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

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 T FPA on the sensor focal plane can mathematically be described with a simple linear expression: T FPA =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.

Visual acuity and contrast sensitivity with compressed motion video

“…In other studies, often a ratio around 1.5 is found 9,19 . The difference may be ascribed to two factors: 1) in this study a color camera with Bayer filter was used, and such a system may have a reduced acuity compared to monochrome systems, and 2) the conditions for the observer were not perfect due to straylight falling on the observer display.…”

“…In their newest version 37 , the aliased signal from undersampled imagers is treated as noise and the model now takes into account the effects of in-band aliasing. A first quantitative comparison 19 indicates that this newest TTP matches the TOD predictions quite closely!…”

“…Aside from the simulation model (section 2.1.2), a simple analytical expression was developed to quickly predict performance as a function of sampling and blur properties of the sensor 19 .…”