Nonlinear Explicit Stochastic Attitude Filter on SO(3)

摘要

This work proposes a nonlinear stochastic filter evolved on the Special Orthogonal Group mathbbSO (3) as a solution to the attitude filtering problem. One of the most common potential functions for nonlinear deterministic attitude observers is studied and reformulated to address the noise attached to the attitude dynamics. The resultant estimator and correction factor demonstrate convergence properties and remarkable ability to attenuate the noise. The stochastic dynamics of the attitude problem are mapped from mathbbSO (3) to Rodriguez vector. The proposed stochastic filter evolved on mathbbSO (3) guarantees that errors in the Rodriguez vector and estimates steer very close to the neighborhood of the origin and that the errors are semi-globally uniformly ultimately bounded in mean square. Simulation results illustrate the robustness of the proposed filter in the presence of high uncertainties in measurements.