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

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

西北工业大学自动化学院西安 710129

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
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出版历程
- 收稿日期: 2024-08-15
- 修回日期: 2024-10-03

Land–Sea Clutter Classification Method Based on Multi-Channel Graph Convolutional Neural Networks

School of Automation, Northwestern Polytechnical University, Xi’an, 710129, China

Funds: The National Natural Science Foundation of China (62473317, U21B2008)

More Information

Corresponding author: WANG Zengfu, wangzengfu@nwpu.edu.cn

摘要

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式空间转换为非欧式空间中的图数据，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%。
- 天波超视距雷达 /
- 地海杂波分类 /
- 图数据 /
- 图卷积神经网络 /
- 异质性
Abstract: Land–sea clutter classification is essential for boosting the target positioning accuracy of skywave over-the-horizon radar. This classification process involves discriminating whether each azimuth-range cell in the Range–Doppler (RD) map is overland or sea. Traditional deep learning methods for this task require extensive, high-quality, and class-balanced labeled samples, leading to long training periods and high costs. In addition, these methods typically use a single azimuth-range cell clutter without considering intra-class and inter-class relationships, resulting in poor model performance. To address these challenges, this study analyzes the correlation between adjacent azimuth-range cells, and converts land–sea clutter data from Euclidean space into graph data in non-Euclidean space, thereby incorporating sample relationships. We propose a Multi-Channel Graph Convolutional Networks (MC-GCN) for land–sea clutter classification. MC-GCN decomposes graph data from a single channel into multiple channels, each containing a single type of edge and a weight matrix. This approach restricts node information aggregation, effectively reducing node attribute misjudgment caused by data heterogeneity. For validation, RD maps from various seasons, times, and detection areas were selected. Based on radar parameters, data characteristics, and sample proportions, we construct a land-sea clutter original dataset containing 12 different scenes and a land-sea clutter scarce dataset containing 36 different configurations. The effectiveness of MC-GCN is confirmed, with the approach outperforming state-of-the-art classification methods with a classification accuracy of at least 92%.
- Skywave over-the-horizon radar /
- Land–sea clutter classification /
- Graph data /
- Graph convolutional networks /
- Heterogeneity

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图 1 距离多普勒图

Figure 1. Range Doppler map

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图 2 地海杂波图

Figure 2. Land/sea clutter map

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图 3 相邻方位-距离单元绝对距离

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

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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

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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

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图 6 图的构建

Figure 6. Graph construction

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图 7 错误的节点聚合

Figure 7. Wrong node aggregation

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图 8 多通道示意图

Figure 8. Diagram of multiple channels

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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

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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

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图 11 不同特性地海杂波样本

Figure 11. Land/sea clutter samples with different characteristics

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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

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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.

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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

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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

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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

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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
分组	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

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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
分组	样本特性	训练集	训练集	训练集	训练集	测试集
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

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表 2 实验环境

Table 2. Experimental environment

环境	版本
System	Windows10(64-bit)
GPU	NVIDIA GeForce RTX 3090
CUDA	11.3.1
python	3.8.0
torch	1.11.0
torchvison	0.12.0
Numpy	1.24.3
matplotlib	3.5.1
dgl	1.1.0

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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_R} * {N_A},{N_D}} \right] $
Output Size	$ \left[ {{N_R} * {N_A},3} \right] $
Beta1	0.5
Beta2	0.999

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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)
分组	方法	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

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表 6 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 (%)

方法	通道数						标准(AC)			多普勒频移(AC)			幅值波动(AC)			窄带射频干扰(AC)
方法	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
MC-GCN	√	√	√	√	√	√	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

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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
70%	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
50%	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
30%	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
10%	ResNet18	55.97	58.36	52.18	57.43	61.72	60.15

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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-GCN	0.157	23
GCN	0.016	10
DCNN	10.535	805

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