College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China.
Sensors (Basel). 2020 Jul 31;20(15):4270. doi: 10.3390/s20154270.
It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.
很难获得非合作短波电台发送的许多已标记的链路建立 (LE) 行为信号。我们提出了一种新颖的一维辅助分类器生成对抗网络 (ACGAN) 来获取更多信号，然后使用一维 DenseNet 识别 LE 行为。首先，从许多真实信号中随机选择几个真实样本作为一维 ACGAN 的训练集。然后，通过将真实样本与经过训练的 ACGAN 生成的虚假样本组合，形成新的训练集。此外，提出了一维卷积自动编码器来描述这些生成样本的可靠性。最后，无需使用通信协议标准，通过使用新的训练集训练一维 DenseNet，就可以识别不同的 LE 行为。实验结果表明，一维 ACGAN 有效地扩充了训练集，从而提高了识别算法的性能。当原始训练样本数分别为 400、700、1000 和 1300 时，一维 ACGAN+DenseNet 的识别准确率分别比一维 DenseNet 高 1.92%、6.16%、4.63%和 3.06%。
