论文标题
合成孔径雷达图像的自动目标识别：调查
Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey
论文作者
论文摘要
用于军事应用的自动目标识别（ATR）是增强情报人员并自动操作军事平台的核心过程之一。本文刺激了合成孔径雷达（SAR）在其对应数据域中提出了几个优点，因此本文调查并评估了当前采用SAR域数据集的SAR ATR体系结构，即移动和固定的目标获取和识别（MSTAR）数据集。根据当前的方法论趋势，我们提出了SAR ATR架构的分类法，并直接比较在标准和扩展的操作条件下每种方法的优势和劣势。此外，尽管MSTAR是标准的SAR ATR基准数据集，但我们还是强调了其弱点，并建议未来的研究方向。
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.
