西安电子科技大学学报（自然科学版）2023，Vol.50Issue(6)：62-74,13.DOI:10.19665/j.issn1001-2400.20231005
面向雷达行为识别的多尺度卷积注意力网络
Multi-scale convolutional attention network for radar behavior recognition
摘要
Abstract
A radar behavior mode recognition framework is proposed aiming at the problems of difficult feature extraction and low recognition stability of the radar signal under a low signal-to-noise ratio,which is based on depth-wise convolution,multi-scale convolution and the self-attention mechanism.It improves the recognition ability in complex environment without increasing the difficulty of training.This algorithm employs depth-wise convolution to segregate weakly correlated channels in the shallow network.Subsequently,it utilizes multi-scale convolution to replace conventional convolution for multi-dimensional feature extraction.Finally,it employs a self-attention mechanism to adjust and optimize the weights of different feature maps,thus suppressing the influence of low and negative correlations in both channels and the spatial domains.Comparative experiments demonstrate that the proposed MSCANet achieves an average recognition rate of 92.25%under conditions of 0～50%missing pulses and false pulses.Compared to baseline networks such as AlexNet,ConvNet,ResNet,and VGGNet,the accuracy has been improved by 5%to 20%.The model exhibits stable recognition of various radar patterns and demonstrates enhanced generalization and robustness.Simultaneously,ablation experiments confirm the effectiveness of deep grouped convolution,multi-scale convolution,and the self-attention mechanism for radar behavior recognition.关键词
深度学习/机器学习/模式识别/深度分组卷积/多尺度卷积/自注意力机制Key words
deep learning/machine learning/mode recognition/depth-wise convolution/multiscale convolution/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
熊敬伟,潘继飞,毕大平,杜明洋..面向雷达行为识别的多尺度卷积注意力网络[J].西安电子科技大学学报（自然科学版）,2023,50(6):62-74,13.基金项目
国家自然科学基金(62071476) （62071476）
