天波雷达干扰检测的RD图分类器设计与融合训练  

Image Classifier Design and Fusion Training for Interference Detection in Sky-wave OTH Radar

作　　者：高天翱 罗忠涛 郑圆圆 张安安 GAO Tianao;LUO Zhongtao;ZHENG Yuanyuan;ZHANG Anan(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Nanjing Research Institute of Electronics Technology,Nanjing Jiangsu 210039,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]南京电子技术研究所,江苏南京210039

出　　处：《现代雷达》2025年第1期8-14,共7页Modern Radar

摘　　要：针对天波超视距雷达的射频干扰和瞬态干扰,将干扰检测问题转化为距离-多普勒(RD)图像分类问题,研究RD图像分类器设计并评估其性能,提出多分类器决策融合与半监督自训练方法。RD图分类器设计包括RD图库建设、纹理特征提取和分类算法设计三步。基于不同分类算法设计多种基本分类器,以仿真图库为训练集,对实测数据强干扰的识别率高达95%,但对弱干扰检测性能不佳。为此,提出基于不同特征视图和学习方法的子分类器组合,给出了多分类器的决策融合和半监督自训练算法。实测图库验证表明,多分类器半监督自训练方法能够有效提高RD图像识别率,将弱干扰检测准确率由低于65%提升至80%以上。Considering the radio frequency interference and transient interference in sky-wave over-the-horizon(OTH)radar,the problem of interference detection is converted into range-Doppler(RD)image classification.RD image classifier design and classification performance is discussed.Multi-classifier decision fusion and semi-supervised self-training methods are proposed.Procedure of RD classifier design consisting of RD dataset construction,textual feature extraction,and classification algorithms are introduced.Based on different classification algorithms,various basic classifiers are designed by using the simulated dataset as training set,which obtains accuracy over 95%on strong interference detection but performs poorly on weak interference detection.Hence,ensemble multiple classifiers of different feature views or learning methods are proposed.Besides,algorithms of decision fusion and semi-supervised self-training are proposed.Experiments results on real dataset show that the self-training method based on multi-classifier fusion can improve the recognition accuracy of RD images effectively and increases the detection accuracy of weak interference from below 65%to over 80%.

关 键 词：天波超视距雷达 干扰检测 RD图像分类 决策融合 半监督自训练 

分 类 号：TN958.93[电子电信—信号与信息处理]