Volume 10 Issue 5
Oct.  2021
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2021  >  10(5): 718-724
Chen Xiao-long, Dong Yun-long, Li Xiu-you, Guan Jian. Modeling of Micromotion and Analysis of Properties of Rigid Marine Targets[J]. Journal of Radars, 2015, 4(6): 630-638. doi: 10.12000/JR15079
Citation: JIANG Yanwen, FAN Hongqi, and LI Shuangxun. A sparse Bayesian learning approach for vortex electromagnetic wave three-dimensional imaging in the Terahertz band[J]. Journal of Radars, 2021, 10(5): 718–724. doi: 10.12000/JR21151
shu

A Sparse Bayesian Learning Approach for Vortex Electromagnetic Wave Three-dimensional Imaging in the Terahertz Band

DOI: 10.12000/JR21151

  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (61871386, 62171446)
More Information
  • Corresponding author: JIANG Yanwen, j1991yuwen@163.com
  • Received Date: 2021-10-16
  • Rev Recd Date: 2021-10-25
    • Available Online: 2021-10-26
  • Publish Date: 2021-10-28
    Abstract
  • In the Inverse Synthetic Aperture Radar (ISAR) imaging system, when the terahertz radar transmits the wide bandwidth signal and vortex electromagnetic wave, Three-Dimensional (3D) high-resolution imaging can be achieved through information decoupling based on the differential radiation field formed by the vortex electromagnetic wave and the synthetic aperture formed by the relative movement of the radar and the target. Accordingly, a 3D imaging model based on the terahertz vortex electromagnetic wave ISAR is established. A new image reconstruction method is proposed based on the Sparse Bayesian Learning (SBL) and subregion amplitude threshold setting methods. The proposed method can significantly simplify the imaging procedure and reduce the computational load. The simulation results indicate that the proposed SBL method can achieve a higher resolution than the conventional fast Fourier transform-based method, and its reconstruction performance increases with an increase in the signal-to-noise ratio.

     

    • FullText(HTML)
    • References(19)
  • [1]
    LIU Kang, LIU Hongyan, QIN Yuliang, et al. Generation of OAM beams using phased array in the microwave band[J]. IEEE Transactions on Antennas and Propagation, 2016, 64(9): 3850–3857. doi: 10.1109/TAP.2016.2589960
    [2]
    郭忠义, 汪彦哲, 郑群, 等. 涡旋电磁波天线技术研究进展[J]. 雷达学报, 2019, 8(5): 631–655. doi: 10.12000/JR19091

    GUO Zhongyi, WANG Yanzhe, ZHENG Qun, et al. Advances of research on antenna technology of vortex electromagnetic waves[J]. Journal of Radars, 2019, 8(5): 631–655. doi: 10.12000/JR19091
    [3]
    SHEN Fei, MU Jiangnan, GUO Kai, et al. Generating circularly polarized vortex electromagnetic waves by the conical conformal patch antenna[J]. IEEE Transactions on Antennas and Propagation, 2019, 67(9): 5763–5771. doi: 10.1109/TAP.2019.2922545
    [4]
    郭桂蓉, 胡卫东, 杜小勇. 基于电磁涡旋的雷达目标成像[J]. 国防科技大学学报, 2013, 35(6): 71–76. doi: 10.3969/j.issn.1001-2486.2013.06.013

    GUO Guirong, HU Weidong, and DU Xiaoyong. Electromagnetic vortex based radar target imaging[J]. Journal of National University of Defense Technology, 2013, 35(6): 71–76. doi: 10.3969/j.issn.1001-2486.2013.06.013
    [5]
    BU Xiangxi, ZHANG Zhuo, CHEN Longyong, et al. Implementation of vortex electromagnetic waves High-Resolution synthetic aperture radar imaging[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17(5): 764–767. doi: 10.1109/LAWP.2018.2814980
    [6]
    LIU Kang, LIU Hongyan, SHA W E I, et al. Backward scattering of electrically large standard objects illuminated by OAM beams[J]. IEEE Antennas and Wireless Propagation Letters, 2020, 19(7): 1167–1171. doi: 10.1109/LAWP.2020.2993687
    [7]
    LIU Kang, CHENG Yongqiang, YANG Zhaocheng, et al. Orbital-angular-momentum-based electromagnetic vortex imaging[J]. IEEE Antennas and Wireless Propagation Letters, 2015, 14: 711–714. doi: 10.1109/LAWP.2014.2376970
    [8]
    YANG Taoli, LI Shihua, XU Ou, et al. Three dimensional SAR imaging based on vortex electromagnetic waves[J]. Remote Sensing Letters, 2018, 9(4): 343–352. doi: 10.1080/2150704X.2017.1421791
    [9]
    JIANG Yanwen, LIU Kang, WANG Hongqiang, et al. Orbital-angular-momentum-based ISAR imaging at terahertz frequencies[J]. IEEE Sensors Journal, 2018, 18(22): 9230–9235. doi: 10.1109/JSEN.2018.2869047
    [10]
    保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.

