现代雷达2025，Vol.47Issue(1)：8-14,7.DOI:10.16592/j.cnki.1004-7859.2025.01.002
天波雷达干扰检测的RD图分类器设计与融合训练
RD Image Classifier Design and Fusion Training for Interference Detection in Sky-wave OTH Radar
高天翱 1罗忠涛 1郑圆圆 2张安安1
作者信息
- 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
- 折叠
摘要
Abstract
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 classi-fication performance is discussed.Multi-classifier decision fusion and semi-supervised self-training methods are proposed.Proce-dure of RD classifier design consisting of RD dataset construction,textual feature extraction,and classification algorithms are intro-duced.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 inter-ference 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Key words
sky-wave over-the-horizon radar/interference detection/range-Doppler image classification/decision fusion/semi-su-pervised self-training分类
信息技术与安全科学引用本文复制引用
高天翱,罗忠涛,郑圆圆,张安安..天波雷达干扰检测的RD图分类器设计与融合训练[J].现代雷达,2025,47(1):8-14,7.
