A quaternion-based indirect Gaussian particle filter for nonlinear attitude estimation

Abstract

A recursively indirect quaternion estimator based on the Gaussian particle filter (GPF) is proposed for nonlinear attitude estimation. The key idea is to estimate an on-tangent-plane Gaussian distribution in the GPF scheme for interpreting the uncertainty of the unit quaternion manifold. The unit quaternion is provided with a global nonsingular attitude description in the prediction step, and the three-dimensional attitude error is estimated in the update step. Based on the framework of the GPF, the proposed filter does not need resampling and regularization compared with the PF. The performance of the proposed filter is verified theoretically and evaluated by experiments. The results show that the proposed filter has a faster convergence speed, lower complexity, and lower computational cost than the existing quaternion PF under a comparable accuracy.