Data Prediction of Optical Head Tracking using Self Healing Neural Model for Head Mounted Display

Abstract

Helmet Mounted Display (HMD) is an essential part in field of avionics. It is worn by the pilot to sight the external environment along with synchronized view of the important parameters of the airplane on its visor. To achieve the perfect synchronized view on the visor of HMD, the coordinates of the external environment and the coordinates of the pilot’s head motion should be in proper synchronization. To acquire the coordinates of the pilot’s head motion, the head tracking process is involved. Head tracking can be done using different tracking techniques such as Optical tracking, Magnetic tracking or Inertial tracking. In this paper, a six-degrees-of-freedom (6-DoF) optical tracker (TrackIR^TM) was used to record the coordinates of the pilot’s head motion in real time on the simulator bed. During the process of acquisition of the coordinates of head movement by optical tracker, the data may get missed due to stray light interference or any other kind of occlusion. To predict the missing data Self Healing Neural Model (SHNM) was applied. More than 88% of accuracy was achieved in prediction of three different sets of missing data. Results were also compared with Back Propagation Neural Network (BPNN).