基于图特征学习的海杂波抑制算法  

Graph Feature Learning-Based Sea Clutter Suppression Method

作　　者：陈鹏[1] 许震 曹振新 王宗新 CHEN Peng;XU Zhen;CAO Zhenxin;WANG Zongxin(State Key Laboratory of Millimeter Waves,School of Information Science and Engineering,Southeast University,Nanjing 210096,Jiangsu,China)

机构地区：[1]东南大学信息科学与工程学院毫米波国家重点实验室,江苏南京210096

出　　处：《兵工学报》2023年第2期534-544,共11页Acta Armamentarii

基　　金：江苏省优秀青年科学基金项目(BK20220128);国家重点研发计划项目(2019YFE0120700);中国航天科技集团公司第八研究院产学研合作基金资助项目(SAST2021-039);国家自然科学基金项目(61801112)。

摘　　要：为有效降低海杂波对海洋雷达的工作影响,研究海杂波的抑制问题,提出一种基于图特征学习的海杂波抑制算法。使用时频变换对雷达回波信号进行维度扩增,基于图嵌入处理深度挖掘图结构特征的思想,并依据海杂波和目标回波信号在时频谱中的不同结构特性,给出一种通过图嵌入进行信号节点特征向量构造的方法。区别于传统时域对消和子空间分解等方法,该方法可以通过时频谱中不同信号的节点分类实现海杂波的抑制。仿真与实测结果表明,该算法可以有效抑制雷达回波信号中的海杂波分量,提升雷达回波信号的信杂比,为海洋雷达进行海杂波的抑制提供了新的思路和途径。In order to reduce the effect of sea clutter on Marine radar, a sea clutter suppression algorithm based on graph feature learning is proposed. The time-frequency transform is used to amplify the dimension of radar echo signal, and based on the idea that the graph structure features can be deeply mined by graph embedding processing, a method of constructing signal node feature vector by graph embedding is presented according to the different structural characteristics of sea clutter and target echo signal in time spectrum. Different from traditional methods such as time domain cancellation and subspace decomposition, this method can be used to implement sea clutter suppression through node classification of different signals in the time spectrum. Simulation and experimental results show that the algorithm can effectively suppress the sea clutter component of radar echo signal, improve the signal to clutter ratio of radar echo signal, and provide a new idea and way for ocean radar to suppress sea clutter.

关 键 词：海杂波抑制 时频变换 图嵌入 图结构 节点分类 

分 类 号：TN957[电子电信—信号与信息处理]