Abstract
Detector sampling and optical blur are two major factors affecting Target Acquisition (TA) performance with modern
EO and IR systems. In order to quantify their relative significance, we simulated five realistic LWIR and MWIR sensors
from very under-sampled (detector pitch d >> diffraction blur Fλ) to well-sampled (Fλ >> d). Next, we measured their
TOD (Triangle Orientation Discrimination) sensor performance curve. The results show a region that is clearly detectorlimited,
a region that is clearly diffraction-limited, and a transition area. For a high contrast target, threshold size TFPA on
the sensor focal plane can mathematically be described with a simple linear expression: TFPA =1.5·d ·w(d/Fλ) + 0.95·
Fλ·w(Fλ/d), w being a steep weighting function between 0 and 1. Next, tacticle vehicle identification range predictions
with the TOD TA model and TTP (Targeting Task Performance) model where compared to measured ranges with
human observers. The TOD excellently predicts performance for both well-sampled and under-sampled sensors. While
earlier TTP versions (2001, 2005) showed a pronounced difference in the relative weight of sampling and blur to range,
the predictions with the newest (2008) TTP version that considers in-band aliasing are remarkably close to the TOD. In
conclusion, the TOD methodology now provides a solid laboratory sensor performance test, a Monte Carlo simulation
model to assess performance from sensor physics, a Target Acquisition range prediction model and a simple analytical
expression to quickly predict sensor performance as a function of sampling and blur. TTP approaches TOD with respect
to field performance prediction.
