Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey
This survey helps military intelligence researchers by organizing SAR ATR methods and identifying gaps, but it is incremental as it synthesizes existing work without new results.
This paper surveys current Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) architectures using the MSTAR dataset, proposing a taxonomy and comparing method strengths/weaknesses under standard and extended conditions while highlighting dataset limitations.
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.