Combat identification using an augmented reality learning system

Abstract

This research will examine training with an augmented reality learning system to identify combat vehicles. Due to the increase in use of unmanned vehicles (UVs) for missions, a question arises: How do we best train operators to perform well when presented with a combat identification task. More specifically: (a) Is training using canonical (front and side) views sufficient? (b) Due to UAV perspective surveillance, are non-canonical/birds eye views necessary for optimal combat identification performance? (c) Would training with either perspective yield sufficient performance?