    BAO Zheng, XING Mengdao, and WANG Tong. Technology of Radar Imaging[M]. Beijing: Publishing House of Electronics Industry, 2005.
    [11]
    GUI Shuliang, LI Jin, and PI Yiming. Security imaging for multi-target screening based on adaptive scene segmentation with terahertz radar[J]. IEEE Sensors Journal, 2019, 19(7): 2675–2684. doi: 10.1109/JSEN.2018.2889884
    [12]
    JIANG Yanwen, WANG Hongqiang, QIN Yuliang, et al. A three-dimensional surface imaging method using THz dual-frequency interferometry[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(11): 1651–1655. doi: 10.1109/LGRS.2016.2600570
    [13]
    SHEEN D M, MCMAKIN D L, and HALL T E. Three-dimensional millimeter-wave imaging for concealed weapon detection[J]. IEEE Transactions on Microwave Theory and Techniques, 2001, 49(9): 1581–1592. doi: 10.1109/22.942570
    [14]
    CARIS M, STANKO S, PALM S, et al. 300 GHz radar for high resolution SAR and ISAR applications[C]. 2015 16th International Radar Symposium, Dresden, Germany, 2015: 577–580. doi: 10.1109/IRS.2015.7226313.
    [15]
    WIPF D P and RAO B D. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal Processing, 2004, 52(8): 2153–2164. doi: 10.1109/TSP.2004.831016
    [16]
    DESAI M D and JENKINS W K. Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar[J]. IEEE Transactions on Image Processing, 1992, 1(4): 505–517. doi: 10.1109/83.199920
    [17]
    蒋彦雯. 太赫兹阵列雷达三维成像技术研究[D]. [博士论文], 国防科技大学, 2018.

    JIANG Yanwen. Study on the 3D imaging technology for terahertz array radar[D]. [Ph. D. dissertation], National University of Defense Technology, 2018.
    [18]
    SEEGER M W and WIPF D P. Variational Bayesian inference techniques[J]. IEEE Signal Processing Magazine, 2010, 27(6): 81–91. doi: 10.1109/MSP.2010.938082
    [19]
    GONZALEZ R C, WOODS R E, EDDINS S L. 阮秋琦, 阮宇智, 译. 数字图像处理[M]. 3版. 北京: 电子工业出版社, 2020.

    GONZALEZ R C, WOODS R E, EDDINS S L. RUAN Qiuqi, RUAN Yuzhi, translation. Digital Image Processing[M]. 3rd ed. Beijing: Publishing House of Electronics Industry, 2020.
    • Relative Articles

