基于特征融合的Fast-CNN的复杂波形调制识别

打开文本图片集

中图分类号：TN974 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.07.10

Abstract： Aiming at the complex electromagnetic environment， the radar signals are complex in type and agile in form，which leads to the problem of lower signal recognition rate and higher arithmetic complexity，a complex waveform modulation recognition method based on fast-convolutional neural network （Fast-CNN）with feature fusion is proposed. Firstly，a larger teacher network （feature fusion network） and a smaler student network（Fast-CNN） are designed.The teacher network extracts and fuses the diferent scale features of the feature map to improve the network recognition rate. The student network removes the redundant channels by pruning method to solve the problem of larger computation. Then，the knowledge trained in the teacher network is transferred to the student network through knowledge distilation. Thus，the network can significantly reduce the amount of computation while maintaining the recognition accuracy. Experiments show that the overall recognition rate of the proposed method for 10 types of complex modulated waveforms reaches more than 99% （20 when the signal-to-noise ratio is greater than -3 dB.

Keywords ： complex waveform； modulation recognition; feature fusion; network pruning;knowledge distillation

0 引言

随着电子对抗技术迅速发展，干扰与抗干扰的对抗逐渐加剧，雷达信号样式逐渐增多，变化更加敏捷，给信号的调制识别带来严峻挑战[1-2]

传统方法方面，文献3-4通过构造不同似然函数实现信号的调制分类，但是这些方法存在计算量大、鲁棒性低、建模和先验知识要求高等缺点。（剩余17195字）