基于多通道图卷积神经网络的地海杂波分类方法

李灿 王增福 张效宣 潘泉

李灿, 王增福, 张效宣, 等. 基于多通道图卷积神经网络的地海杂波分类方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24165
引用本文: 李灿, 王增福, 张效宣, 等. 基于多通道图卷积神经网络的地海杂波分类方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24165
LI Can, WANG Zengfu, ZHANG Xiaoxuan, et al. Land-sea clutter classification method based on multi-channel graph convolutional neural networks[J]. Journal of Radars, in press. doi: 10.12000/JR24165
Citation: LI Can, WANG Zengfu, ZHANG Xiaoxuan, et al. Land-sea clutter classification method based on multi-channel graph convolutional neural networks[J]. Journal of Radars, in press. doi: 10.12000/JR24165

基于多通道图卷积神经网络的地海杂波分类方法

DOI: 10.12000/JR24165
基金项目: 国家自然科学基金(62473317, U21B2008)
详细信息
    作者简介:

    李 灿,博士生,主要研究方向为天波雷达数据处理、深度学习、图深度学习

    王增福,博士,副教授,主要研究方向为天波雷达数据处理、信息融合、传感器管理

    张效宣,博士生,主要研究方向为遥感图像生成与分类

    潘 泉,博士,教授,主要研究方向为信息融合理论及应用、目标跟踪与识别技术、光谱成像及图像处理

    通讯作者:

    王增福 wangzengfu@nwpu.edu.cn

  • 责任主编:许述文 Corresponding Editor: XU Shuwen
  • 中图分类号: TN958.93

Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Neural Networks

Funds: The National Natural Science Foundation of China (62473317, U21B2008)
More Information
  • 摘要: 地海杂波分类是提升天波超视距雷达目标定位精度的关键技术,其核心是判别距离多普勒(RD)图中每个方位-距离单元背景源自陆地或海洋的过程。基于传统深度学习的地海杂波分类方法需海量高质量且类别均衡的有标签样本,训练时间长,费效比高;此外,其输入为单个方位-距离单元杂波,未考虑样本的类内和类间信息,导致模型性能不佳。针对上述问题,该文通过分析相邻方位-距离单元之间的相关性,将地海杂波数据由欧氏空间转换为非欧氏空间中的图数据,引入样本之间的关系,并提出一种基于多通道图卷积神经网络(MC-GCN)的地海杂波分类方法。MC-GCN将图数据由单通道分解为多通道,每个通道只包含一种类型的边和一个权重矩阵,通过约束节点信息聚合的过程,能够有效缓解由异质性造成的节点属性误判。该文选取不同季节、不同时刻、不同探测区域RD图,依据雷达参数、数据特性和样本比例,构建包含12种不同场景的地海杂波原始数据集和36种不同配置的地海杂波稀缺数据集,并对MC-GCN的有效性进行验证。通过与最先进的地海杂波分类方法进行比较,该文所提出的MC-GCN在上述数据集中均表现最优,其分类准确率不低于92%。

     

  • 图  1  距离多普勒图

    Figure  1.  Range-Doppler map

    图  2  地海杂波图

    Figure  2.  Land-sea clutter map

    图  3  相邻方位-距离单元绝对距离

    Figure  3.  The absolute distance between adjacent azimuth-range cells

    图  4  相邻方位-距离单元余弦相似度

    Figure  4.  The cosine similarity between adjacent azimuth-range cells

    图  5  相邻方位-距离单元皮尔逊相关系数

    Figure  5.  The pearson correlation coefficient between adjacent azimuth-range cells

    图  6  图的构建

    Figure  6.  Graph construction

    图  7  错误的节点聚合

    Figure  7.  Wrong node aggregation

    图  8  多通道示意图

    Figure  8.  Diagram of multiple-channels

    图  9  MC-GCN中单个节点更新示意图

    Figure  9.  Diagram of a single node update in MC-GCN

    图  10  基于MC-GCN的地海杂波分类流程图

    Figure  10.  Flowchart of the land-sea clutter classification based on MC-GCN

    图  11  不同特性地海杂波样本

    Figure  11.  Land-sea clutter samples with different characteristics

    图  12  原始数据集中不同类别杂波比例

    Figure  12.  Proportion of different categories of clutter in the original dataset

    图  13  MC-GCN在原始数据集上的混淆矩阵

    Figure  13.  Confusion matrix of MC-GCN on the original dataset

    图  14  MC-GCN在原始数据集上的PE、RE和F1值

    Figure  14.  PE, RE and F1 values of MC-GCN on the original dataset

    图  15  MC-GCN在原始数据集上的正确率与损失曲线

    Figure  15.  Accuracy and loss curve of MC-GCN on the original dataset

    图  16  MC-GCN的地海杂波分类结果图

    Figure  16.  Figure of the land/sea clutter classification results by MC-GCN

    表  1  地海杂波数据集设置(%)