  • Relative Articles

    [1]HUANG Zhongling, WU Chong, YAO Xiwen, WANG Lipeng, HAN Junwei. Physically Explainable Intelligent Perception and Application of SAR Target Characteristics Based on Time-frequency Analysis[J]. Journal of Radars, 2024, 13(2): 331-344. doi: 10.12000/JR23191
    [2]GAO Zhiqi, SUN Shuchen, HUANG Pingping, QI Yaolong, XU Wei. Improved L1/2 Threshold Iterative High Resolution SAR Imaging Algorithm[J]. Journal of Radars, 2023, 12(5): 1044-1055. doi: 10.12000/JR22243
    [3]CHEN Hongmeng, YU Jizhou, ZHANG Wenjie, LI Yachao, LI Jun, CAI Liang, LU Yaobing. Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario[J]. Journal of Radars, 2023, 12(6): 1125-1137. doi: 10.12000/JR23080
    [4]DU Bang, QIU Xiaolan, ZHANG Zhe, LEI Bin, DING Chibiao. L1 Minimization with Perturbation for Off-grid Tomographic SAR Imaging[J]. Journal of Radars, 2022, 11(1): 62-70. doi: 10.12000/JR21093
    [5]ZHAO Yao, XU Juncong, QUAN Xiangyin, CUI Li, ZHANG Zhe. Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures[J]. Journal of Radars, 2022, 11(1): 52-61. doi: 10.12000/JR21210
    [6]LI Jianbing, WANG Xuesong. Review of Radar Characteristics and Sensing Technologies of Distributed Soft Target[J]. Journal of Radars, 2021, 10(1): 86-99. doi: 10.12000/JR20052
    [7]XIE Jinwei, LI Zhenfang, WANG Fan, WANG Zhibin. SAR Tomography Imaging for Buildings Using an Inconsistency Criterion for Amplitude and Phase[J]. Journal of Radars, 2020, 9(1): 154-165. doi: 10.12000/JR19062
    [8]PAN Jie, WANG Shuai, LI Daojing, LU Xiaochun. High-resolution Wide-swath SAR Moving Target Imaging Technology Based on Distributed Compressed Sensing[J]. Journal of Radars, 2020, 9(1): 166-173. doi: 10.12000/JR19060
    [9]Wei Shunjun, Tian Bokun, Zhang Xiaoling, Shi Jun. Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming[J]. Journal of Radars, 2018, 7(6): 664-675. doi: 10.12000/JR17103
    [10]Hu Jingqiu, Liu Falin, Zhou Chongbin, Li Bo, Wang Dongjin. CS-SAR Imaging Method Based on Inverse Omega-K Algorithm[J]. Journal of Radars, 2017, 6(1): 25-33. doi: 10.12000/JR16027
    [11]Ren Xiaozhen, Yang Ruliang. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase[J]. Journal of Radars, 2016, 5(1): 65-71. doi: 10.12000/JR15135
    [12]Chen Wenfeng, Li Shaodong, Yang Jun, Ma Xiaoyan. Multiple Measurement Vectors ISAR Imaging Algorithm Based on a Class of Linearized Bregman Iteration[J]. Journal of Radars, 2016, 5(4): 389-401. doi: 10.12000/JR16057
    [13]Chen Ying, Zhong Fei, Guo Shuxu. Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal[J]. Journal of Radars, 2016, 5(5): 531-537. doi: 10.12000/JR15106
    [14]Zhong Jinrong, Wen Gongjian. Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)[J]. Journal of Radars, 2016, 5(1): 99-108. doi: 10.12000/JR15056
    [15]Li Liechen, Li Daojing, Huang Pingping. Airship Sparse Array Antenna Radar Real Aperture Imaging Based on Compressed Sensing and Sparsity in Transform Domain[J]. Journal of Radars, 2016, 5(1): 109-117. doi: 10.12000/JR14159
    [16]Yang Jun, Zhang Qun, Luo Ying, Deng Donghu. Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing[J]. Journal of Radars, 2016, 5(1): 90-98. doi: 10.12000/JR14107
    [17]Xiao Peng, Wu Youming, Yu Ze, Li Chunsheng. Azimuth Ambiguity Suppression in SAR Images Based on Compressive Sensing Recovery Algorithm[J]. Journal of Radars, 2016, 5(1): 35-41. doi: 10.12000/JR16004
    [18]Gu Fufei, Zhang Qun, Yang Qiu, Huo Wenjun, Wang Min. Compressed Sensing Imaging Algorithm for High-squint SAR Based on NCS Operator[J]. Journal of Radars, 2016, 5(1): 16-24. doi: 10.12000/JR15035
    [19]Liao Ming-sheng, Wei Lian-huan, Wang Zi-yun, Timo Balz, Zhang Lu. Compressive Sensing in High-resolution 3D SAR Tomography of Urban Scenarios[J]. Journal of Radars, 2015, 4(2): 123-129. doi: 10.12000/JR15031
    [20]Zhong Li-hua, Hu Dong-hui, Ding Chi-biao, Zhang Wen-yi. ISAR Sparse Aperture Imaging Algorithm for Large Size Target[J]. Journal of Radars, 2012, 1(3): 292-300. doi: 10.3724/SP.J.1300.2012.20033
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(9)

