Augmented reality ‎(‎AR‎)‎ systems are often used to superimpose virtual objects or information on a scene to improve situational awareness‎.‎ Delays in the display system or inaccurate registration of objects destroy the sense of immersion a user experiences when using AR systems‎.‎ AC electromagnetic trackers are ideal for these applications when combined with head orientation prediction to compensate for display system delays‎.‎ Unfortunately‎,‎ these trackers do not perform well in environments that contain conductive or ferrous materials due to magnetic field distortion without expensive calibration techniques‎.‎ In our work we focus on both the prediction and distortion compensation aspects of this application‎,‎ developing a ‎"‎small footprint‎"‎ predictive filter for display lag compensation and a simplified calibration system for AC magnetic trackers‎.‎ In the first phase of our study we presented a novel method of tracking angular head velocity from quaternion orientation using an Extended Kalman Filter in both single model ‎(‎DQEKF‎)‎ and multiple model ‎(‎MMDQ‎)‎ implementations‎.‎In the second phase of our work we have developed a new method of mapping the magnetic field generated by the tracker without high precision measurement equipment‎.‎ This method uses simple fixtures with multiple sensors in a rigid geometry to collect magnetic field data in the tracking volume‎.‎ We have developed a new algorithm to process the collected data and generate a map of the magnetic field distortion that can be used to compensate distorted measurement data‎.‎