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.>