    1. 陈姣，刘毓，邓云开，金重阳，杨志航. 基于BiLSTM-CNN的地基SAR永久散射体选取. 国外电子测量技术. 2024(07): 24-32 .
    2. 白泽朝，王彦平，王振海，胡俊，李洋，林赟. 地基大视场SAR形变监测的非均匀大气相位校正方法. 雷达学报. 2023(01): 53-63 . 本站查看
    3. 徐甫，王政，李振洪，李永生. 复杂环境下的地基雷达大气改正方法. 武汉大学学报(信息科学版). 2023(12): 2069-2081 .
    4. 杜年春，王玉明，沈向前，谢翔. 滑坡灾害监测的圆弧合成孔径雷达大气相位校正方法. 测绘学报. 2022(10): 2139-2148 .
    5. 金重阳，刘毓，邓云开，田卫明，胡政权. 面向复杂大气扰动的GB-InSAR相位误差补偿方法. 信号处理. 2022(11): 2432-2442 .
    6. 汤进，刘毓，邓云开，胡政权，陈姣. 地基干涉合成孔径雷达的大气相位补偿研究. 测绘科学. 2022(11): 145-154 .
    7. 朱嘉鑫，邓云开，田卫明，胡程. 基于K-means算法的时序GB-InSAR图像PS实时选择方法. 信号处理. 2021(03): 349-357 .
    8. 朱嘉鑫，李永建，肖刚，余东洋，付振华，张志家，朱晨浩，戴颖超. 基于SAR图像配准的地基干涉雷达间断测量技术. 露天采矿技术. 2021(05): 46-50 .
    9. 吴昊，刘毓，邓云开，田卫明. 基于气象数据辅助的GB-InSAR大气相位补偿方法. 信号处理. 2021(08): 1496-1506 .

