论文标题
Rogue发射器检测使用混合网络的自动编码器和深度度量学习网络
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning
论文作者
论文摘要
Rogue发射器检测（RED）是维护安全互联网应用程序的至关重要技术。现有的基于深度学习的红色方法是在友好环境下提出的。但是，这些方法在低信噪比（SNR）方案下执行不稳定。为了解决这个问题，我们提出了一种强大的红色方法，该方法是一个混合网络的网络，该网络是自动编码器和深度度量学习（DML）。具体而言，采用Denoising自动编码器来减轻噪声干扰，然后在低SNR下提高其鲁棒性，而DML在改善特征歧视方面起着重要作用。进行了几个典型的实验，以评估自动依赖性监视数据集和IEEE 802.11数据集上提出的红色方法，并将其与现有的红色方法进行比较。仿真结果表明，所提出的方法比现有方法具有更高的判别语义向量，可实现更好的红色性能和更高的噪声鲁棒性。
Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under the friendly environments. However, these methods perform unstable under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an IEEE 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.
