Spatial frequency dependence of target signature for infrared performance modeling

Todd Du Bosq; Jeffrey Olson

doi:10.1117/12.885259

9 May 2011 Spatial frequency dependence of target signature for infrared performance modeling

Todd Du Bosq, Jeffrey Olson

Proceedings Volume 8014, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII; 80140M (2011) https://doi.org/10.1117/12.885259
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

The standard model used to describe the performance of infrared imagers is the U.S. Army imaging system target acquisition model, based on the targeting task performance metric. The model is characterized by the resolution and sensitivity of the sensor as well as the contrast and task difficulty of the target set. The contrast of the target is defined as a spatial average contrast. The model treats the contrast of the target set as spatially white, or constant, over the bandlimit of the sensor. Previous experiments have shown that this assumption is valid under normal conditions and typical target sets. However, outside of these conditions, the treatment of target signature can become the limiting factor affecting model performance accuracy. This paper examines target signature more carefully. The spatial frequency dependence of the standard U.S. Army RDECOM CERDEC Night Vision 12 and 8 tracked vehicle target sets is described. The results of human perception experiments are modeled and evaluated using both frequency dependent and independent target signature definitions. Finally the function of task difficulty and its relationship to a target set is discussed.

Citation Download Citation

Todd Du Bosq and Jeffrey Olson "Spatial frequency dependence of target signature for infrared performance modeling", Proc. SPIE 8014, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII, 80140M (9 May 2011); https://doi.org/10.1117/12.885259

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KEYWORDS

Performance modeling

Targeting Task Performance metric

Spatial frequencies

Sensors

Visual process modeling

Fourier transforms

Infrared signatures

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