基于深度学习和集成学习的辐射源信号识别

随着电磁环境的日益复杂和雷达辐射源信号类型的逐渐增多,如何有效地识别雷达信号类型成为一个重要的问题。为解决这个问题,提出了一种基于深度学习和集成学习的辐射源信号识别框架。该框架由特征提取和分类器设计两部分组成。第一部分,将雷达信号变换到时频域,利用栈式降噪自编码模型学习时频图像的特征。深度模型的训练采用无监督预学习和有监督微调相结合。第二部分,构造一个集成不同支持向量机分类器的模型对雷达信号进行识别。利用8种不同的辐射源信号验证了提出模型的有效性,结果表明结合这两种机器学习的方法有助于提高辐射源信号的识别正确率。

Radar emitter signal recognition based on deep learning and ensemble learning

With the increasingly complex electromagnetic environment of communication, as well as the gradually increased radar signal types, how to effectively identify the types of radar signals is an important problem. To address this problem, a recognition framework based on deep learning and ensemble learning is proposed. This framework is composed of feature extraction and classifier design. In the first state, transform radar signals to time-frequency domain and learn the time-frequency picture feature by using stacked denoising autoencoders. A learning method of unsupervised pre-learning and supervise fine-tuning is used to train this deep model. In the second state, a model of ensemble multiple support vector machine classifier is created to recognize radar signals. Eight types of emitter signals are adopted in simulation experiment to validate the effectiveness of the proposed framework, and the results show that the joint model helps to obtain higher recognition accuracy.