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A GMM-based Algorithm for Classification of Radar emitters

机译:基于GMM的雷达发射器分类算法

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A Gaussian Mixture Model(GMM)-based Algorithm for the Classification of Radar emitters in autonomous Electronic Support Measure Systems is described in this paper. We first build a Gaussian model for every radar emitter, and then use the Expectation-Maximization (EM) Algorithm to train the parameters of the model. Finally, we construct a classifier whose input is the parameters of pulse and whose output is the type of radar. Results of experiments and comparisons with Neural Network Algorithm (Fuzzy ARTMAP) demonstrate that the proposed algorithm is effective in the condition of low SNR.
机译:本文描述了用于自主电子支持测量系统中的雷达发射器分类的高斯混合模型(GMM)基础算法。我们首先为每个雷达发射器构建高斯模型,然后使用期望 - 最大化(EM)算法训练模型的参数。最后,我们构建一个分类器,其输入是脉冲的参数,其输出是雷达的类型。具有神经网络算法的实验和比较结果(模糊艺术图)证明所提出的算法在低SNR的条件下是有效的。

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