Predicting pilot look-angle with a radial basis function network

This paper demonstrates that a radial basis function (RBF) network can be used to estimate or predict future head-positions from samples of the current and past positions. The results, shown for a network trained and tested over a recorded head position time series, imply that the technique is a viable method for reducing latency in the head-slaved computer generated imagery of a flight simulator. The procedure for building the network-based estimator is straightforward, using example-based learning to associate certain transformations on the current position to the value of position at a later time, The success of the approach is found to be dependent on the degree to which the training data represent the full range of motion found in the simulator. A single-output network is shown to accurately estimate rotational position (azimuth) over all motion types of interest, a range from low through high acceleration motion. It is also shown that performance can be improved by adding data variations to the network training set. The network´s accuracy is evaluated for several different prediction intervals, from 150 msec through 350 msec, with the best results found to be at 200 msec and below. Finally, the results for a single-output network are extended to a two-output network, demonstrating prediction of both elements of the look-angle (azimuth, elevation) in one RBF network