基于多通道图卷积神经网络的地海杂波分类方法  

Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks

作　　者：李灿 王增福[1] 张效宣 潘泉[1] LI Can;WANG Zengfu;ZHANG Xiaoxuan;PAN Quan(School of Automation,Northwestern Polytechnical University,Xi’an 710129,China)

机构地区：[1]西北工业大学自动化学院,西安710129

出　　处：《雷达学报(中英文)》2025年第2期322-337,共16页Journal of Radars

基　　金：国家自然科学基金(62473317,U21B2008)。

摘　　要：地海杂波分类是提升天波超视距雷达目标定位精度的关键技术,其核心是判别距离多普勒(RD)图中每个方位-距离单元背景源自陆地或海洋的过程。基于传统深度学习的地海杂波分类方法需海量高质量且类别均衡的有标签样本,训练时间长,费效比高;此外,其输入为单个方位-距离单元杂波,未考虑样本的类内和类间信息,导致模型性能不佳。针对上述问题,该文通过分析相邻方位-距离单元之间的相关性,将地海杂波数据由欧氏空间转换为非欧氏空间中的图数据,引入样本之间的关系,并提出一种基于多通道图卷积神经网络(MC-GCN)的地海杂波分类方法。MC-GCN将图数据由单通道分解为多通道,每个通道只包含一种类型的边和一个权重矩阵,通过约束节点信息聚合的过程,能够有效缓解由异质性造成的节点属性误判。该文选取不同季节、不同时刻、不同探测区域RD图,依据雷达参数、数据特性和样本比例,构建包含12种不同场景的地海杂波原始数据集和36种不同配置的地海杂波稀缺数据集,并对MC-GCN的有效性进行验证。通过与最先进的地海杂波分类方法进行比较,该文所提出的MC-GCN在上述数据集中均表现最优,其分类准确率不低于92%。Land-sea clutter classification is essential for boosting the target positioning accuracy of skywave over-the-horizon radar.This classification process involves discriminating whether each azimuth-range cell in the Range-Doppler(RD)map is overland or sea.Traditional deep learning methods for this task require extensive,high-quality,and class-balanced labeled samples,leading to long training periods and high costs.In addition,these methods typically use a single azimuth-range cell clutter without considering intra-class and inter-class relationships,resulting in poor model performance.To address these challenges,this study analyzes the correlation between adjacent azimuth-range cells,and converts land-sea clutter data from Euclidean space into graph data in non-Euclidean space,thereby incorporating sample relationships.We propose a Multi-Channel Graph Convolutional Networks(MC-GCN)for land-sea clutter classification.MC-GCN decomposes graph data from a single channel into multiple channels,each containing a single type of edge and a weight matrix.This approach restricts node information aggregation,effectively reducing node attribute misjudgment caused by data heterogeneity.For validation,RD maps from various seasons,times,and detection areas were selected.Based on radar parameters,data characteristics,and sample proportions,we construct a land-sea clutter original dataset containing 12 different scenes and a land-sea clutter scarce dataset containing 36 different configurations.The effectiveness of MC-GCN is confirmed,with the approach outperforming state-ofthe-art classification methods with a classification accuracy of at least 92%.

关 键 词：天波超视距雷达 地海杂波分类 图数据 图卷积神经网络 异质性 

分 类 号：TN958.93[电子电信—信号与信息处理]