A new ΔT metric evaluation for target and background under various statistics

To generate images to assess the detection performance of thermal imaging systems and countermeasured platform signatures, a method to characterize the background and target is required so that their signatures can be statistically matched. Current methods use an area-weighted average temperature difference (AWAΔT), which is regarded as inadequate in representing observer´s sensitivity to the inherent detection cues of the target/background/clutter signatures. In an effort to identify a more robust and accurate ΔT metric definition for statistical background and target matching, Battelle developed a new ΔT metric definition and equation using RMS pixel-based higher order statistics for the background and target signature pixel data in a scene image. This new ΔT metric is evaluated using images under various target and background statistics, for example, combinations of exponential, Gaussian, log-normal, and Rice distributions. The metric is also compared to other metrics. This new ΔT metric provides a better estimate of true signature difference between the background/clutter and target, enabling more accurate matching of the background/clutter and target for use in sensor detection performance assessment