基于深层残差网络和三元组损失的雷达信号识别方法

针对分类网络难以有效扩展分类数量的问题,提出了一种基于深层残差网络和三元组损失的雷达信号识别方法。该方法首先将雷达信号作为深层残差网络的输入,通过一维卷积将雷达信号映射到128维欧几里得空间,得到信号的特征向量;然后利用三元组损失函数调整网络参数,使得同类信号之间特征向量的欧式距离减小而不同类别信号之间的距离增大;最后通过基于样本库的识别算法实现对信号的分类识别。实验结果表明,相较于传统的分类网络,该方法在保证识别准确率的同时使得模型能够对分类数量进行有效扩展。

Radar signal recognition method based on deep residual network and triplet loss

To solve the problem that the classification network is difficult to effectively expand the number of classifications, a radar signal recognition method based on deep residual network and triplet loss is proposed. This method firstly takes the radar signal as the input of the deep residual network, maps the radar signal to 128-dimensional Euclidean space through one-dimensional convolution, and obtains the signal's eigenvector; then uses the triplet loss function to adjust the network parameters so that the Euclidean distance of feature vectors between homogeneous signals decreases and the distance between different types of signals increases; finally, the classification of the signals is realized through a sample library-based recognition algorithm. Experimental results show that compared with traditional classification networks, this method ensures the accuracy of recognition while enabling the model to effectively expand the number of classifications.