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An extension of the linear H ∞ filter, presented here as the extended H ∞ particle filter (E H ∞ 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 H ∞ 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 H ∞ particle filter (E H ∞ PF) the nature of the system, composed of dynamics and noises, seeks to degrade the state estimate. The E H ∞ 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 H ∞ 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. [ABSTRACT FROM AUTHOR]