    Other cited types(15)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04010203040
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 10.8 %FULLTEXT: 10.8 %META: 72.8 %META: 72.8 %PDF: 16.4 %PDF: 16.4 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 17.6 %其他: 17.6 %其他: 0.6 %其他: 0.6 %China: 0.3 %China: 0.3 %Germany: 0.1 %Germany: 0.1 %India: 0.1 %India: 0.1 %Reserved: 0.1 %Reserved: 0.1 %San Jose: 0.2 %San Jose: 0.2 %Seattle: 0.0 %Seattle: 0.0 %Ulu Bedok: 0.0 %Ulu Bedok: 0.0 %[]: 0.6 %[]: 0.6 %三明: 0.1 %三明: 0.1 %上海: 1.4 %上海: 1.4 %中卫: 0.1 %中卫: 0.1 %临沂: 0.1 %临沂: 0.1 %丹东: 0.0 %丹东: 0.0 %乌鲁木齐: 0.1 %乌鲁木齐: 0.1 %伊斯法罕: 0.2 %伊斯法罕: 0.2 %佛山: 0.1 %佛山: 0.1 %保定: 0.0 %保定: 0.0 %凤凰城: 0.0 %凤凰城: 0.0 %包头: 0.0 %包头: 0.0 %北京: 12.0 %北京: 12.0 %北京市: 0.0 %北京市: 0.0 %北海: 0.1 %北海: 0.1 %南京: 1.9 %南京: 1.9 %南充: 0.0 %南充: 0.0 %南昌: 0.0 %南昌: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.2 %台北: 0.2 %台湾省: 0.0 %台湾省: 0.0 %合肥: 0.2 %合肥: 0.2 %周口: 0.1 %周口: 0.1 %呼和浩特: 0.2 %呼和浩特: 0.2 %咸阳: 0.1 %咸阳: 0.1 %哈尔滨: 0.3 %哈尔滨: 0.3 %哈尔滨市: 0.0 %哈尔滨市: 0.0 %嘉兴: 0.1 %嘉兴: 0.1 %大同: 0.1 %大同: 0.1 %天津: 0.2 %天津: 0.2 %太原: 0.0 %太原: 0.0 %宁波: 0.1 %宁波: 0.1 %安康: 0.0 %安康: 0.0 %宝鸡: 0.2 %宝鸡: 0.2 %宣城: 0.2 %宣城: 0.2 %宿迁: 0.2 %宿迁: 0.2 %巴音郭楞: 0.0 %巴音郭楞: 0.0 %常州: 0.5 %常州: 0.5 %常德: 0.0 %常德: 0.0 %广安: 0.0 %广安: 0.0 %广州: 0.8 %广州: 0.8 %张家口: 0.8 %张家口: 0.8 %张家界: 0.1 %张家界: 0.1 %徐州: 0.1 %徐州: 0.1 %慕尼黑: 0.2 %慕尼黑: 0.2 %成都: 1.6 %成都: 1.6 %扬州: 0.1 %扬州: 0.1 %新乡: 0.3 %新乡: 0.3 %新加坡: 0.0 %新加坡: 0.0 %新泽西: 0.0 %新泽西: 0.0 %无锡: 0.0 %无锡: 0.0 %昆明: 0.5 %昆明: 0.5 %杭州: 1.6 %杭州: 1.6 %桂林: 0.0 %桂林: 0.0 %武威: 0.1 %武威: 0.1 %武汉: 0.6 %武汉: 0.6 %汉中: 0.1 %汉中: 0.1 %沈阳: 0.0 %沈阳: 0.0 %沧州: 0.1 %沧州: 0.1 %济南: 0.1 %济南: 0.1 %济宁: 0.0 %济宁: 0.0 %淮安: 0.1 %淮安: 0.1 %深圳: 0.3 %深圳: 0.3 %温州: 0.1 %温州: 0.1 %渭南: 0.0 %渭南: 0.0 %湖州: 0.0 %湖州: 0.0 %湘潭: 0.0 %湘潭: 0.0 %漯河: 0.2 %漯河: 0.2 %烟台: 0.1 %烟台: 0.1 %焦作: 0.0 %焦作: 0.0 %玉林: 0.2 %玉林: 0.2 %珠海: 0.1 %珠海: 0.1 %瓦赫兰: 0.2 %瓦赫兰: 0.2 %石家庄: 0.7 %石家庄: 0.7 %福州: 0.1 %福州: 0.1 %秦皇岛: 0.2 %秦皇岛: 0.2 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.0 %绍兴: 0.0 %美国伊利诺斯芝加哥: 0.1 %美国伊利诺斯芝加哥: 0.1 %芒廷维尤: 15.4 %芒廷维尤: 15.4 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.1 %苏州: 0.1 %衢州: 0.1 %衢州: 0.1 %西宁: 29.0 %西宁: 29.0 %西安: 2.2 %西安: 2.2 %贵港: 0.1 %贵港: 0.1 %贵阳: 0.3 %贵阳: 0.3 %赤峰: 0.0 %赤峰: 0.0 %运城: 0.3 %运城: 0.3 %郑州: 1.5 %郑州: 1.5 %重庆: 0.5 %重庆: 0.5 %铁岭: 0.1 %铁岭: 0.1 %镇江: 0.2 %镇江: 0.2 %长沙: 0.7 %长沙: 0.7 %长沙市: 0.0 %长沙市: 0.0 %青岛: 0.2 %青岛: 0.2 %香港: 0.0 %香港: 0.0 %香港特别行政区: 0.1 %香港特别行政区: 0.1 %齐齐哈尔: 0.2 %齐齐哈尔: 0.2 %其他其他ChinaGermanyIndiaReservedSan JoseSeattleUlu Bedok[]三明上海中卫临沂丹东乌鲁木齐伊斯法罕佛山保定凤凰城包头北京北京市北海南京南充南昌厦门台北台湾省合肥周口呼和浩特咸阳哈尔滨哈尔滨市嘉兴大同天津太原宁波安康宝鸡宣城宿迁巴音郭楞常州常德广安广州张家口张家界徐州慕尼黑成都扬州新乡新加坡新泽西无锡昆明杭州桂林武威武汉汉中沈阳沧州济南济宁淮安深圳温州渭南湖州湘潭漯河烟台焦作玉林珠海瓦赫兰石家庄福州秦皇岛纽约绍兴美国伊利诺斯芝加哥芒廷维尤芝加哥苏州衢州西宁西安贵港贵阳赤峰运城郑州重庆铁岭镇江长沙长沙市青岛香港香港特别行政区齐齐哈尔

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(1515) PDF downloads(214) Cited by(24)
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return