雷达微弱目标智能化处理技术与应用

陈小龙; 何肖阳; 邓振华; 关键; 杜晓林; 薛伟; 苏宁远; 王金豪

doi:10.12000/JR23160

雷达微弱目标智能化处理技术与应用

DOI: 10.12000/JR23160

1.
海军航空大学烟台 264001
2.
烟台大学烟台 264005
3.
哈尔滨工程大学烟台研究院烟台 264001

基金项目: 国家自然科学基金(62222120, 61931021)，山东省自然科学基金(ZR2021YQ43)

详细信息

作者简介:
陈小龙，博士，教授，主要研究方向为雷达低慢小目标检测、海杂波抑制、雷达智能信号处理等

何肖阳，硕士生，主要研究方向为海杂波背景下的目标检测

邓振华，硕士生，主要研究方向为基于深度学习的海空背景目标识别、分类

关　键，博士，教授，博士生导师，主要研究方向为雷达目标检测与跟踪、侦察图像处理和信息融合等

杜晓林，博士，副教授，硕士生导师，主要研究方向为雷达信号处理、波形设计、协方差矩阵估计等

薛　伟，博士，教授，博士生导师，主要研究方向为水下及地下无线通信技术、通信信号检测与识别技术等

苏宁远，博士生，主要研究方向为雷达智能信号处理、海面目标检测

王金豪，硕士生，主要研究方向为低慢小目标多域多特征检测

通讯作者:
陈小龙 cxlcxl1209@163.com

杜晓林 duxiaolin168@vip.163.com

责任主编：杜兰 Corresponding Editor: DU Lan
中图分类号: TN957.51
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出版历程
- 收稿日期: 2023-09-07
- 修回日期: 2024-01-29
- 网络出版日期: 2024-03-13
- 刊出日期: 2024-06-28

Radar Intelligent Processing Technology and Application for Weak Target

1.
Naval Aviation University, Yantai 264001, China
2.
Yantai University, Yantai 264005, China
3.
Yantai Research Institute of Harbin Engineering University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (62222120, 61931021), Shandong Provincial Natural Science Foundation (ZR2021YQ43)

More Information

Corresponding author: CHEN Xiaolong, cxlcxl1209@163.com; DU Xiaolin, duxiaolin168@vip.163.com

摘要

摘要: 雷达微弱目标处理是实现优异探测性能的基础和前提，在复杂的实际环境应用过程中，由于强杂波干扰、目标信号微弱、图像特征不明显、有效特征难提取等问题，导致雷达微弱目标检测与识别一直是雷达处理领域中的难点之一。传统模型类处理方法与实际工作背景和目标特性匹配不精准，导致通用性不强。近年来，深度学习在雷达智能信息处理领域取得了显著进展，深度学习算法通过构建深层神经网络，可以自动地从大量雷达数据中学习特征表示，提高目标检测和识别的性能。该文分别从雷达目标微弱信号处理、图像处理、特征学习等多个方面系统梳理和总结近年来雷达微弱目标智能化处理的研究进展，具体包括噪声与杂波抑制、微弱目标信号增强；低、高分辨雷达图像和特征图处理；特征提取、融合、目标分类与识别等。针对目前微弱目标智能化处理应用存在的泛化能力有限、特征单一、可解释性不足等问题，从小样本目标检测(迁移学习、强化学习)、多维多特征融合检测、网络模型可解释性、知识与数据联合驱动等方面对未来发展进行了展望。
- 微弱目标 /
- 深度学习 /
- 雷达信号处理 /
- 雷达图像处理 /
- 特征学习 /
- 小样本检测 /
- 特征融合 /
- 可解释性
Abstract: Weak target signal processing is the cornerstone and prerequisite for radar to achieve excellent detection performance. In complex practical applications, due to strong clutter interference, weak target signals, unclear image features, and difficult effective feature extraction, weak target detection and recognition have always been challenging in the field of radar processing. Conventional model-based processing methods do not accurately match the actual working background and target characteristics, leading to weak universality. Recently, deep learning has made significant progress in the field of radar intelligent information processing. By building deep neural networks, deep learning algorithms can automatically learn feature representations from a large amount of radar data, improving the performance of target detection and recognition. This article systematically reviews and summarizes recent research progress in the intelligent processing of weak radar targets in terms of signal processing, image processing, feature extraction, target classification, and target recognition. This article discusses noise and clutter suppression, target signal enhancement, low- and high-resolution radar image and feature processing, feature extraction, and fusion. In response to the limited generalization ability, single feature expression, and insufficient interpretability of existing intelligent processing applications for weak targets, this article underscores future developments from the aspects of small sample object detection (based on transfer learning and reinforcement learning), multidimensional and multifeature fusion, network model interpretability, and joint knowledge- and data-driven processing.
- Weak target /
- Deep learning /
- Radar signal processing /
- Radar image processing /
- Feature learning /
- Small sample testing /
- Feature fusion /
- Interpretability

HTML全文

图 1 基于GCN的杂波抑制方法流程图^[12]

Figure 1. Flowchart of GCN-based clutter suppression method^[12]

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图 2 基于RSETransformer的信号增强算法系统框图^[14]

Figure 2. Block diagram of RSETransformer based signal enhancement algorithm system^[14]

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图 3 DAE-GAN系统框图^[15]

Figure 3. Block diagram of DAE-GAN system^[15]

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图 4 基于LSTM预测的海面目标检测流程图^[18]