    Table  1.   The setting of land-sea clutter dataset (%)

    分组 样本特性 原始数据集 稀缺数据集 测试集
    训练集 训练集 训练集 训练集
    A组 标准 70 50 30 10 30
    多普勒频移 70 50 30 10 30
    幅值波动 70 50 30 10 30
    窄带射频干扰 70 50 30 10 30
    B组 标准 70 50 30 10 30
    多普勒频移 70 50 30 10 30
    幅值波动 70 50 30 10 30
    窄带射频干扰 70 50 30 10 30
    C组 标准 70 50 30 10 30
    多普勒频移 70 50 30 10 30
    幅值波动 70 50 30 10 30
    窄带射频干扰 70 50 30 10 30
    下载: 导出CSV

    表  2  实验环境

    Table  2.   Experimental environment

    环境版本
    SystemWindows10(64-bit)
    GPUNVIDIA GeForce RTX 3090
    CUDA11.3.1
    python3.8.0
    torch1.11.0
    torchvison0.12.0
    Numpy1.24.3
    matplotlib3.5.1
    dgl1.1.0
    下载: 导出CSV

    表  3  实验参数

    Table  3.   Experimental parameters

    参数 数值
    Epoch 500
    Learning rate 0.001
    Hidden units 16
    Layers 2
    Input size $ \left[ {{N_{\mathrm{R}}} * {N_{\mathrm{A}}},{N_{\mathrm{D}}}} \right] $
    Output size $ \left[ {{N_{\mathrm{R}}} * {N_{\mathrm{A}}},3} \right] $
    Beta1 0.5
    Beta2 0.999
    下载: 导出CSV

    表  4  原始数据集相关性分析

    Table  4.   Correlation analysis on the original dataset

    分组 标准 多普勒频移 幅值波动 窄带射频干扰
    AD CS PCC AD CS PCC AD CS PCC AD CS PCC
    A组 2.21, 1.79 0.76, 0.84 0.73, 0.82 1.97, 1.87 0.95, 0.95 0.81, 0.82 1.97, 1.59 0.88, 0.92 0.84, 0.89 2.04, 1.60 0.92, 0.95 0.81, 0.88
    B组 1.96, 1.71 0.89, 0.92 0.83, 0.87 1.90, 1.47 0.93, 0.95 0.86, 0.90 1.88, 1.59 0.90, 0.93 0.86, 0.89 2.10, 1.83 0.85, 0.89 0.72, 0.79
    C组 1.95, 1.52 0.91, 0.94 0.84, 0.89 2.11, 1.71 0.90, 0.93 0.76, 0.82 2.13, 1.69 0.97, 0.98 0.71, 0.81 1.99, 1.63 0.91, 0.94 0.83, 0.88
    下载: 导出CSV

    表  5  原始数据集与稀缺数据集上分类准确率(%)

    Table  5.   Classification accuracy on the original dataset and the scarce dataset (%)

