Predictive head movement tracking using a Kalman filter

The use of head movements in control applications leaves the hands free for other tasks and utilizes the mobility of the head to acquire and track targets over a wide field of view. We present the results of applying a Kalman filter to generate prediction estimates for tracking head positions. A simple kinematics approach based on the assumption of a piecewise constant acceleration process is suggested and is shown to track head positions with an rms error under 2 degrees for head movements with accelerations smaller than 3000 degrees /s. To account for the wide range of head dynamic characteristics, an adaptive approach with input estimation is developed. The performance of the Kalman filter is compared to that based on a simple polynomial predictor.