视频SAR成像与动目标阴影检测技术

丁金闪

丁金闪. 视频SAR成像与动目标阴影检测技术[J]. 雷达学报, 2020, 9(2): 321–334. doi: 10.12000/JR20018
引用本文: 丁金闪. 视频SAR成像与动目标阴影检测技术[J]. 雷达学报, 2020, 9(2): 321–334. doi: 10.12000/JR20018
DING Jinshan. Focusing algorithms and moving target detection based on video SAR[J]. Journal of Radars, 2020, 9(2): 321–334. doi: 10.12000/JR20018
Citation: DING Jinshan. Focusing algorithms and moving target detection based on video SAR[J]. Journal of Radars, 2020, 9(2): 321–334. doi: 10.12000/JR20018

视频SAR成像与动目标阴影检测技术

doi: 10.12000/JR20018
基金项目: 科技部重点研发计划项目(2016YFE0200400)
详细信息
    作者简介:

    丁金闪(1980–),男,江苏人,教授,博士生导师,西安电子科技大学电子工程学院副院长,研究方向为毫米波与太赫兹雷达技术、雷达信号处理技术。E-mail: ding@xidian.edu.cn

    通讯作者:

    丁金闪 ding@xidian.edu.cn

  • 责任主编:张润宁 Corresponding Editor: ZHANG Running
  • 中图分类号: TN957

Focusing Algorithms and Moving Target Detection Based on Video SAR

Funds: Key R & D plan of science and Technology Departmen (2016YFF0200400)
More Information
  • 摘要: 视频合成孔径雷达(SAR)技术将观测场景的动态信息以视频方式呈现出来,其高帧率成像特性有利于实现对地面机动目标的实时探测。视频SAR信号处理关键技术主要包括高帧率成像处理算法和运动目标检测技术等。该文对视频SAR成像处理进行了探讨,给出了两种典型视频SAR成像处理仿真数据结果,详细分析了视频SAR阴影形成机理和对动目标检测性能的影响,并将基于机器学习的视频SAR阴影目标检测技术与经典处理方法在实际数据上进行了验证对比。

     

  • 图  1  视频SAR成像处理中孔径划分示意图

    Figure  1.  Schematic diagram of aperture segment

    图  2  第10帧SAR图像及其局部放大图

    Figure  2.  Image of frame 10 and its zoom-in

    图  3  第20帧SAR图像及其局部放大图

    Figure  3.  Image of frame 20 and its zoom-in

    图  4  第30帧SAR图像及其局部放大图

    Figure  4.  Image of frame 30 and its zoom-in

    图  5  运动目标阴影产生模型俯视图

    Figure  5.  Top view geometry for the shadow formation of a moving target

    图  6  动目标遮挡模型

    Figure  6.  Illustrations of occlusion time caused by a moving target

    图  7  视频SAR动目标阴影仿真结果

    Figure  7.  Simulation of moving target shadows in video SAR image

    图  8  阴影区域点目标归一化脉冲响应

    Figure  8.  Normalized impulse response of a ground point scatterer

    图  9  SHBR变化特性曲线

    Figure  9.  SHBR curves

    图  10  阴影检测性能曲线

    Figure  10.  Detection performance curve of shadow detection

    图  11  基于背景差分的阴影检测流程图

    Figure  11.  Flowchart of shadow detection based on background difference

    图  12  基于背景差分的阴影检测结果

    Figure  12.  Results of shadow detection based on background difference

    图  13  基于深度网络的视频SAR动目标检测流程图

    Figure  13.  Flowchart of the moving target detection approach using deep neural network in video SAR

    图  14  用于漏警目标预测的Bi-LSTM网络结构

    Figure  14.  Structure of the designed Bi-LSTM for suppressing the missing alarm

    图  15  基于Faster-RCNN的初步检测结果

    Figure  15.  Preliminary detection results simply by using Faster-RCNN

    图  16  基于深度神经网络的动目标最终检测结果

    Figure  16.  Detection results of moving targets by using the DNN-based approach

    表  1  仿真参数

    Table  1.   Simulation parameters

    参数名称数值单位
    雷达载频$17.6$${\rm{GHz}}$
    调频率$1.5 \times {10^{13}}$${\rm{Hz/s}}$
    采样率$5 \times {10^7}$${\rm{Hz}}$
    脉冲重复频率$1000$${\rm{Hz}}$
    脉冲宽度$4.096 \times {10^{ - 5}}$${\rm{s}}$
    载机速度$180$${\rm{m/s}}$
    场景中心斜距$10000$${\rm{m}}$
    载机高度$6000$${\rm{m}}$
    成像擦地角$36.8$°
    天线电尺寸$0.4$${\rm{m}}$
    成像帧率$10$${\rm{Hz}}$
    下载: 导出CSV

    表  2  基于背景差分的阴影检测性能统计

    Table  2.   Statistical results of shadow detection based on background difference

    目标总数正确检测的目标虚警目标漏警目标
    7306587772
    下载: 导出CSV

    表  3  基于实测视频SAR数据的检测性能对比(目标总数:730)

    Table  3.   Comparisons of detection performance on the real video sar data (Target number: 730)

    方法正确检测的目标虚警目标漏警目标
    基于背景差分的阴影检测方法6587772
    Faster-RCNN60773123
    Faster-RCNN+滑窗密度聚类6069124
    Faster-RCNN+滑窗密度聚类+Bi-LSTM72397
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-15
  • 修回日期:  2020-04-20
  • 网络出版日期:  2020-05-08

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