    分组 方法 标准(AC) 多普勒频移(AC) 幅值波动(AC) 窄带射频干扰(AC)
    70% 50% 30% 10% 70% 50% 30% 10% 70% 50% 30% 10% 70% 50% 30% 10%
    A组 MC-GCN 97.62 96.78 95.93 95.09 96.52 96.33 96.05 96.19 96.90 96.73 96.76 96.40 96.63 96.28 96.24 93.32
    GCN 94.88 94.50 92.53 90.03 90.98 90.48 89.82 89.08 96.29 95.19 96.12 91.51 92.72 91.28 89.52 89.13
    GAT 94.91 92.74 93.15 92.89 90.89 89.63 89.18 86.01 95.05 95.98 94.81 92.86 91.67 91.21 89.82 88.87
    TA-GAN 94.21 92.59 90.61 90.37 92.36 91.16 88.69 86.27 94.12 93.94 92.95 91.99 92.37 91.69 90.11 88.52
    ResNet18 95.40 90.20 84.84 78.63 94.48 90.75 83.65 75.44 96.57 89.83 84.43 75.02 95.49 91.56 85.45 77.48
    DCNN 94.29 90.75 82.81 74.53 93.46 89.75 81.83 72.94 95.97 90.68 82.74 69.52 95.05 91.12 83.27 70.64
    B组 MC-GCN 96.69 96.28 95.04 95.88 97.04 97.08 95.22 95.75 97.14 95.52 95.04 93.47 96.72 96.53 96.34 95.30
    GCN 93.58 93.10 92.81 92.27 93.82 93.70 92.90 90.78 91.08 91.22 90.34 88.97 92.19 90.18 90.77 89.51
    GAT 94.08 93.72 93.62 92.96 92.40 92.04 91.57 90.64 92.86 91.46 89.87 88.83 92.72 91.77 91.34 89.90
    TA-GAN 94.29 92.74 91.79 90.37 94.37 92.72 91.49 91.66 92.28 91.38 89.05 87.96 93.43 91.98 90.80 88.96
    ResNet18 96.11 90.39 83.42 75.18 95.36 89.72 81.19 74.74 94.14 90.66 81.79 74.38 93.33 89.90 83.28 75.84
    DCNN 95.68 89.57 81.35 74.96 94.92 88.43 79.76 71.48 93.15 88.51 78.24 71.39 92.74 85.80 78.56 72.94
    C组 MC-GCN 96.74 96.62 95.57 94.71 96.53 96.51 95.97 95.94 95.78 95.95 94.80 92.88 96.81 96.49 96.37 95.92
    GCN 92.43 91.03 88.96 87.29 90.10 91.38 89.06 88.85 91.09 91.50 90.41 87.39 92.61 91.53 90.09 89.45
    GAT 92.44 90.84 89.94 89.36 91.26 90.54 90.06 89.42 92.07 91.45 91.12 90.50 91.60 91.78 90.45 88.50
    TA-GAN 92.36 91.61 89.47 86.64 91.79 90.96 89.52 88.33 92.47 91.99 90.89 89.87 92.41 91.46 90.51 89.73
    ResNet18 94.90 90.41 85.19 77.49 95.19 90.00 80.01 74.62 95.44 91.30 81.21 73.71 93.67 89.47 86.42 77.47
    DCNN 94.66 89.36 81.59 76.97 94.75 88.97 79.58 72.38 93.45 87.16 79.44 71.21 91.95 87.27 82.86 75.64
    注:表5表6加粗数值...。
    下载: 导出CSV

    表  6  MC-GCN在原始数据集在不同通道组合下分类准确率(%)

    Table  6.   Classification accuracy of the original dataset under different channel combinations (%)

    通道数 标准(AC) 多普勒频移(AC) 幅值波动(AC) 窄带射频干扰(AC)
    1 2 3 4 5 6 A组 B组 C组 A组 B组 C组 A组 B组 C组 A组 B组 C组
    97.62 96.69 97.74 96.52 97.04 96.53 96.90 97.14 95.78 96.63 96.72 96.81
    90.41 91.93 87.62 85.29 89.14 91.28 79.27 84.53 93.49 92.05 85.98 91.68
    96.09 95.33 94.19 95.21 94.29 95.18 95.85 95.54 94.02 96.05 95.92 95.48
    95.24 94.93 91.72 93.38 92.46 89.41 95.40 94.41 90.94 89.00 95.40 95.83
    94.43 93.59 94.24 91.14 93.30 88.31 93.12 91.74 91.06 93.52 92.06 93.01
    92.26 93.50 93.45 92.81 93.38 91.72 91.63 93.97 92.01 92.78 91.88 93.85
    91.98 91.31 84.08 90.41 88.19 89.69 94.07 87.74 88.61 93.47 92.84 86.98
    下载: 导出CSV

    表  7  不同方法在标准场景下跨域分类准确率(%)

    Table  7.   Cross-domain classification accuracy of different methods in standard scenarios (%)

    训练集 方法 A→B
    (AC)
    B→A
    (AC)
    A→C
    (AC)
    C→A
    (AC)
    B→C
    (AC)
    C→B
    (AC)
    70% MC-GCN 90.91 86.15 80.51 89.41 79.21 86.51
    ResNet18 81.28 84.57 79.25 85.37 83.52 84.94
    50% MC-GCN 87.75 86.96 89.27 89.56 66.60 85.84
    ResNet18 74.39 76.82 68.74 74.58 75.73 77.48
    30% MC-GCN 87.10 87.91 79.32 80.75 55.29 79.56
    ResNet18 62.54 67.49 63.46 66.57 69.49 68.37
    10% MC-GCN 88.94 78.23 74.99 80.56 85.27 84.58
    ResNet18 55.97 58.36 52.18 57.43 61.72 60.15
    下载: 导出CSV

    表  8  计算复杂度

    Table  8.   Computation complexity

    模型空间复杂度/M时间复杂度/s
    MC-GCN0.15723
    GCN0.01610
    DCNN10.535805
    下载: 导出CSV
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