This paper presents a reliable in-motion alignment algorithm for a low cost Strapdown Inertial Navigation System/Global Positioning System (SINS/GPS) combination under random misalignment angles, which transforms attitude alignment into an attitude estimation problem. Based on Rodrigues parameters, an alignment model with a linear state-space equation and a second order nonlinear measurement equation is established. Furthermore, by employing a Taylor expansion on the nonlinear measurement equation, we implement a second order Extended Kalman Filter (EKF2). The proposed method uses a single filter that can not only determine the initial attitude, but also estimate the sensor errors. In addition, a scheme is given for avoiding singularity, which makes the algorithm more widely suitable for random misalignment angles. Experimental ground tests are performed with a low-cost Micro-Electromechanical System (MEMS) SINS, which validates the efficacy of the proposed method. The performance compared to the traditional alignment algorithm is also given.

For a strapdown inertial navigation system (SINS), a traditional navigation frame switching mechanization can be used to solve the all-Earth navigation problem, but this kind of method suffers from complex and discontinuous algorithms. On the basis of improving vertical channel damping technology, a complete and unified all-earth navigation scheme arranged in the earth-centered earth-fixed (ECEF) frame is designed as an alternative for global applications, which results in a simpler continuous algorithm and lower computational complexity. We also propose a virtual polar region technology based on the grid attitude and velocity invariant method (G-AVIM), which can complete the polar navigation verification through the actual test data of non-polar regions. In the reconstructed polar region trajectory, the vehicle attitude, velocity, and height information relative to the ground remain unchanged, so the real test environment can be completely reproduced in the polar region. Simulations and field test results verified the proposed methods.

In order to solve the problems of heavy computational load and poor real time of the information fusion method based on the federated Kalman filter (FKF), a novel information fusion method based on the complementary filter is proposed for strapdown inertial navigation (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system of an aerospace plane. The complementary filters are designed to achieve the estimations of attitude, velocity, and position in the SINS/CNS/GPS integrated navigation system, respectively. The simulation results show that the proposed information fusion method can effectively realize SINS/CNS/GPS information fusion. Compared with FKF, the method based on complementary filter (CF) has the advantages of simplicity, small calculation, good real-time performance, good stability, no need for initial alignment, fast convergence, etc. Furthermore, the computational efficiency of CF is increased by 94.81%. Finally, the superiority of the proposed CF-based method is verified by both the semi-physical simulation and real-time system experiment.

The inner lever arm effect of the accelerometer triad, also known as the size effect, means that the sensitive centers of the three accelerometers in an inertial measurement unit (IMU) cannot be mechanically installed at one point. Without compensation, it will seriously deteriorate the accuracy of calculated velocity during rotation. For the rotational inertial navigation system (RINS), this paper analyzes the influence of the inner lever arm on the rotational alignment, and proposes a multi-position alignment method based on 2-dimensional inner lever arm online estimation. In this method, the measurement center of IMU is defined at the sensitive center of a horizontal accelerometer, so that the influential inner lever arm can be simplified to two dimensions, and a fine alignment filter is designed for on-line estimation. Simulations and experiments show that the method can effectively ensure the accuracy of multi-position alignment while reducing the state dimension and calculation amount. The repeatability of the online estimation result of the inner lever arm is better than 0.1 mm.