The Extended H∞ Particle Filter for Attitude Estimation Applied to Remote Sensing Satellite CBERS-4

Abstract

An extension of the linear (Formula presented.) filter, presented here as the extended (Formula presented.) particle filter (E (Formula presented.) PF), is used in this work for attitude estimation, which presents a process and measurement model with nonlinear functions. The simulations implemented use orbit and attitude data from CBERS-4 (China–Brazil Earth Resources Satellite-4), making use of the robustness characteristics of the (Formula presented.) filter. The CBERS-4 is the fifth satellite of an advantageous international scientific interaction between Brazil and China for the development of remote sensing satellites used for strategic application in monitoring water resources and controlling deforestation in the Legal Amazon. In the extended (Formula presented.) particle filter (E (Formula presented.) PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The E (Formula presented.) PF deals with this by aiming for robustness, using a performance parameter in its cost function, in addition to presenting an advantageous feature of using a reduced number of particles for state estimation. The justification for the application of this method is because the non-Gaussian uncertainties that appear in the attitude sensors impair the estimation process and the E (Formula presented.) PF minimizes in signal estimation the worst effects of disturbance signals without a priori knowledge of them, as shown in the results, in addition to presenting good precision within the prescribed requirements, with 100 particles representing a processing time 2.09 times less than the PF with 500 particles.