A statistical radar clutter classifier

W. Stehwien, S. Haykin
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引用次数: 9

Abstract

An algorithm that successfully classifies radar clutter into one of several major categories, including bird, weather, and target classes, is described. Parametric Bayes classification is applied to a set of features derived from the reflection coefficients computed using the multisegment version of Burg's formula. These coefficients are then transformed and grouped to meet the requirements for multivariate Gaussian behavior. The addition of two amplitude-related features aids in distinguishing between point targets and distributed clutter. Average probabilities of correct classification of 70% to 90% have been found when testing the classifier on recorded radar data.<>
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统计雷达杂波分类器
描述了一种成功地将雷达杂波分类为几种主要类别之一的算法,包括鸟类,天气和目标类别。参数贝叶斯分类应用于一组特征,这些特征来自使用Burg公式的多段版本计算的反射系数。然后对这些系数进行变换和分组,以满足多元高斯行为的要求。增加两个振幅相关的特征有助于区分点目标和分布式杂波。在雷达记录数据上进行测试,发现分类器的平均正确分类概率为70% ~ 90%。
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