陈小龙,,
关键,
周伟,
刘宁波,
董云龙
海军航空大学 烟台 264001
基金项目:国家自然科学基金(U1933135, 61931021)，山东省重点研发计划(2019GSF111004, 2019JZZY010415)，基础加强计划技术领域基金(2102024)
详细信息
作者简介:牟效乾(1995–)，男，山东烟台人，硕士生。研究领域包括智能雷达信号处理、动目标检测等。E-mail: 1012226010@qq.com
陈小龙(1985–)，男，山东烟台人，博士，副教授。研究领域包括雷达信号处理、海杂波抑制、雷达智能探测等。入选中国科协“青年人才托举工程”，被评为中国电子学会优秀科技工作者，获中国电子学会优博，中国专利优秀奖，军队科技进步一等奖等。E-mail: cxlcxl1209@163.com
关键：关 键(1968–)，男，辽宁锦州人，教授，博士生导师。主要研究方向包括雷达目标检测与跟踪、侦察图像处理和信息融合。获国家科技进步二等奖1项、军队科技进步一等奖2项，山东省技术发明一等奖1项；“百千万人才工程”国家级人选，入选教育部新世纪优秀人才支持计划。E-mail: guanjian_68@163.com
周伟：周 伟(1980–)，男，湖北黄石人，副教授，主要研究方向为多源信息融合、侦察图像处理、目标检测与识别。E-mail: yeaweam@gmail.com
刘宁波(1983–)，男，海军航空大学信息融合研究所副教授、博士，主要研究方向为雷达信号智能处理、海上目标探测技术。E-mail: lnb198300@163.com
董云龙(1974–)，男，天津宝坻人，教授，主要研究方向为多传感器信息融合。E-mail: china_dyl@sina.com
通讯作者:陈小龙 cxlcxl1209@163.com
责任主编：许述文 Corresponding Editor: XU Shuwen中图分类号:TN957.51
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出版历程
收稿日期:2020-07-02
修回日期:2020-08-16
网络出版日期:2020-08-25
Clutter Suppression and Marine Target Detection for Radar Images Based on INet
MOU Xiaoqian,CHEN Xiaolong,,
GUAN Jian,
ZHOU Wei,
LIU Ningbo,
DONG Yunlong
Naval Aviation University, Yantai 264001, China
Funds:The National Natural Science Foundation of China (U1933135, 61931021), The Key Research and Development Program of Shandong Province (2019GSF111004, 2019JZZY010415), The Fundamental Strengthening Technology Program (2102024))
More Information
Corresponding author:CHEN Xiaolong, cxlcxl1209@163.com
摘要
摘要:强海杂波与海面目标的复杂特性使得海面目标回波微弱，有效的海杂波抑制和稳健快速的目标检测是雷达对海上目标探测需考虑的重要因素。然而，现有的海面目标检测算法对于复杂环境下的目标检测性能有限，环境和目标特性适应性差。该文设计了一种杂波抑制和目标检测融合网络(INet)，通过层归一化-传递和连接方法提取关键目标特征，采用注意力网络抑制杂波和增强目标，构建跨阶段局部残差网络保证检测网络的轻量化和准确性。基于导航雷达在多种观测条件下采集的回波数据，构建了海面目标雷达图像数据集；通过模型的预训练和平面位置显示器(PPI)图像的帧间积累对INet进行了优化，得到了Optimized INet(O-INet)模型。经过多种天气条件下实测数据测试和验证，并与YOLOv3, YOLOv4，双参数CFAR和二维CA-CFAR对比后证明，所提方法在提高检测概率、降低虚警率和复杂条件下的强泛化能力有显著优势。
关键词:雷达图像/
动目标检测/
杂波抑制/
融合网络/
帧间积累
Abstract:A marine radar device is a major navigation tool for boaters and ships. The images produced by marine radars detect not only hard targets such as ships and coastlines, but also reflections from the sea surface, known as sea clutter. The strong sea clutter and the complex characteristics of marine targets result in transmission of weak echo signals of the images to the radar, which makes difficult for radars to distinguish and analyze. So, effective sea clutter suppression and robust, fast target detection mechanisms are needed for radar to detect marine targets efficiently. However, the existing marine target detection algorithms have limited performance for target detection under complex environments, and have poor adaptability to environment and target characteristics. In this paper, an Integrated Network (INet) for clutter suppression and target detection algorithm is proposed and designed to optimize the signals received from the targets. The layer normalization algorithm integrated with transfer function is used to extract key target features, and the spatial attention network is used to suppress the clutter and to enhance the target signals, and a local cross-scale residual network is built to ensure the weightlessness of the system and accuracy of the detection network. Based on the echo data collected by the navigation radar under various observation conditions, radar images with marine target dataset were constructed. INet was optimized through pre-training of the model and inter-frame accumulation of Plan Position Indicator (PPI) images to obtain the Optimized INet (O-INet). The measured data were verified, tested, and compared with data obtained through various algorithms such as YOLOv3, YOLOv4, two-parameter CFAR, and two-dimensional CA-CFAR. The results obtained prove that the proposed method has superior advantages over other methods in improving detection probability, reducing false alarm rate, and strong generalization ability under complex conditions.
Key words:Radar images/
Moving target detection/
Clutter suppression/
Integrated Network (INet)/
Inter-frame integration
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