In-flight initial alignment for small UAV MEMS-based navigation via adaptive unscented Kalman filtering approach

In this paper, an adaptive unscented Kalman filter for UAV's MEMS-based navigation is derived to realize in-flight initial alignment aided by GNSS (global navigation satellite system) and fulfill data fusion. In the filter, unscented transformation is used to handle strong INS (Inertial Navigation System) model nonlinearity under large misalignment condition due to large and sudden maneuvers, and the technique of optimal adaptive factor is used to resist the influence of noise uncertainty of MIMU (MEMS-based Inertial Measurement Unit) and kinematic model errors. The flight test results indicate the proposed alignment algorithm can complete the initial alignment more quickly and accurately compared with the conventional EKF/UKF-based in-motion alignment approaches, especially when the initial attitude errors are large. As a unified in-flight alignment, it can guarantee the accurate and reliable alignment in situations of either large or small initial attitude errors without model changes for small UAV applications.