The predicted Minimum Resolvable Temperature (MRT) values from five MRT models are compared to the measured MRT values for eighteen long-wave thermal imaging systems. The most accurate model, which is based upon the output of NVTherm IP, has an advantage over the other candidate models because it accounts for performance degradations due to blur and bar sampling. Models based upon the FLIR 92 model tended to predict overly optimistic values for all frequencies. The earliest models for MRT's for staring arrays did not incorporate advanced eye effects and had the tendency to provide pessimistic estimates as the frequency approached the Nyquist limit. MRT MODELING BACKGROUNDTo an individual who is new to the field of thermal modeling, it seems counter-intuitive to think that there would be a desire to model an MRT based upon sensor measurables or sensor design parameters. The short answer to this conundrum is that the original prediction model for thermal performance was based upon the two-dimensional MRT, which is the geometric mean (carried out un-conveniently along the temperature axis and not the frequency axis) of the measured horizontal and vertical MRTs. Because it was necessary to have an accurate 2-dimensional MRT along several frequencies in order to have a thorough thermal range prediction, it was not uncommon for lab observers to perform four trial runs at eight or more frenquencies. It quickly became apparent that rather than directly measure the MRTs it would be more convenient to simply model the predicted MRT based upon various sensor parameters.Also, during the period when 1 st generation and 2 nd generation scanning and scanning sampled systems were the dominant thermal programs, there was usually not a means of digitizing the signal from the detector array. With the exception of systems containing an EO-MUX display configuration the main source of data from a thermal system were experiments that incorporated human in the loop testing. So, because digital data was not generally available and large scale tests involving man in the loop evaluation of sensor performance were prohibitively expensive, MRT measurements were a reasonable basis for validating the performance of a thermal imaging system.Recently, there have not been many new theories on predicting MRTs for staring arrays. Part of the reasoning for this might have to do with the fact that with the introduction of the Target Task Performance (TTP) Metric, 1 the MRT measurement is no longer directly implemented in the computation of range performance for thermal imaging systems. WHY MODEL A PREDICTED MRT AFTER THE TTP METRIC?Ideally, it makes sense for any thermal EO system to be qualified based upon a large number of standard tests (i.e. 3D Noise, MTF, Uniformity, etc.). Unfortunately in some cases system engineers / integrators do not have the equipment or expertise needed to perform the measurements listed above. In other cases thermal EO systems do not even have an output that can be digitized to perform any measurement. Especially in the ...

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Predicting an accurate Minimum Resolvable Temperature Difference (MRTD) for a thermal imaging system is often hindered by inaccurate measurements of system gain and display characteristics. Variations in these terms are often blamed for poor agreement between model predictions and measured MRTD. By averaging over repeated human measurements, and carefully recording all system parameters affecting image quality, it should be possible to make an accurate prediction of MRTD performance for any resolvable frequency. Utilizing the latest NVESD performance models with updates for noise, apparent target angle, and human vision, predicted MRT are compared with measured curves. We present results for one well characterized mid-wave thermal staring system. *This document is approved for public release; distribution is unlimited. BACKGROUNDThe Minimum Resolvable Temperature Difference (MRTD or MRT) [1],[2] is a laboratory test that had performance implications for 1st and 2nd generation FLIRs (scanned systems). The MRT is considered to be a visual acuity evaluation of sensor performance, MRT is measurable in the laboratory, and can be related to field performance. Embedded in test results are a characterization of the thermal sensitivity and resolution of the sensor and the ability of a human observer to use the sensor to discriminate objects.It was once extremely important to model and measure the MRT performance for a given sensor. The utility of this concept was that the detection, recognition and identification criteria for a thermal task were related to the spatial frequency of a just visible four bar pattern (comparable to Johnson's criteria of cycles on target) as predicted either by theory or measured in the laboratory. The MRT equation, in the 1975 NVL Static Performance Model and its successors FLIR90/92 and NVTHERM, predicts ∆T for the recognition of 4-bar patterns as a function of spatial frequency. The modeled MRT equation contains sensor, target, and observer characteristics. These include detector noise, sensor component Modulation Transfer Functions (MTFs), processing electronics, display characteristics, and observer eye/brain models.Due to the sampling limitations of undersampled imaging systems, along with a myriad of other sensor effects that were not encapsulated in the MRT equation (e.g., digital effects, sensor boost, LACE, LAP, super-resolution), a new model needed to be developed that was no longer tied to a MRT equation. [3], [4] The new models NVThermIP and NV-IPM can utilize laboratory measurements as inputs, but they rely only on objective measurements such as Modulation Transfer Function and 3D Noise to determine thermal performance predictions.Still, the laboratory MRT measurement has utility in determining possible system level degradations, not easily revealed through objective laboratory measurements. These could include platform vibration, non-optimized displays, eyepiece effects, data resampling, human viewing factors, and other degradations not included in the performance models. If all ...