Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as the gyro and magnetometer biases. The adaptation is performed using a single adaptive factor calculated in the base of the residual sequence and the process noise covariance matrix tuned dynamically via multiplication with this factor. The simulation results demonstrate that the proposed Adaptive Unscented Kalman Filter (AUKF) outperforms the conventional UKF in the sense of estimation accuracy and convergence characteristics. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.