A multiple hypotheses testing approach to radar detection and pre-classification

M. Greco, F. Gini, A. Farina
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引用次数: 2

Abstract

This work presents a single-scan-processing approach to the problem of detecting and pre-classifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their covariance matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probabilities of false alarm, detection and classification. The extra signal-to-noise power ratio necessary to preclassify a target once a detection has occurred is also derived.
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雷达探测与预分类的多假设检验方法
这项工作提出了一种单扫描处理方法来检测和预分类可能属于不同目标类别的雷达目标。该方法基于最大后验(MAP)和Neyman-Pearson (NP)准则的混合,保证了期望的恒定虚警率(CFAR)行为。将目标建模为均值为零的子空间随机信号,并给出协方差矩阵。根据不同的信号子空间来区分不同的目标类,这些子空间由它们的协方差矩阵指定。通过数值分析和蒙特卡罗仿真,从虚警概率、检测概率和分类概率等方面研究了该方法的性能。在检测发生后,还推导了对目标进行预分类所需的额外信噪比。
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