Accurate indoor localization is essential for autonomous robotic agents to perform tasks ranging from warehouse management to remote sensing in greenhouses. Recently Ultra Wideband (UWB) distance measurements have been used to estimate position and velocity indoors. These UWB-measurements are known to be corrupted by a varying bias. Besides, current estimation methods are not suitable for large areas with a low beacon coverage. The goal
of this thesis was therefore twofold. First, a simple bias model was proposed to reduce the influence of the UWB bias while still being implementable on a micro-processor. This model was shown to reduce the measurement error with 50% on validation data. Using this model, UWB-localization in a static beacon-configuration can be quickly improved. Second, an adaptation of the standard Moving Horizon Estimation (MHE) method was proposed that uses a time-window of range measurements to increase the robustness to outliers and is still real-time implementable on a micro-processor. This Moving Horizon Model Parametrization (MH-MP) does not estimate every state in the complete time-window, but only estimates an offset of the initial state in the window. An analysis of simulation data and data gathered in flight has shown that the proposed MH-MP outperforms the Extended Kalman Filter (EKF) in both the
position and velocity estimate and has a comparable computation time. Further research is necessary to investigate the possibility of estimating the UWB-bias model parameters online.