A Shipborne Drones Transfer Alignment Algorithm Based on Relative Attitude Nonlinear Time-Varying Model

The transfer alignment algorithm is a key technology for the prelaunch attitude initialization of shipborne drones. The estimation results of the traditional filter-based transfer alignment algorithm depend on a priori information. Different levels of inertial navigation systems (INSs) necessitate the design of distinct filter parameters. Moreover, in complex working environments, improper filter parameter design often leads to suboptimal estimation results or even filter divergence. In light of the limitations of current methods, this article introduces an analytical transfer alignment algorithm that utilizes the time-varying characteristics of relative attitude. The method reveals the propagation mechanism of initial attitude error and relative installation error in the attitude update process, and a relative attitude nonlinear model of primary INS and secondary INS is developed. Equivalent rotation vectors and higher-order infinitesimal principles are used for model simplification and linearized approximation, and the decoupling of error sources is realized. Finally, through the two-axis platform sway and shipboard experiments, it is concluded that the proposed method verified can achieve the transfer alignment accuracy within 0.04^° (3σ) for INS with gyro bias stability of 0.5^°/h.