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The particle filter (PF) is investigated in this paper to solve the spacecraft attitude and gyro draft estimation problem based on biased gyro and vector observations. For alleviating the potential computational burden problem associated with the number of required particles, a dual PF filtering algorithm is adopted, in which the first PF is used to estimate the quaternion and the second to determine the gyro drift errors. The attitude is expressed by three-component vector generalized Rodrigues parameters (GRPs), where only three parameters are needed to describe orientation and the singularity of the covariance matrix when using unit quaternion in attitude estimation due to unit norm constraints is also avoided. The efficiency of the dual PF estimator is verified through numerical simulation of a fully actuated rigid body with gyro and three-axis-magnetometers (TAM). For comparison, unscented Kalman filter (UKF) is used to gauge the performance of PF. The results presented in this paper clearly demonstrate that the PF is superior to UKF in coping with the nonlinear model.