  Automatic Target Recognition (ATR) for military applications is one of the
core processes towards enhancing intelligencer and autonomously operating
military platforms. Spurred by this and given that Synthetic Aperture Radar
(SAR) presents several advantages over its counterpart data domains, this paper
surveys and assesses current SAR ATR architectures that employ the most popular
dataset for the SAR domain, namely the Moving and Stationary Target Acquisition
and Recognition (MSTAR) dataset. Based on the current methodology trends, we
propose a taxonomy for the SAR ATR architectures, along with a direct
comparison of the strengths and weaknesses of each method under both standard
and extended operational conditions. Additionally, despite MSTAR being the
standard SAR ATR benchmarking dataset we also highlight its weaknesses and
suggest future research directions.
