摘要
针对传统海面漂浮小目标的特征检测方法难以有效提取目标特征的问题,提出了一种基于RCMDE-XGBoost海面小目标检测方法。利用变分模态分解对信号进行去噪预处理,通过精细复合多尺度散布熵提取目标的多尺度特征,构建多维度特征矩阵,输入XGBoost网络进行特征分类,通过模型训练,实现海面小目标检测。利用IPIX雷达实测数据库,在#54、#311、#320海情HV极化方式下检测率分别达到了93.33%、92.38%、95%,相较于图连通密度检测法平均提升12%,证明了RCMDE-XGBoost检测方法有效。
Aiming at the problem that the traditional floating small target feature detection method is difficult to extract the target feature effectively,this paper analyzes the feature of small target on the sea surface,and studies the principle of fine composite multi-scale dispersion entropy(RCMDE).A small target detection method based on RCMDE-XGBoost is proposed.The signal was de-noised by using variational mode decomposition,the multi-scale features of the target were extracted by fine composite multi-scale dispersion entropy,the multi-dimensional feature matrix was constructed and input into XGBoost network for feature classification,and the small target detection on the sea surface was realized through model training.Using the IPIX radar measurement database,the detection rate of#54,#311,#320 HV polarization mode reaches 93.33%,92.38%,95%respectively,which is 12%higher than the graph connected density detection method on average,proving the effectiveness of RCMDE-XGBoost detection method.
作者
王海峰
行鸿彦
陈梦
赵迪
李瑾
Wang Haifeng;Xing Hongyan;Chen Meng;Zhao Di;Li Jin(Jiangsu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044,China;Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第1期12-20,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(62171228)
国家重点研发计划(2021YFE0105500)项目资助
作者简介
王海峰,2020年于南京信息工程大学获得学士学位,现为南京信息工程大学研究生,主要研究方向为微弱信号检测。E-mail:wanghf1997@qq.com;通信作者:行鸿彦,1983年于太原理工大学获得学士学位,1990年于吉林大学获得硕士学位,2003年于西安交通大学获得博士学位,现为南京信息工程大学教授、博士生导师,主要研究方向为气象仪器设计与计量、信号检测与处理等。E-mail:xinghy@nuist.edu.cn;陈梦,2020年于淮阴师范学院获得学士学位,现为南京信息工程大学研究生,主要研究方向为时间延迟估计、信号处理。E-mail:2630255937@qq.com
