基于脉间特征深度学习的雷达辐射源识别

多功能雷达在复杂程序调度下，发射信号参数呈现取值范围宽、捷变速度快、变化随机性强等特点，非合作接收方难以对其建立有效的信号模型，给电子侦察系统的雷达辐射源识别带来严峻挑战。本文提出一种基于深度学习的复杂体制雷达辐射源识别方法，利用大样本全脉冲数据形成脉间参数变化的图像特征表示，从宏观上揭示雷达辐射源隐含的波形设计机理，并设计了基于AlexNet网络的图像特征深度学习网络开展辐射源识别，实测数据实验表明了本文的方法对一定时间跨度内的有限部同型多功能雷达具有良好的识别性能，为多功能雷达辐射源智能个体识别提供了新的解决思路。 

Radar Emitter Identification Based on Deep Learning of Inter-pulse Features

Under the complex program scheduling, the multi-function radar has the characteristics of wide value range, fast agility, and strong randomness. It is difficult for non-cooperative receivers to establish an effective model of the signal, which brings serious challenges to the radar radiation source identification of electronic reconnaissance systems. This paper proposed a complex system radar emitter identification method based on deep learning, which used full pulse data of a large sample to form an image feature representation of pulse-to-pulse parameter changes, macroscopically revealed the waveform design mechanism implied by the radar radiation source, and designed a deep learning network of image features based on AlexNet to carry out radiation sources identification. The measured data experiments show that the algorithm has good recognition performance for several multifunctional radars of the same type within a certain time span, which provides a new solution to the intelligent identification of multi-function radar emitter.