基于舰载雷达小样本的低慢小目标识别方法  

Low-slow and small target recognition method with few shot learning based on shipborne radar

作　　者：李轲[1] 张建强[1] 李胜军 王沫然 LI Ke;ZHANG Jian-qiang;LI Sheng-jun;WANG Mo-ran(Electronic Engineering Institute,Naval University of Engineering,Wuhan 430033,China;Sichuan Jiuzhou Electric Group Co.,Ltd.,Mianyang 621000,China)

机构地区：[1]海军工程大学电子工程学院,湖北武汉430033 [2]四川九洲电器集团有限责任公司,四川绵阳621000

出　　处：《舰船科学技术》2023年第18期123-128,共6页Ship Science and Technology

基　　金：武器装备综合研究项目(2020103280)。

摘　　要：本文针对舰载雷达低、慢、小目标探测面临的小样本识别问题,提出一种基于小样本学习的低慢小目标分类识别方法。该方法将低慢小目标雷达回波数据转换到小波变换域,利用多头注意力机制和双向长短记忆人工神经网络相结合的方式,解决了小样本目标分类识别的问题。在低慢小目标雷达回波仿真数据集上,开展模型训练和算法验证,分析任务差异性与识别准确率的关系,实验结果表明该方法对典型低慢小目标识别精度可达90%以上,在雷达目标识别领域具有较好的应用价值。Aiming at a small number of sample recognition problems faced by low-slow and small target detection of shipborne radar,a low-slow and small target recognition method with few shot learning based on shipborne radar is pro-posed.The method converts the radar echo data of low-slow and small targets into the wavelet transform domain,and solves the problem of target recognition by using a combination of multi-headed attention mechanism and bi-directional long short-term memory artificial neural network.Model training and algorithm validation are carried out on the low-slow and small tar-get radar echo simulation dataset,and the relationship between task variability and recognition accuracy is analyzed.Experi-mental results show that the recognition accuracy of typical low-slow and small targets can reach more than 90%,which veri-fies the good performance of this method and shows promising application in the field of radar target recognition.

关 键 词：目标识别 小波变换 多头注意力机制 长短期记忆网络 小样本学习 

分 类 号：TN959.1-7[电子电信—信号与信息处理]