In order to control the orientation of a satellite, it is important to estimate the attitude accurately. Time series estimation is especially important in micro and nanosatellites, whose sensors are usually low-cost and have higher noise levels than high end sensors. Also, the algorithms should be able to run on systems with very restricted computer power. In this work, we evaluate five Kalman-type filtering algorithms for attitude estimation with 3-axis magnetometer and sun sensor measurements. The Kalman-type filters are selected so that each of them is designed to mitigate one error source for the unscented Kalman filter that is used as baseline. We investigate the distribution of the magnetometer noises and show that the Student's t-distribution is a better model for them than the Gaussian distribution. We consider filter responses in four operation modes: steady state, recovery from incorrect initial state, short-term sensor noise increment, and long-term increment. We find that a Kalman-type filter designed for Student's t sensor noises has the best combination of accuracy and computational speed for these problems, which leads to a conclusion that one can achieve more improvements in estimation accuracy by using a filter that can work with heavy tailed noise than by using a nonlinearity minimizing filter that assumes Gaussian noise. (C) 2019 Elsevier Masson SAS. All rights reserved.