Figure 4. Flowchart of sea surface target detection based on LSTM prediction^[18]

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图 5 基于多帧联合目标检测流程概览^[21]

Figure 5. Overview of the multi-frame based joint target detection process^[21]

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图 6 雷达多维数据图结构的构建示意图^[12]

Figure 6. Schematic diagram of the construction of the radar multidimensional data graph structure^[12]

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图 7 基于GAN的MIMO雷达协方差矩阵数据恢复方法(不同协方差矩阵的雷达波束方向)^[26]

Figure 7. GAN-based data recovery method for MIMO radar covariance matrix (radar beam direction for different covariance matrices)^[26]

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图 8 SCS-GAN模型结构图^[11]

Figure 8. Structure of SCS-GAN model^[11]

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图 9 对海雷达智能信息处理开发平台实时海杂波抑制对比结果(SCS-GAN，台风5级海况)

Figure 9. Comparison results of real-time sea clutter suppression for the development platform of intelligent information processing for sea radar (SCS-GAN, Typhoon 5 sea state)

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图 10 海上目标导航雷达图像数据集示意图^[33]

Figure 10. Schematic diagram of the maritime target navigation radar image dataset^[33]

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图 11 雷达P显图像海上目标检测算法流程图^[33]

Figure 11. Flowchart of the maritime target detection algorithm for radar PPI images^[33]

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图 12 Faster R-CNN和Radar-PPInet对不同环境下雷达P显图像的检测结果^[34]

Figure 12. Detection results of Faster R-CNN and Radar-PPInet for radar PPI images in different environments^[34]

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图 13 通过SSE模块的检测效果图^[39]

Figure 13. Effect of detection by SSE module^[39]

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图 14 无人机微多普勒时频谱图

Figure 14. Spectrogram of the drone at micro-Doppler time

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图 15 基于多尺度神经网络的“低慢小”目标分类方法^[43]

Figure 15. Multi-scale neural network based low, slow and small target classification method^[43]

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图 16 基于时频图自主学习的检测流程图^[44]

Figure 16. Flowchart of detection based on autonomous learning of time-frequency diagrams^[44]

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图 17 高海况条件下海上目标的距离-多普勒谱图

Figure 17. Distance-Doppler spectra of targets at sea under high sea state conditions

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图 18 加入特征金字塔后的AD-CNN算法^[50]

Figure 18. AD-CNN algorithm after adding feature pyramid^[50]

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图 19 多通道特征模块流程示意图^[54]

Figure 19. Flow diagram of the multi-channel characterization module^[54]

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图 20 基于特征分解CNN的SAR图像目标检测^[55]

Figure 20. Feature decomposition CNN-based target detection for SAR images^[55]

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图 21 NAS-FPN热力图结果^[59]

Figure 21. NAS-FPN heat map results^[59]

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图 22 深层注意力特征融合模块^[61]

Figure 22. Deep attention feature fusion module^[61]

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图 23 海上微动目标CNN检测与分类流程图^[63]

Figure 23. Flowchart of CNN detection and classification of marine micro-motion targets^[63]

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图 24 海上微动目标分类迁移学习流程图

Figure 24. Flowchart of migratory learning for target categorization of marine micro-motion

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图 25 无人机微动特征识别深度迁移学习网络模型

Figure 25. Deep transfer learning network model for micro-motion feature recognition of drones

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图 26 深度强化学习原理框架图

Figure 26. Diagram of the principle framework of deep reinforcement learning

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图 27 结合强化学习的SAR目标检测方法整体框架^[70]

Figure 27. Overall framework of SAR target detection method combined with reinforcement learning^[70]

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图 28 DCCNN网络结构图^[20]

Figure 28. DCCNN network structure diagram^[20]

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图 29 无人机微动识别的可解释性学习框架

Figure 29. Interpretable learning framework for drone micro-motion recognition

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图 30 知识-数据联合驱动方法的动机

Figure 30. Motivation for a joint knowledge-data-driven approach

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表 1 舰船、干扰检测识别准确率(%)^[50]

Table 1. Ship and interference detection and identification accuracy (%)^[50]

模型名称	舰船	干扰	平均
恒虚警率检测方法CA-CFAR	84.2	80.4	82.3
自适应阈值分割OTSU	88.3	83.9	86.1
卷积网络CNN	97.1	87.7	92.4
非对称检测卷积神经网络AD-CNN	99.5	94.1	96.8
非对称检测卷积神经网络AD-CNN (Efficient)	99.6	94.4	97.0

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表 2 不同网络性能对比分析

Table 2. Comparative analysis of different network performance

网络模型	雷达特征	数据集	模型组成结构	适用条件
LeNet, AlexNet, GoogLeNet^[63]	海上微动特征	仿真目标和实测海杂波(Intelligent pixel-processing, IPIX雷达)	基于LeNet, AlexNet, GoogLeNe模型的检测与分类网络	杂波背景下的雷达微动目标
AlexNet^[64]	飞机尾涡特征	某机场40天内航班起飞的情况	基于AlexNet模型的识别网络	大气风场中尾涡
Transformer 融合网络^[61]	多站协同雷达HRRP特征	单部雷达采集的某一航线的5型目标回波	以Transformer作为特征提取主体结构，并在此基础上设计了3个新的辅助模块：角度引导模块、前级特征交互模块以及深层注意力特征融合模块	多站协同雷达目标

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