Articulation study for SAR ATR baseline algorithm

This study investigates how operating conditions (OCs) impact the performance of a synthetic aperture radar (SAR) automatic target recognition (ATR) algorithm. We characterize the performance of the algorithm as a function of OCs to understand the algorithm's strengths and weaknesses and guide further development. This paper examines the classification stage of a template method called Quantized Grayscale Matching (QGM). To thoroughly investigate this problem, asymptotic prediction code is used to generate synthetic data for both training and testing to answer several questions. How does articulation impact the performance of the algorithm? How much training data is needed to handle the articulation of the targets? Certain targets may need more training data than others, but why? Which articulation states present the biggest challenge and why? How to have synthetic results have similar characteristics as measured results? These answers will help guide algorithm development and provide a framework to explore other OCs.