视频合成孔径雷达双域联合运动目标检测方法

丁金闪 仲超 温利武 徐众

丁金闪, 仲超, 温利武, 等. 视频合成孔径雷达双域联合运动目标检测方法[J]. 雷达学报, 2022, 11(3): 313–323. doi: 10.12000/JR22036
引用本文: 丁金闪, 仲超, 温利武, 等. 视频合成孔径雷达双域联合运动目标检测方法[J]. 雷达学报, 2022, 11(3): 313–323. doi: 10.12000/JR22036
DING Jinshan, ZHONG Chao, WEN Liwu, et al. Joint detection of moving target in video synthetic aperture radar[J]. Journal of Radars, 2022, 11(3): 313–323. doi: 10.12000/JR22036
Citation: DING Jinshan, ZHONG Chao, WEN Liwu, et al. Joint detection of moving target in video synthetic aperture radar[J]. Journal of Radars, 2022, 11(3): 313–323. doi: 10.12000/JR22036

视频合成孔径雷达双域联合运动目标检测方法

doi: 10.12000/JR22036
基金项目: 国家自然科学基金(62171358)
详细信息
    作者简介:

    丁金闪(1980-),男,江苏人,博士,教授,研究方向为视频雷达系统及信号处理技术、新体制雷达等

    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

Joint Detection of Moving Target in Video Synthetic Aperture Radar

Funds: The National Natural Science Foundation of China (62171358)
More Information
  • 摘要: 视频合成孔径雷达(SAR)具有高帧率成像能力,可作为地面运动目标探测的重要技术手段。经典SAR地面动目标显示(SAR-GMTI)依靠目标回波能量来实现动目标检测,同时动目标阴影亦可作为视频SAR动目标检测的重要途径。然而,由于动目标能量和阴影的畸变或涂抹,依靠单一方式难以实现稳健的动目标检测。该文基于目标能量和阴影的双域联合检测思想,分别通过快速区域卷积神经网络和航迹关联两种技术途径实现了视频SAR动目标联合检测,给出了机载实测数据处理结果,并进行了详细分析。该文方法充分利用目标阴影与能量的特征及空时信息,提升了机动目标检测的稳健性。

     

  • 图  1  美国Sandia实验室公布的视频SAR结果

    Figure  1.  SAR video released by Sandia laboratory

    图  2  SAR图像及距离多普勒谱示意图

    Figure  2.  Illustrations of SAR image and RD spectrum

    图  3  基于快速区域卷积神经网络的双域联合检测流程图

    Figure  3.  Flow chart of joint detection algorithm based on Dual Faster R-CNN

    图  4  跨域航迹联合的动目标检测算法流程图

    Figure  4.  Flow chart of joint detection algorithm based on JTA

    图  5  经典Faster R-CNN与Dual Faster R-CNN检测结果对比

    Figure  5.  Comparison results of classical Faster R-CNN and Dual Faster R-CNN

    图  6  图像域帧间关联与JTA算法检测结果对比

    Figure  6.  Comparison results of data association in image domain and JTA algorithm

    表  1  运动目标阴影及其能量所在帧

    Table  1.   Frame number of target shadow and energy

    动目标阴影所在帧能量所在帧
    T1[1, 14][1, 14]
    T2[1, 64][1, 64]
    T3[1, 24][1, 38]
    T4[9, 28][8, 42]
    T5[26, 64][20, 64]
    下载: 导出CSV

    表  2  动目标检测性能统计结果

    Table  2.   Statistical results of moving target detection in SAR imagery

    方法虚警漏警
    图像域帧间关联7563
    Faster R-CNN716
    Dual Faster R-CNN017
    联合域航迹关联(JTA)115
    下载: 导出CSV

