Satellite attitude estimation in simulated non-Gaussian white noise using Particle Filter and Extended Kalman Filter

Abstract

Extended Kalman filter (EKF) has been found as most widely used algorithm for state estimation due to its simplicity for implementation and theoretically attractive in the sense that minimizes the variance of the estimation error. Nevertheless it is known that EKF algorithm strictly assumed that the nature of the noise or errors in the system is Gaussian white noise. Yet, in real world this is not always true, which will lead to less accurate estimation.  However there is an estimation approach that does not require the assumption of a specific noise as EKF which is particle filter (PF), which hypothetically can provide more accurate estimation under non-Gaussian noise condition. Hence, this work will study and compare accuracy performance of both estimation algorithms in simulated non-Gaussian white noise for satellite attitude application.