Highly advanced sensor technologies give our military commanders a significant command and control (C2) advantage over our enemies during conflicts, particularly with respect to situation awareness (SA). The use of advanced sensor technology models in synthetic battlespace gives war fighters parallel advantages. Two accepted simulation methodologies for analyzing the impact of sensor technologies are through Human-in-the-Loop (HITL) experiments, such as Joint Urban Operations (JUO), which utilize sensor capabilities to assist human participants during the experiments, and Monte Carlo Constructive (MCC) simulations, which can be used to model human performance. In HITL experiments using Joint Semi-Automated Forces (JSAF), participants describe their SA using Situation Awareness Objects (SAOs) which then can be reconstructed using Endsley's (1995) three levels of SA (perception, comprehension, and prediction). MCC experiments, which are dominated by algorithmically determined behaviors, can be used to model SA. Sensor measurements currently can be fused to perceive individual entities, but do not have the capability to recognize groupings of entities, resulting only in partial perceptual SA. Furthermore, current sensor data fusion models do not produce the second and third levels of SA, comprehension and prediction.

This paper will report research efforts to utilize both methodologies to expand the use of SAOs beyond player declarations to the automatic generation of SAOs. We develop a method to organize events drawn from scenarios taken from HITL experiments using SAOs in order to develop situation awareness algorithms for the MCC runs. These model-generated synthetic SAOs (SSAOs) can be compared to SAOs generated by human players to identify the accuracy of the models as well as be used to identify strengths and weaknesses in player performance.