Sensor performance as a function of sampling (d) and optical blur (Fλ)

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. Keywords: TOD, TTP, Target Acquisition, range prediction, diffraction blur, detector siz