This paper, in allusion to the limitations of traditional transfer alignment methods based on the external measurement equipment or the empirical model of angular deformation, proposes a rapid and accurate transfer alignment method without relying on the empirical angular deformation model. Firstly, the relationship between the actual angular deformation and the angular velocities measured by the gyroscopes in the master and slave inertial navigation systems (INSs) is derived to roughly estimate the angular deformation. Secondly, according to the error characteristics of gyroscopes, the error model of angular deformation is established. Thirdly, expanding the angular deformation error instead of the installation error angle, flexure angle and flexure angle rate into the state vector, a low-order transfer alignment filtering model independent of the empirical angular deformation model is established. The proposed method not only gets rid of the dependence on an empirical angular deformation model, but also realises the rapid and accurate initial alignment of the slave INS without adding any external measurement equipment. The simulations and experiments evidence the validity of the proposed transfer alignment method.

A novel robust scheme for Transfer Alignment (TA) is proposed for improving the accuracy of the navigation of a Hypersonic Cruise Vehicle (HCV). The main goal is to instil robustness in the safety and accuracy of the attitude determination, despite mode uncertainties. This article focuses on Robust Sparse-Grid Quadrature Filtering (R-SGQF) using two given robust factors for norm-bounded model uncertainties in non-linear systems. Missile dynamic and measurement model uncertainties are established to validate TA technologies. The nominal stability of the R-SGQF is defined by estimating error dynamics. The technique gives sufficient conditions for the R-SGQF in terms of two parameterised Riccati equations. Robust stability is analysed using Lyapunov theory and the accuracy level of the Sparse-Grid Quadrature (SGQ) algorithm. Embedding the SGQ technique into the robust filter structure, R-SGQF is not only of robust stability against uncertainty but also of higher accuracy. The simulation results of the TA algorithm demonstrate that attitude determinations validate the effectiveness of the R-SGQF algorithm.

The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.