Representing the observer in electro-optical target acquisition models

Electro-optical target acquisition models predict the probability that a human observer recognizes or identifies a target. To accurately model targeting performance, the impact of imager blur and noise on human vision must be quantified. In the most widely used target acquisition models, human vision is treated as a "black box" that is characterized by its signal transfer response and detection thresholds. This paper describes an engineering model of observer vision. Characteristics of the observer model are compared to psychophysical data. This paper also describes how to integrate the observer model into both reflected light and thermal sensor models.