TN911. 7
国家自然科学基金(62171228)、国家重点研发计划(2021YFE0105500)项目资助
Wang Haifeng
1. Jiangsu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & TechnologyXing Hongyan
1. Jiangsu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & TechnologyChen Meng
1. Jiangsu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & TechnologyZhao Di
1. Jiangsu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & TechnologyLi Jin
1. Jiangsu Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology,2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology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.
王海峰,行鸿彦,陈 梦,赵 迪,李 瑾.基于精细复合多尺度散布熵与 XGBoost 的 海面小目标检测方法[J].电子测量与仪器学报,2023,37(1):12-20
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