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Moving and stationary target acquisition and recognition (MSTAR) model-based automatic target recognition: search technology for a robust ATR

机译:基于移动和静止目标获取和识别(MSTAR)模型的自动目标识别:强大的ATR的搜索技术

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Abstract: DARPA/Air Force Research Laboratory Moving and Stationary Target Acquisition and Recognition (MSTAR) program is developing state-of-the-art model based vision approach to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The model-based approach requires using off-line developed target models in an on-line hypothesize-and-test manner to compare predicted target signatures with image data and output target reports. Central to this model-based ATR is the PEMS (Predict-Extract-Match-Search) subsystem. The Search module is critical to PEMS by providing intelligent control to traverse the hypothesis feature space. A major MSTAR goal is to demonstrate robust ATR for variations in targets including partially hidden targets. This paper will provide an update on the technology being developed under MSTAR and the status of this model based ATR research, specifically concentrating on the Search Module.!1
机译:摘要:DARPA /空军研究实验室移动和固定目标获取与识别(MSTAR)计划正在开发基于模型的合成孔径雷达(SAR)自动目标识别(ATR)视觉技术。基于模型的方法要求以离线假设和测试的方式使用离线开发的目标模型,以将预测的目标特征与图像数据和输出目标报告进行比较。基于模型的ATR的核心是PEMS(预测-提取-匹配-搜索)子系统。搜索模块通过提供智能控制以遍历假设特征空间,对于PEMS至关重要。 MSTAR的主要目标是展示针对目标(包括部分隐藏目标)变化的强大ATR。本文将提供有关MSTAR下正在开发的技术的最新信息,以及基于ATR研究的该模型的现状,特别是搜索模块。!1

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