用于调制识别的二维累积量特征统计模型
刘沛;水鹏朗;郭永明;李宁
【期刊名称】《西安电子科技大学学报（自然科学版）》 【年(卷),期】2014(000)002
【摘要】高阶累积量是一种用于数字调制方式识别的重要特征。笔者在高斯白噪声信道下，构造了一种用于识别线性数字调制方式的二维归一化四阶累积量特征，并推导出该特征近似服从高斯分布。为了验证该模型与特征样本服从的统计模型一致，根据贝叶斯准则在二维特征平面上构造最大似然分类器，并从理论上推导出二元调制方式识别问题的平均分类正确率，它与仿真实验得到的平均分类正确率吻合得很好，证明了该方法的正确性。%Higher order cumulants are the key features for implementing digital modulation classification. However,few available literatures focus on the statistical model of cumulant features.A two-dimensional normalized fourth-order cumulant feature is proposed to classify linear digital modulation in the additive white Gaussian noise channel,and then it is derived that the two-dimensional feature asymptotically obeys Gaussian distribution.In order to show the correctness of the proposition,a maximum likelihood classifier is formed in the two-dimensional feature domain according to the Bayesian criterion.The average probability of correct classification of the binary class problem is theoretically determined,which is consistent with the result obtained by simulations,thus justifying the correctness of the proposed theoretical results.
用于调制识别的二维累积量特征统计模型
