基于深度学习的多特征融合海面目标检测方法

汪翔; 汪育苗; 陈星宇; 臧传飞; 崔国龙

doi:10.12000/JR23105

基于深度学习的多特征融合海面目标检测方法

doi: 10.12000/JR23105

汪翔¹,
汪育苗¹,
陈星宇¹,
臧传飞¹,
崔国龙^{1, 2, ,}

1.
电子科技大学信息与通信工程学院成都 611731
2.
电子科技大学长三角研究院衢州 324000

基金项目: 国家自然科学基金(62271126)，衢州市财政资助科研项目(2022D008, 2022D005)，广东省重点领域研发计划(2020B090905002)，高等学校学科创新引智计划(B17008)

详细信息

作者简介:
汪　翔，博士生，主要研究方向为雷达目标检测识别和机器学习

汪育苗，博士生，主要研究方向为雷达目标检测、杂波抑制和深度学习

陈星宇，硕士生，主要研究方向为雷达目标检测跟踪和深度学习

臧传飞，硕士生，主要研究方向为雷达杂波抑制、雷达目标跟踪和深度学习

崔国龙，教授，博士生导师，主要研究方向为最优化理论和算法、雷达目标检测理论、波形多样性以及阵列信号处理等

通讯作者:
崔国龙 cuiguolong@uestc.edu.cn

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: 2023-06-14
- 修回日期: 2023-07-15
- 网络出版日期: 2023-08-15

Deep Learning-based Marine Target Detection Method with Multiple Feature Fusion

1.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
Yangtze Delta Region Institute, University of Electronic Science and Technology of China, Quzhou 324000, China

Funds: The National Natural Science Foundation of China (62271126), The Municipal Government of Quzhou (2022D008, 2022D005), The Guangdong Key Areas Research and Development Program (2020B090905002), 111 Project (B17008)

More Information

Corresponding author: CUI Guolong, cuiguolong@uestc.edu.cn

摘要

摘要: 该文考虑了海杂波环境下的雷达目标检测问题，提出了一种基于深度学习的海面目标检测器。该检测器通过融合从不同数据源中提取的多种互补性特征以增加目标和杂波的差异性，从而提升对海面目标的检测性能。具体来说，该检测器首先利用两个特征提取分支分别从距离像和距离多普勒谱图中提取多层次快时间特征和距离特征；然后，设计局部-全局特征提取结构从特征的慢时间维度或多普勒维度提取序列关联性；接着，提出基于自适应卷积权重学习的特征融合模块，实现快慢时间特征和距离多普勒特征的高效融合；最后，对多层次特征进行融合、上采样和非线性映射获得检测结果。基于两个公开雷达数据集上的实验验证了所提检测器的检测性能。
- 雷达目标检测 /
- 海杂波 /
- 深度学习 /
- 卷积神经网络 /
- 特征融合
Abstract: Considering the problem of radar target detection in the sea clutter environment, this paper proposes a deep learning-based marine target detector. The proposed detector increases the differences between the target and clutter by fusing multiple complementary features extracted from different data sources, thereby improving the detection performance for marine targets. Specifically, the detector uses two feature extraction branches to extract multiple levels of fast-time and range features from the range profiles and the range-Doppler (RD) spectrum, respectively. Subsequently, the local-global feature extraction structure is developed to extract the sequence relations from the slow time or Doppler dimension of the features. Furthermore, the feature fusion block is proposed based on adaptive convolution weight learning to efficiently fuse slow-fast time and RD features. Finally, the detection results are obtained through upsampling and nonlinear mapping to the fused multiple levels of features. Experiments on two public radar databases validated the detection performance of the proposed detector.
- Radar target detection /
- Sea clutter /
- Deep learning /
- Convolutional neural network /
- Feature fusion

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图 1 IPIX数据集#17子数据集的某段HH极化距离像

Figure 1. Range profiles of #17 sub-data set with HH polarization in IPIX database

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图 2 IPIX数据集#17子数据集HH极化距离多普勒谱图

Figure 2. Range-Doppler spectrum of #17 sub-data set with HH polarization in IPIX database

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图 3 MFF检测器架构图

Figure 3. Architecture of the MFF detector

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图 4 序列特征提取模块结构

Figure 4. The structure of sequence feature extraction block

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图 5 基于自适应卷积权重学习的多特征融合模块结构

Figure 5. Structure of multiple feature fusion block based on adaptive convolution weight learning

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图 6 检测层结构

Figure 6. The structure of detection layer

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图 7 不同检测器在IPIX数据集上检测性能

Figure 7. Detection performance of various detectors in IPIX database

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图 8 “2021010150614_03_staring.mat”数据集幅度图

