Advances in communication technology have enabled signal modulation classification (SMC) to be widely used in noncooperative identification situations, such as spectrum detection, electronic countermeasures, and target identification. In the face of complex electromagnetic environments and various classification tasks, the class imbalance phenomenon in modulated signal data sets has become a problem that cannot be ignored. For the SMC based on machine learning, the unbalanced training data set will cause the actual decision boundary to shift, thereby reducing the prediction accuracy of minority signals. And for SMC based on deep learning, unbalanced data will lead to distortion of the feature space and affect the extraction of discriminative features. However, the existing modulation classification methods cannot effectively deal with the imbalance problem. This study introduces an oversampling method tailored for modulation signals. Our method balances the data set by synthesizing new samples according to the distribution of signal samples and the distance between samples, which will effectively reduce the impact of the imbalance problem on the classifier. For modulated signals, experimental results show that our method performs better than other oversampling methods. In addition to the SMC task, we test the performance of the proposed method for individual identification of radiation sources on the aircraft communications addressing and reporting system data set. Compared with other comparison methods, our method improves the classification performance the most.

中文翻译：

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基于过采样的余弦距离和分布不平衡信号调制分类


通信技术的进步使信号调制分类 （SMC） 能够广泛用于非合作识别情况，例如频谱检测、电子对抗和目标识别。面对复杂的电磁环境和各种分类任务，调制信号数据集中的类不平衡现象已成为一个不容忽视的问题。对于基于机器学习的 SMC，不平衡的训练数据集会导致实际决策边界偏移，从而降低少数信号的预测精度。而对于基于深度学习的 SMC，不平衡的数据会导致特征空间的失真，影响判别性特征的提取。然而，现有的调制分类方法无法有效处理不平衡问题。本研究介绍了一种为调制信号量身定制的过采样方法。我们的方法根据信号样本的分布和样本之间的距离，合成新的样本来平衡数据集，这将有效减少不平衡问题对分类器的影响。对于调制信号，实验结果表明，我们的方法比其他过采样方法性能更好。除了 SMC 任务外，我们还在飞机通信寻址和报告系统数据集上测试了拟议的辐射源个体识别方法的性能。与其他比较方法相比，我们的方法对分类性能的提升最大。