四川大学学报（自然科学版）2024，Vol.61Issue(5)：137-146,10.DOI:10.19907/j.0490-6756.2024.053005
小样本条件下多功能雷达工作模式识别方法
Method for multi-functional radar operational mode recognition under small-sample conditions
摘要
Abstract
In increasingly complex electromagnetic environments,the recognition of multifunction radar oper-ating modes continues to face numerous challenges.In particular,the limited number of intercepted signal samples from multifunction radars,combined with poor sample augmentation quality,leads to low accuracy in operating mode recognition.This approach is driven by the synergy of Adaptive Padding TransGAN(Gen-erative Adversarial Network with Adaptive Padding)and Model-Agnostic Meta-Learning.Initially,The Adaptive Padding TransGAN is employed for adaptive sample padding and sample augmentation,starting with the context of fitting small-sample data.Subsequently,the Model-Agnostic Meta-Learning algorithm in meta-learning is integrated to achieve precise identification of multifunctional radar operational modes under limited sample conditions.Finally,compared to algorithms combining Generative Adversarial Networks with Meta-Learning and traditional Support Vector Machine classifiers,simulation results demonstrate that the proposed approach significantly enhances recognition accuracy by 2.39%and 17.42%,respectively.The ef-fectiveness of this method in accurately identifying multifunction radar operating modes under small sample conditions has been validated.关键词
多功能雷达/模式识别/小样本/数据增强/注意力机制/模型无关元学习Key words
Multi-function radar/Pattern recognition/Few-Shot/Data Augmentation/Attention Mecha-nism/Model-Agnostic Meta-Learning分类
信息技术与安全科学引用本文复制引用
戴子瑜,普运伟,杜林,何志强..小样本条件下多功能雷达工作模式识别方法[J].四川大学学报（自然科学版）,2024,61(5):137-146,10.基金项目
国家自然科学基金项目(61561028) （61561028）
