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Non-cooperative identification of civil aircraft using a generalised mutual subspace method

机译:使用广义互子空间法的民航非合作识别

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摘要

The subspace-based methods are effectively applied to classify sets of feature vectors by modelling them as subspaces. However, their application to the field of non-cooperative target identification of flying aircraft is barely seen in the literature. In these methods, setting the subspace dimensionality is always an issue. Here, it is demonstrated that a modified mutual subspace method, which uses to set the importance of each subspace basis, is a promising classifier for identifying sets of range profiles coming from real in-flight targets with no need to set the subspace dimensionality in advance. The assembly of a recognition database is also a challenging task. In this study, this database comprises predicted range profiles coming from electromagnetic simulations. Even though the predicted and actual profiles differ, the high recognition rates achieved reveal that the algorithm might be a good candidate for its application in an operational target recognition system.
机译:通过将子向量建模为子空间,基于子空间的方法可有效地应用于对特征向量集进行分类。然而,它们在飞行器非合作目标识别领域的应用在文献中几乎看不到。在这些方法中,设置子空间维数始终是一个问题。在这里,证明了一种改进的相互子空间方法,该方法用于设置每个子空间基础的重要性,是一种有前途的分类器,用于识别来自真实飞行目标的范围轮廓集,而无需事先设置子空间维数。识别数据库的组装也是一项艰巨的任务。在这项研究中,该数据库包含来自电磁仿真的预测范围轮廓。即使预测的配置文件和实际的配置文件有所不同,所实现的高识别率也表明该算法可能是其在操作目标识别系统中应用的良好候选者。

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