Particle Filtering for Gyroless Attitude/Angular Rate Estimation Algorithm

Abstract

A novel spacecraft attitude and angular rate estimation algorithm is proposed using particle filter (PF) with the modified Rodrigues parameters (MRPs) representing the attitude, under both gyroless and gyro-disable modes. Belonging to the class of Monte Carlo sequential methods, the filter uses the unscented Kalman filter (UKF) to generate importance proposal distribution. It can not only avoid the limitation of the UKF which can only apply to Gaussian distribution, but also avoid the limitation of the standard PF which can not include the new measurements. A special procedure is developed to account for the curse of the dimensionality related to the PF in existence of augmented state vector. MRPs are used for attitude representations. The singularity problem associated with the MRPs is addressed as well using switching method. Simulation results demonstrate that the estimation algorithm has faster convergence rate and higher accuracy compared with the recently presented UKF, and it shows a reduction of about 10% in computational load compared with that using the quaternion estimation algorithm.