基于特征融合的调制识别增强与迁移演化

针对调制识别中单一图像的特征信息不足，区分度不够高，识别范围受限的问题。本文提出了一种基于时频图和星座图特征融合的调制识别特征增强方法，利用深度学习神经网络提取信号图像的特征，构建特征空间，通过多维特征融合，挖掘和整合不同特征的优势，增强模型算法的鲁棒性。此外运用了模型迁移的方法，仅需对分类器进行训练，大幅节约了训练时间和资源，具有很强的实时性和实用性。仿真结果显示，在0db左右的条件下，相比于单一特征图像，采用特征融合增强的方法能将信号的平均识别率提高约25%，通过模型迁移，省去了卷积神经网络的训练，所需的训练时间约为迁移前的9.6%，消耗内存约为迁移前的7.3%，同时模型的识别率损失控制在了5%以内。

Modulation recognition enhancement and migration evolution based on feature fusion

In modulation recognition, the feature information of a single image is insufficient, the degree of discrimination is not high enough, and the recognition range is limited. In this paper, a modulation recognition feature enhancement method based on the feature fusion of time-frequency map and constellation map is proposed. The deep learning neural network is used to extract the features of signal image and construct the feature space. Through multi-dimensional feature fusion, the advantages of different features are mined and integrated to enhance the robustness of the model algorithm. In addition, the method of model migration is used, which only needs to train the classifier, which greatly saves the training time and resources, and has strong real-time and practicability. The simulation results show that under the condition of about 0 dB, compared with a single feature image, the average recognition rate of the signal can be improved by about 25% by using the feature fusion enhancement method. Through the model migration, the training of convolutional neural network is omitted, and the training time required is about 10% of that before migration, and the memory consumption is about 7.3% of that before migration. At the same time, the loss of recognition rate of the model is controlled within 5%.