    表  3  处理耗时对比结果

    Table  3.   Comparison of processing times

    方法单帧平均耗时(s)
    图像域帧间关联1.58
    Faster R-CNN1.55
    Dual Faster R-CNN2.89
    联合域航迹关联(JTA)2.21
    下载: 导出CSV
  • [1] DAMINI A, BALAJI B, PARRY C, et al. A videoSAR mode for the x-band wideband experimental airborne radar[C]. SPIE 7699, Algorithms for Synthetic Aperture Radar Imagery XVII, Orlando, USA, 2010: 76990E.
    [2] MILLER J, BISHOP E, and DOERRY A. An application of backprojection for video SAR image formation exploiting a subaperature circular shift register[C]. SPIE 8746, Algorithms for Synthetic Aperture Radar Imagery XX, Baltimore, USA, 2013: 874609.
    [3] WALLACE H B. Development of a video SAR for FMV through clouds[C]. SPIE 9479, Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2015, Baltimore, USA, 2015: 94790L.
    [4] CHEN H C and MCGILLEM C D. Target motion compensation by spectrum shifting in synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(3): 895–901. doi: 10.1109/7.256313
    [5] BARBAROSSA S and FARINA A. Detection and imaging of moving objects with synthetic aperture radar. Part 2: Joint time-frequency analysis by Wigner-Ville distribution[J]. Journals & Magazines, 1992, 139(1): 89–97. doi: 10.1049/ip-f-2.1992.0011
    [6] HUANG Penghui, LIAO Guisheng, YANG Zhiwei, et al. A fast SAR imaging method for ground moving target using a second-order WVD transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 1940–1956. doi: 10.1109/TGRS.2015.2490582
    [7] LIGHTSTONE L, FAUBERT D, and REMPEL G. Multiple phase centre DPCA for airborne radar[C]. 1991 IEEE National Radar Conference, Los Angeles, USA, 1991: 36–40.
    [8] ZHANG Yuhong. Along track interferometry synthetic aperture radar (ATI-SAR) techniques for ground moving target detection[R]. AFRL-SN-RS-TR-2005-410, 2006.
    [9] 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
    [10] JAHANGIR M. Moving target detection for synthetic aperture radar via shadow detection[C]. 2007 IET International Conference on Radar Systems, Edinburgh, UK, 2007: 1–5.
    [11] WANG Hui, CHEN Zhansheng, and ZHENG Shichao. Preliminary research of low-RCS moving target detection based on Ka-band video SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 811–815. doi: 10.1109/LGRS.2017.2679755
    [12] ZHANG Ying, MAO Xinhua, YAN He, et al. A novel approach to moving targets shadow detection in VideoSAR imagery sequence[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, USA, 2017: 606–609.
    [13] TIAN Xiaoqing, LIU Jing, MALLICK M, et al. Simultaneous detection and tracking of moving-target shadows in ViSAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1182–1199. doi: 10.1109/TGRS.2020.2998782
    [14] ZHAO Baojun, HAN Yuqi, WANG Hongshuo, et al. Robust shadow tracking for video SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5): 821–825. doi: 10.1109/LGRS.2020.2988165
    [15] DING Jinshan, WEN Liwu, ZHONG Chao, et al. Video SAR moving target indication using deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(10): 7194–7204. doi: 10.1109/TGRS.2020.2980419
    [16] ZHANG Yun, YANG Shiyu, LI Hongbo, et al. Shadow tracking of moving target based on CNN for video SAR system[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 4399–4402.
    [17] ZHANG Hao and LIU Zhe. Moving target shadow detection based on deep learning in video SAR[C]. 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021: 4155–4158.
    [18] WEN Liwu, DING Jinshan, and LOFFELD O. Video SAR moving target detection using dual faster R-CNN[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2984–2994. doi: 10.1109/JSTARS.2021.3062176
    [19] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031
    [20] ZHONG Chao, DING Jinshan, and ZHANG Yuhong. Joint tracking of moving target in single-channel video SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5212718. doi: 10.1109/TGRS.2021.3115491
    [21] BAR-SHALOM Y, FORTMANN T E, and CABLE P G. Tracking and data association[J]. The Journal of the Acoustical Society of America, 1990, 87(2): 918–919. doi: 10.1121/1.398863
    [22] REID D. An algorithm for tracking multiple targets[J]. IEEE Transactions on Automatic Control, 1979, 24(6): 843–854. doi: 10.1109/TAC.1979.1102177
    [23] ROHLING H. Radar CFAR thresholding in clutter and multiple target situations[J]. IEEE Transactions on Aerospace and Electronic Systems, 1983, AES-19(4): 608–621. doi: 10.1109/TAES.1983.309350
    [24] SINGER R A and SEA R G. New results in optimizing surveillance system tracking and data correlation performance in dense multitarget environments[J]. IEEE Transactions on Automatic Control, 1973, 18(6): 571–582. doi: 10.1109/TAC.1973.1100421
    [25] RAMACHANDRA K V and DIVISION R C. Optimum steady state position, velocity, and acceleration estimation using noisy sampled position data[J]. IEEE Transactions on Aerospace and Electronic Systems, 1987, AES-23(5): 705–708. doi: 10.1109/TAES.1987.310865
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出版历程
  • 收稿日期:  2022-03-02
  • 修回日期:  2022-04-29
  • 网络出版日期:  2022-05-24
  • 刊出日期:  2022-06-28

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