The Targeting Task Performance (TTP) Metric A New Model for Predicting Target Acquisition Performance

Vollmerhausen, Richard H.; Jacobs, Eddie L.

doi:10.21236/ada422493

Cited by 90 publications

(76 citation statements)

References 21 publications

(22 reference statements)

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“…Tactical vehicle ID data for well-sampled and under-sampled imagery are used from a series of TTP validation experiments published by Vollmerhausen et al (2004Vollmerhausen et al ( , 2008 10,11 . Details on the experiments and sensor specifications are reported in the referred papers.…”

Section: Experimental Datamentioning

confidence: 99%

“…On the basis of a number of experiments with well-sampled imagery, the authors of the TTP validation study 10 derived the target set specific parameters required for model predictions: V 50 = 20.8, target characteristic size = 3.1 m, and target characteristic contrast is high (sufficiently high to be resolution limited). With these parameters and the V 50 ↔ M 75 conversion derived for well-sampled imagers 13 The probability vs. range relationships predicted with the TOD TA model are shown in Figure 7 together with the data.…”

Section: Tod Range Predictionsmentioning

confidence: 99%

“…The predictions are excellent. Note that there are no free parameters: target set specific parameters were derived for well-sampled imagery in the original study 10 and V 50 was converted to M 75 for well-sampled imagery 13 . So TOD predictions match tactical vehicle ID data for both well-sampled and under-sampled imagery.…”

Section: Tod Range Predictionsmentioning

confidence: 99%

“…TA range performance was calculated for tacticle vehicle ID with two under-sampled imagers, using target set specific parameters that were derived with well-sampled imagery 10,11 . The results of the predictions, shown in Figure 7, are excellent, illustrating that the model (and the TOD) correctly weigh blur and sampling for these sensors.…”

Section: Tod and Ttp Range Predictionsmentioning

confidence: 99%

“…In this region, both sampling and blur contribute to the visual performance with the sensor system. Several promising models exist to predict TA performance with EO sensors: the TOD method and model 3,4,5,6 , TRM3 7,8 and the TTP metric 9,10,11 . They all claim to cope with well-sampled and under-sampled imagers, but they disagree in the relative importance of detector spacing and diffraction blur on the 'effective resolution' or VA with the sensor.…”

Section: Introductionmentioning

confidence: 99%

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

Bijl¹,

Hogervorst²

2009

SPIE Proceedings

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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.

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