No 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.

An improved algorithm is proposed to manage with the huge computation burden of the quaternion particle filter in aircraft attitude estimation. Based on the particle filtering frame, the new filter provides robust performance for nonlinear and non-Gaussian stochastic systems. And the posterior distribution of the new estimator is approximated as a new quaternion distribution to realize parallel computation. In addition, similar to the extended Kalman filter, this new method implements time update by replacing particles update with linear transformation to reduce computational complexity. Numerical simulations are carried out to compare the new algorithm to the extended Kalman filter and to quaternion particle filter in simulation results. The simulation results indicate that this estimation technique has faster convergence rate than the extended Kalman filter and takes less computation times than quaternion particle filter under the same accuracy as quaternion particle filter.