Detection system performance analysis is frequently performed assuming Gaussian background statistics, often for convenience or due to a lack of better information. The Gaussian background assumption creates a relationship between probability of detection and probability of false-alarm (the receiver operating characteristic curve or ROC curve) as a function of signal-to-noise ratio. When the background distribution is non-Gaussian (e.g., with strong skew or excess kurtosis), analysis of detection system performance based on the estimated variance of the background signal under the assumption of Gaussianity will result in misleading estimates of detection and false alarm probabilities. In order to correctly define the ROC curve, the background statistics must be known. For infrared imaging systems, one example of a background which may be strongly non-Gaussian is the radiance field of a wavy sea-surface. Although the sea-surface slope field is assumed to be a Gaussian random field, the radiance field maps nonlinearly to the slope field, producing the phenomenon of sun glitter. The result is strongly non-Gaussian radiance distribution functions for certain sea-surface viewing conditions. Based on an analytical expression for sea surface radiance due to Ross, Potvin, and Dion (2005), we construct an approximate analytic expression for the distribution function of single-point (i.e., correlated neither in time nor space) sea-surface radiance as observed by a passive, square-law, electro-optical/infrared detector. With this distribution function, the relationship between detection and false-alarm probabilities can be more accurately characterized.
A core component to modeling visible and infrared sensor responses is the ability to faithfully recreate background noise and clutter in a synthetic image. Most tracking and detection algorithms use a combination of signal to noise or clutter to noise ratios to determine if a signature is of interest. A primary source of clutter is the background that defines the environment in which a target is placed. Over the past few years, the Electro-Optical Systems Laboratory (EOSL) at the Georgia Tech Research Institute has made significant improvements to its in house simulation framework GTSIMS. First, we have expanded our terrain models to include the effects of terrain orientation on emission and reflection. Second, we have included the ability to model dynamic reflections with full BRDF support. Third, we have added the ability to render physically accurate cirrus clouds. And finally, we have updated the overall rendering procedure to reduce the time necessary to generate a single frame by taking advantage of hardware acceleration. Here, we present the updates to GTSIMS to better predict clutter and noise doe to non-uniform backgrounds. Specifically, we show how the addition of clouds, terrain, and improved non-uniform sky rendering improve our ability to represent clutter during scene generation.