雷达微弱目标信号、图像与特征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

陈小龙, 何肖阳, 邓振华, 等. 雷达微弱目标信号、图像与特征智能化处理技术与应用[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR23160
引用本文: 陈小龙, 何肖阳, 邓振华, 等. 雷达微弱目标信号、图像与特征智能化处理技术与应用[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR23160
CHEN Xiaolong, HE Xiaoyang, DENG Zhenhua, et al. Intelligent processing technology and application for radar weak target signals, images and features[J]. Journal of Radars, in press. doi: 10.12000/JR23160
Citation: CHEN Xiaolong, HE Xiaoyang, DENG Zhenhua, et al. Intelligent processing technology and application for radar weak target signals, images and features[J]. Journal of Radars, in press. doi: 10.12000/JR23160

雷达微弱目标信号、图像与特征智能化处理技术与应用

doi: 10.12000/JR23160
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,研究生,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

  • 责任主编:杜兰 Corresponding Editor: DU Lan
  • 中图分类号: TN957.51

Intelligent Processing Technology and Application for Radar Weak Target Signals, Images and Features

Funds: The National Nature Science Foundation of China (62222120, 61931021), Shandong Provincial Natural Science Foundation (ZR2021YQ43)
More Information
  • 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。

     

  • 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]

    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]

    图  3  DAE-GAN系统框图[15]

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

    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]

    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]

    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]

    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]

    图  8  SCS-GAN模型结构图[11]

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

    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)

    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]

    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]

    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]

    图  13  通过SSE模块的检测效果图[39]

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

    图  14  无人机微多普勒时频谱图

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

    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]

    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]

    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

    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]

    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]

    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]

    图  21  NAS-FPN热力图结果[58]

    Figure  21.  NAS-FPN heat map results[58]

    图  22  深层注意力特征融合模块[61]

    Figure  22.  Deep attention feature fusion module[61]

    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]

    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 microtremors

    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

    图  26  深度强化学习原理框架图

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

    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]

    图  28  DCCNN网络结构图[1]

    Figure  28.  DCNN network structure diagram[1]

    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 micromotion recognition

    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

    表  1  舰船、干扰检测识别准确率(%)[50]

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

    模型名称舰船干扰平均
    恒虚警率检测方法CA-CFAR0.8420.8040.823
    自适应阈值分割OTSU0.8830.8390.861
    卷积网络CNN0.9710.8770.924
    非对称检测卷积神经网络AD-CNN0.9950.9410.968
    非对称检测卷积神经网络AD-CNN(Efficient)0.9960.9440.970
    下载: 导出CSV

    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
    下载: 导出CSV
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