| 基于INet的雷达图像杂波抑制和目标检测方法 |
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| 引用本文： | 牟效乾,陈小龙,关键,周伟,刘宁波,董云龙.基于INet的雷达图像杂波抑制和目标检测方法[J].雷达学报,2020,9(4):640-653. |
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| 作者姓名： | 牟效乾 陈小龙 关键 周伟 刘宁波 董云龙 |
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| 作者单位： | 海军航空大学 烟台 264001 |
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| 基金项目： | 国家自然科学基金;基础加强计划;山东省重点研发计划 |
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| 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。
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| 关 键 词： | 雷达图像 动目标检测 杂波抑制 融合网络 帧间积累 |
| 收稿时间： | 2020-07-02 |
Clutter Suppression and Marine Target Detection for Radar Images Based on INet |
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| MOU Xiaoqian,CHEN Xiaolong,GUAN Jian,ZHOU Wei,LIU Ningbo,DONG Yunlong.Clutter Suppression and Marine Target Detection for Radar Images Based on INet[J].Journal of Radars,2020,9(4):640-653. |
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| Authors: | MOU Xiaoqian CHEN Xiaolong GUAN Jian ZHOU Wei LIU Ningbo DONG Yunlong |
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| Affiliation: | Naval Aviation University, Yantai 264001, China |
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| 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. |
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