College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Sensors (Basel). 2022 Jul 1;22(13):4980. doi: 10.3390/s22134980.
With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information. In the paper, we focus on the MFR state recognition with actual intercepted MFR signals and develop it by introducing recurrent neural networks (RNNs) of deep learning into the modeling of MFR signals. According to the layered MFR signal architecture, we propose a novel end-to-end state recognition approach with two RNNs' connections. This approach makes full use of RNNs' ability to directly tackle corrupted data and automatically learn the features from input data. So, it is practical and less dependent on prior information. In addition, the hierarchical modeling method applied to the end-to-end network effectively restricts the scale of the end-to-end model so that the model can be trained with a small amount of data. Simulation results on a real MFR show the excellent recognition performance of our end-to-end approach with little prior information.
随着多功能雷达（MFR）的广泛应用，传统的雷达信号识别技术很难满足当前电子情报系统的需求。对于 MFR 的信号识别，不仅需要识别发射器的类型或个体，还需要识别其当前状态。现有方法通过分层建模来识别 MFR 状态，但大多数方法严重依赖先验信息。在本文中，我们专注于使用实际截获的 MFR 信号进行 MFR 状态识别，并通过将深度学习的递归神经网络（RNN）引入 MFR 信号建模来开发该方法。根据分层 MFR 信号结构，我们提出了一种新颖的端到端状态识别方法，该方法使用两个 RNN 的连接。该方法充分利用了 RNN 直接处理损坏数据的能力，并自动从输入数据中学习特征。因此，它是实用的，并且对先验信息的依赖性较小。此外，应用于端到端网络的分层建模方法有效地限制了端到端模型的规模，使得可以使用少量数据来训练模型。在真实 MFR 上的仿真结果表明，我们的端到端方法在使用少量先验信息的情况下具有出色的识别性能。
