysical experiments demonstrated that the proposed algorithm performed better, and the estimate error approached only several minutes of arc when the master attitude reference had over 1-degree attitude error.