A demonstration of the confuser and likelihood modeling benefits for target detection in SAR imagery (Invited Paper)

A common approach to the detection of objects in sensor data is to model the target, compare the input data to that model and then if the match is close enough, declare target-present. This is how many automatic target recognition (ATR) systems operate. An alternative approach is to also have confuser models (CMs) and to consider how close the input data is to all of the models in the library. The advantages of CMs can be increased by also modeling the match score likelihoods for targets and confusers. This paper considers several methods for using CMs and likelihood models (LMs) and demonstrates their relative merits with a mean-squared-error based ATR on the MSTAR synthetic-aperture-radar (SAR) public data set. Two benefits of CMs and LMs are demonstrated. They improve the ability of the ATR to discriminate targets and confusers, as one might expect, but they can also help the ATR estimate the confidence it should have in its decisions. In the demonstration, the area-under-the-ROC curve was increased from 0.88 to 0.94 by CM use. For the important case of out-of-library confusers, if the probability of false alarm (Pfa) is set to 0.1 then CMS and LMs increase probability of detection (Pd) from 0.40 to 0.65. On the other hand if the Pd is set to 0.9 then the CMs and LMs decrease Pfa from 0.50 to 0.35. The posterior estimate (i.e., the ATR's confidence) had a reduction in RMS error from 0.27 to 0.09 through the use of CMs and LMs.