Operating condition sampling strategies for evaluating ATR performance

Synthetic data is commonly used to assess the performance of Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems modeling the OC space in question. In this work we demonstrate that the use of an informed sampling technique compared to an uninformed sampling approach can efficiently assess the "OC gap" between train and test OC spaces as the gap narrows. To demonstrate the effectiveness of an informed sampling approach, SAR ATR experiments are conducted as a function of how representative the train distribution of OCs are compared to the test OC space given a variety of challenging OC scenarios. Algorithm performance is assessed over a series of experiments given discrepancies between azimuth and depression angle of the sensor.