Combat Identification Using an Augmented Reality Learning System

Combat identification (CID) is a high priority task throughout modern warfare. However, due to the quantity of fratricide accidents, it is reasonable to question the reliability of current CID training methods. With perceptual limitations (i.e. the keyhole effect), and 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? (d) Would training soldiers on both perspectives return superior combat identification performance? This research will examine training with an augmented reality learning system (BuildAR Software) to identify combat vehicles (i.e., Main Battle Tanks, and Personal Carriers). Therefore, subjects will be trained to identify tanks and personal carriers using either canonical or birds eye non-canonical perspectives. Further, training effectiveness will be measured using a computer-based assessment at the end of the study.