Squeeze excitation densely connected residual convolutional networks for specific emitter identification based on measured signals

Abstract

Specific emitter identification (SEI) refers to the process of distinguishing emitter individuals, which is important for electric support measure systems. Traditional SEI methods are based on hand-crafted features and have the problem of low accuracy. A novel convolutional neural network (CNN) approach, named squeeze excitation densely connected residual convolutional network (SEDCRN) approach, is therefore proposed. SEDCRN can directly recognize signals without complex preprocessing. It obtains the beneficial advantage of rediscovering new, more useful new features from low-level information and reducing feature redundancy by embedding the squeeze excitation module and residual connections into the densely connected convolutional structure. BesideIn addition, SEDCRN adopts center loss as an auxiliary loss function, which can further enhance the ability of feature learning and expression, especially for SEI tasks. The proposed approach is evaluated using a real automatic, dependent surveillance-broadcast signal dataset. Experiments show that SEDCRN outperforms traditional methods and other CNN-based methods in terms of accuracy and parameter efficiency.