With the growing number of image-producing sensors in different spectral bands it is often desirable to provide the operational community with an estimation of the level of target acquisition/recognition performance that could be expected for specific scenarios using these sensors. Many target acquisition/recognition models have been developed over the decades to try and predict expected human performance under various conditions. Many of these are relatively complicated and often concentrate on specific aspects, such as search strategies, atmospherics, or sensor parameters while ignoring other factors. This paper describes the development of a simple, high-level target acquisition/recognition model for predicting human performance for a particular class of operationally relevant, time-based scenarios involving sensor-aided viewing. Assumptions and relevant factors considered in developing the model are discussed and the model, in different forms, is presented. Fundamentally, the model is based on previously-collected human visual performance data using images of the Landolt C acuity target recorded using a short-wave infrared sensor, the Johnny Johnson target recognition criteria, and basic scenario parameters. Limited real-world testing of the model has been accomplished.

Date Published: 23 May 2013
PDF: 12 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874510 (23 May 2013); doi: 10.1117/12.2017644

Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)