Automated test methods have been developed and implemented which provide a high degree of correlation with average manual test results using human observers. The results of this effort are given in this paper using the presently implemented MRT models. New data using the FLIR92 3-D noise model are also presented which offer a more detailed description of sensor noise over previous implementations. This improves the accuracy of automated MRT and will facilitate testing of scanning time delay and integration (TDI) and staring array thermal imaging sensors in the future.
The automated minimum resolvable temperature (AMRT) test method for IR sensor performance testing is developing into an accepted method of automated test. This paper gives the basis of testing AMRT and shows how it relates to the manual measurement that has been correlated to field performance. The lessons learned as a result of implementing AMRT at Northrop are reported in this paper. These lessons are discussed in the areas of developing algorithms and methodologies which work well in the typical noise environment. Improved methods have been developed to give a better measure of signal response in the presence of many kinds of noise. Various types of noise measurements are addressed as well as their impact on the resulting AMRT.