Specific emitter identification (SEI) is an important problem in the field of electronic intelligence. There are two major limitations in most existing SEI methods: First, the features should be artificially extracted, which requires specialized expertise; Second, various features are not merged effectively to improve performance. In this paper, an automatic multi-feature extraction and fusion method based on deep ensemble learning is proposed for SEI. This method extracts and fuses multiple features via data-driven strategies using convolutional neural networks (CNN). No additional constraints are required on the type and number of the features to be fused. Three typical characteristics of radar pulse signals, including amplitude, phase and spectrum asymmetry, are taken as example features for method description and performance verification. Experiment results demonstrate that, the proposed method performs well in automatic feature extraction, and it can significantly improve the SEI performance via multi-feature fusion.

特定的发射器标识（SEI）是电子情报领域的重要问题。在大多数现有的SEI方法中，存在两个主要局限性：首先，应人为提取特征，这需要专业知识。其次，各种功能没有有效地合并以提高性能。提出了一种基于深度集成学习的自动多特征提取与融合方法。该方法使用卷积神经网络（CNN）通过数据驱动策略提取和融合多个特征。对于要融合的特征的类型和数量，不需要其他约束。雷达脉冲信号的三个典型特征，包括幅度，相位和频谱不对称性，被作为示例特征进行方法描述和性能验证。