When a pico satellite is under normal operational conditions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunctions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter(REKF) and robust unscented Kalman filter(RUKF), for the case of measurement malfunctions. In both filters, by the use of defined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satellite, and the results are compared. However, if the measurements are not reliable because of any kind of malfunctions in the estimation system, the Kalman filter gives Inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter (REKF) and robust unscented Kalman filter (RUKF), for the case of measurement malfunctions. In both filters, by the use of defined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satellite, and the results are compared.