A New Method For Inertial Strapdown Navigation System Alignment Under Large Misalignment Based On A Velocity Error Transformation

Initial alignment is the basis for obtaining accurate misalignment angles of Strapdown Inertial Navigation Systems (SINS), and its accuracy and efficiency will directly affect the SINS performance. The traditional linear error models for initial alignment based on small misalignment angles have become mature and have been used in a variety of ways, covering static, dynamic and shaky disturbance bases, and are usually based on linear differential error equation models derived from perturbations of the Eulerian Ф angle or ψ angle. However, with the development of SINS technology and the emergence of different application scenarios, the classical small misalignment angle error model is subject to large errors and inaccuracies in complex applications such as high dynamics and large misalignment angles. As a result, initial alignment techniques and non-linear filtering techniques for large misalignment angles have been researched and developed, and the SINS error model has changed from the traditional linear model to a non-linear model. The existing non-linear error model for large misalignment angles contains a specific force term obtained from the instrument output, which has a direct impact on the filtering performance due to the large errors in the dynamic conditions. The new method proposed in this paper, enables to replace the specific force term in the error model with a gravity term by a velocity error transformation, thus eliminating the effect of the noise of the instrument specific force term. The new method also uses an 5th order CKF to achieve higher filtering accuracy. The method in this paper has the property of faster convergence and higher alignment accuracy. Simulation tests prove that the method proposed in this paper outperforms the traditional methods in the large misalignment angle case. It is also shown that the large misalignment angle error model based on the velocity error transformation is equivalent to the traditional large misalignment angle error model and the classical small misalignment angle linear error model in the small misalignment angle condition.