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基于无监督图互信息最大化的海面小目标异常检测

许述文 何绮 茹宏涛

许述文, 何绮, 茹宏涛. 基于无监督图互信息最大化的海面小目标异常检测[J]. 电子与信息学报. doi: 10.11999/JEIT230887
引用本文: 许述文, 何绮, 茹宏涛. 基于无监督图互信息最大化的海面小目标异常检测[J]. 电子与信息学报. doi: 10.11999/JEIT230887
XU Shuwen, HE Qi, RU Hongtao. Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230887
Citation: XU Shuwen, HE Qi, RU Hongtao. Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230887

基于无监督图互信息最大化的海面小目标异常检测

doi: 10.11999/JEIT230887
基金项目: 国家自然科学基金(61871303, 62071346),高等学校学科创新引智计划(111计划)(B18039)
详细信息
    作者简介:

    许述文:男,教授,研究方向为雷达目标检测、海杂波特性分析与统计机器学习

    何绮:女,硕士生,研究方向为雷达目标检测、图信号处理、图神经网络

    茹宏涛:男,博士生,研究方向为雷达目标检测、图信号处理、图神经网络

    通讯作者:

    许述文 swxu@mail_xidian.edu.cn

  • 中图分类号: TN957.51

Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax

Funds: The National Natural Science Foundation of China (61871303, 62071346), The Fund for Foreign Scholars in University Research and Teaching Programs (The 111 Project) (B18039)
  • 摘要: 受到复杂海洋环境的影响,雷达对海面慢速小目标难以实现高性能检测。对于这类目标,传统的基于能量的统计检测方法存在着严重的性能损失。针对这一问题,该文提出了基于互信息最大化框架下的海面小目标检测方法,实现海杂波背景下的无监督目标异常检测任务。首先,考虑到高分辨雷达回波不满足传统神经网络对样本独立同分布的假设,该文从图的角度重新建模数据,利用回波的空时相关特性来构建图拓扑结构。该文提出相对最大节点度并联合7个已有特征作为节点的初始表示向量。接下来,采用图注意力网络作为互信息最大化框架中的编码器学习节点表示向量。最后,使用异常检测算法进行目标检测,并实现虚警可控。经实测数据验证,使用快速凸包学习算法时,相比三特征检测器,所提检测器性能提升了9.2%;相比时频三特征检测器,性能提升了7.9%。当网络输出更高维的表示向量时,使用孤立森林算法的检测器的性能提升了27.4%。
  • 图  1  图建模示意图(红色表示根节点,黄色表示与根节点相连接的节点)

    图  2  可视图技术示意

    图  3  RMD和RPH特征的分离度

    图  4  DGI框架

    图  5  基于孤立森林的检测器

    图  6  IPIX数据集中20组数据的平均信杂比

    图  7  基于互信息最大化的海面小目标检测流程

    图  8  检测概率随$m$的变化曲线

    图  9  平均检测概率随DGI输出节点表示向量维度的变化曲线

    图  10  本文所提方法与三特征检测器、时频三特征检测器在20组数据平均极化下的对比

    图  11  本文所提方法与孤立森林检测器在20组数据平均极化下的对比

    图  12  采用相同三特征的本文所提方法与三特征检测器在20组数据平均极化下的对比

    表  1  快速凸包算法在IPIX数据集20组数据不同极化情况下平均检测概率

    检测器HHHVVHVV平均
    三特征检测器0.65440.78250.82220.63430.7234
    时频三特征检测器0.69610.79110.81730.64260.7368
    本文所提检测器0.78000.85050.87490.75740.8157
    下载: 导出CSV

    表  2  孤立森林算法在IPIX数据集20组数据不同极化情况下平均检测概率

    检测器HHHVVHVV平均
    8特征孤立森林检测器0.32690.50850.56230.29980.4244
    本文所提检测器0.59650.80010.82520.57160.6983
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-08-11
  • 修回日期:  2024-01-23
  • 网络出版日期:  2024-02-05

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