Figure 8. Amplitude image of “2021010150614_03_staring.mat” data set

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图 9 不同检测器在测试集上的检测可视化

Figure 9. Detection visualization of various detectors on test set

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表 1 MFF检测器参数设置

Table 1. Parameter setting of MFF detector

参数名称	特征提取模块	池化层	序列特征提取模块	特征融合模块	上采样层	检测层
核尺寸	(1,3), (1,3); (1,3), (1,3); (1,3), (1,3);	(1,2)	(3,1), (3,1); (1,1), (1,1)	(1,1), (1,1); (1,3), (1,3)	(1,4); (1,2)	(1,3), (1,3); (1,1)
步长	(1,1), (1,1); (1,1), (1,1); (1,1), (1,1);	(1,1)	(2,1), (2,1); (1,1), (1,1)	(1,1), (1,1); (1,1), (1,1)	(1,4); (1,2)	(1,1), (1,1); (1,1);
填充尺寸	(0,1), (0,1); (0,1), (0,1); (0,1), (0,1);	(0,0)	(1,0), (1,0); (0,0), (0,0)	(0,0), (0,0); (0,1), (0,1)	(0,0); (0,0)	(0,1), (0,1); (0,0)
通道维度	(1,16), (16,16); (16,32), (32,32); (32,64), (64,64);	N/A	(${C_1}$,$2{C_1}$), ($2{C_1}$,${C_1}$); (16,8), (8,1)	(${C_1}$,1), (${C_1}$,1); (${C_1}$,${{{C_1}} \mathord{\left/ {\vphantom {{{C_1}} 2}} \right. } 2}$), (${{{C_1}} \mathord{\left/ {\vphantom {{{C_1}} 2}} \right. } 2}$,${C_1}$)	(64,64); (32,32)	(112,64),(64,64); (64,2)
注：对于3个序列特征提取模块和特征融合模块，${C_1}$依次设置为16,32,64。

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表 2 雷达参数

Table 2. Radar parameter

数据集名称	带宽(MHz)	载频(GHz)	脉冲重复频率(Hz)	脉宽(μs)
IPIX数据集	5	9.39	1000	0.2
雷达对海探测数据集	25	9.3～9.5	1600	3

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表 3 IPIX数据集子数据集信息

Table 3. Information of sub-dataset in IPIX database

数据集索引	子数据集名称	目标主回波单元	目标次回波单元
#17	19931107_135603_starea	9	8, 10, 11
#18	19931107_141630_starea	9	8, 10, 11
#19	19931107_145028_starea	8	7, 9
#25	19931108_213827_starea	7	6, 8
#26	19931108_220902_starea	7	6, 8
#30	19931109_191449_starea	7	6, 8
#31	19931109_202217_starea	7	6, 8
#40	19931110_001635_starea	7	5, 6, 8
#54	19931111_163625_starea	8	7, 9, 10
#280	19931118_023604_stareC0000	8	7, 10
#283	19931118_035737_stareC0000	10	8, 9, 11, 12
#310	19931118_162155_stareC0000	7	6, 8, 9
#311	19931118_162658_stareC0000	7	6, 8, 9
#320	19931118_174259_stareC0000	7	6, 8, 9

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表 4 训练样本数和测试样本数

Table 4. Sample number of training set and test set

数据集名称	训练集样本数	测试集样本数
IPIX数据集每个子数据集	7858	5237
雷达对海探测数据集	953	374

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表 5 MFF检测器和其变体检测器检测性能

Table 5. Detection performance of the MFF detector and its variant detectors

检测器名称	平均实际虚警率	平均检测概率
MFF	0.0004	0.9870
变体1	0.0005	0.9478
变体2	0.0005	0.9814

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表 6 不同特征融合方法检测性能

Table 6. Detection performance of different feature fusion approaches

融合方法	平均实际虚警率	平均检测概率
所提方法	0.0004	0.9870
特征串接	0.0003	0.9477
特征求和	0.0031	0.9619

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表 7 不同检测器在IPIX数据集不同极化数据上的平均检测概率

Table 7. Averagely detection probability of various detectors in IPIX database with different polarizations

检测器名称	HH	HV	VV	VH
MFF	1.0000	1.0000	1.0000	1.0000
三特征	0.6465	0.8620	0.5848	08630
支持向量机	0.6767	0.7673	0.6053	0.7761
MDCCNN	0.8224	0.8998	0.8217	0.9100
Bi-LSTM	0.7380	0.8889	0.7970	0.7963

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表 8 不同检测器在IPIX数据集不同极化数据上的平均实际虚警率

Table 8. Averagely actual false alarm rate of various detectors in IPIX database with different polarizations

检测器名称	HH	HV	VV	VH
MFF	0.0014	0.0016	0.0016	0.0017
三特征	0.0037	0.0043	0.0038	0.0043
支持向量机	0.0033	0.0036	0.0042	0.0035
MDCCNN	0.0047	0.0020	0.0049	0.0020
Bi-LSTM	0.0042	0.0022	0.0057	0.0029

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表 9 不同检测器在雷达对海探测数据集上的检测性能

Table 9. Detection performance of various detectors in SDRDSP database

检测器名称	实际虚警率	检测概率
MFF	0.0004	0.9870
CA-CFAR	0.0083	0.9265
GO-CFAR	0.0052	0.8944
SO-CFAR	0.0225	0.9588

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