WANG Yingfu, YIN Jiapeng, LU Zhonghao, et al. Analysis of the influence of distributed interrupted-sampling repeating signals on airborne interferometer parameter measurements[J]. Journal of Radars, 2024, 13(5): 1037–1048. doi: 10.12000/JR24090
Citation: QIU Xiaolan, LUO Yitong, SONG Shujie, et al. Microwave vision three-dimensional SAR experimental system and full-polarimetric data processing method[J]. Journal of Radars, 2024, 13(5): 941–954. doi: 10.12000/JR24137

Microwave Vision Three-dimensional SAR Experimental System and Full-polarimetric Data Processing Method

DOI: 10.12000/JR24137 CSTR: 32380.14.JR24137
Funds:  The National Natural Science Foundation of China (61991420, 61991421, 61991424)
More Information
  • Corresponding author: QIU Xiaolan, xlqiu@mail.ie.ac.cn
  • Received Date: 2024-07-05
  • Rev Recd Date: 2024-09-02
  • Available Online: 2024-09-03
  • Publish Date: 2024-09-18
  • Three-Dimensional (3D) Synthetic Aperture Radar (SAR) holds great potential for applications in fields such as mapping and disaster management, making it an important research focus in SAR technology. To advance the application and development of 3D SAR, especially by reducing the number of observations or antenna array elements, the Aerospace Information Research Institute, Chinese Academy of Sciences, (AIRCAS) has pioneered the development of the full-polarimetric Microwave Vision 3D SAR (MV3DSAR) experimental system. This system is designed to serve as an experimental platform and a source of data for microwave vision SAR 3D imaging studies. This study introduces the MV3DSAR experimental system along with its full-polarimetric SAR data set. It also proposes a set of full-polarimetric data processing scheme that covers essential steps such as polarization correction, polarization coherent enhancement, microwave vision 3D imaging, and 3D fusion visualization. The results from the 3D imaging data set confirm the full-polarimetric capabilities of the MV3DSAR experimental system and validate the effectiveness of the proposed processing method. The full-polarimetric unmanned aerial vehicle -borne array interferometric SAR data set, released through this study, offers enhanced data resources for advancing 3D SAR imaging research.

     

  • [1]
    KNAELL K K and CARDILLO G P. Radar tomography for the generation of three-dimensional images[J]. IEE Proceedings-Radar, Sonar and Navigation, 1995, 142(2): 54–60. doi: 10.1049/ip-rsn:19951791.
    [2]
    ZHU Xiaoxiang and BAMLER R. Superresolving SAR tomography for multidimensional imaging of urban areas: Compressive sensing-based TomoSAR inversion[J]. IEEE Signal Processing Magazine, 2014, 31(4): 51–58. doi: 10.1109/MSP.2014.2312098.
    [3]
    TEBALDINI S, NAGLER T, ROTT H, et al. Imaging the internal structure of an alpine glacier via L-band airborne SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12): 7197–7209. doi: 10.1109/TGRS.2016.2597361.
    [4]
    HUANG Yue, FERRO-FAMIL L, and REIGBER A. Under-foliage object imaging using SAR tomography and polarimetric spectral estimators[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(6): 2213–2225. doi: 10.1109/TGRS.2011.2171494.
    [5]
    FORNARO G, LOMBARDINI F, and SERAFINO F. Three-dimensional multipass SAR focusing: Experiments with long-term spaceborne data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 702–714. doi: 10.1109/TGRS.2005.843567.
    [6]
    张福博. 阵列干涉SAR三维重建信号处理技术研究[D]. [博士论文], 中国科学院大学, 2015.

    ZHANG Fubo. Research on signal processing of 3-D reconstruction in linear array synthetic aperture radar interferometry[D]. [Ph.D. dissertation], University of Chinese Academy of Sciences, 2015.
    [7]
    REIGBER A and MOREIRA A. First demonstration of airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5): 2142–2152. doi: 10.1109/36.868873.
    [8]
    ZHU Xiaoxiang and BAMLER R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 247–258. doi: 10.1109/TGRS.2011.2160183.
    [9]
    ZHU Xiaoxiang, GE Nan, and SHAHZAD M. Joint sparsity in SAR tomography for urban mapping[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1498–1509. doi: 10.1109/JSTSP.2015.2469646.
    [10]
    丁赤飚, 仇晓兰, 徐丰, 等. 合成孔径雷达三维成像——从层析、阵列到微波视觉[J]. 雷达学报, 2019, 8(6): 693–709. doi: 10.12000/JR19090.

    DING Chibiao, QIU Xiaolan, XU Feng, et al. Synthetic aperture radar three-dimensional imaging—from TomoSAR and array InSAR to microwave vision[J]. Journal of Radars, 2019, 8(6): 693–709. doi: 10.12000/JR19090.
    [11]
    仇晓兰, 焦泽坤, 杨振礼, 等. 微波视觉三维SAR关键技术及实验系统初步进展[J]. 雷达学报, 2022, 11(1): 1–19. doi: 10.12000/JR22027.

    QIU Xiaolan, JIAO Zekun, YANG Zhenli, et al. Key technology and preliminary progress of microwave vision 3D SAR experimental system[J]. Journal of Radars, 2022, 11(1): 1–19. doi: 10.12000/JR22027.
    [12]
    JIAO Zekun, DING Chibiao, QIU Xiaolan, et al. Urban 3D imaging using airborne TomoSAR: Contextual information-based approach in the statistical way[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170: 127–141. doi: 10.1016/j.isprsjprs.2020.10.013.
    [13]
    JIAO Zekun, QIU Xiaolan, DONG Shuhang, et al. Preliminary exploration of geometrical regularized SAR tomography[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 201: 174–192. doi: 10.1016/j.isprsjprs.2023.05.019.
    [14]
    朱庆涛, 殷君君, 曾亮, 等. 基于邻域一致性的极化SAR图像仿射配准[J]. 雷达学报, 2021, 10(1): 49–60. doi: 10.12000/JR20120.

    ZHU Qingtao, YIN Junjun, ZENG Liang, et al. Polarimetric SAR image affine registration based on neighborhood consensus[J]. Journal of Radars, 2021, 10(1): 49–60. doi: 10.12000/JR20120.
    [15]
    仇晓兰, 罗一通, 程遥, 等. SAR微波视觉三维成像数据集-3.0[EB/OL]. 雷达学报. https://radars.ac.cn/web/data/getData?newsColumnId=1cbc9f2d-f2ee-4748-9972-748c007f697f, 2024.

    QIU Xiaolan, LUO Yitong, CHENG Yao, et al. SAR microwave vision 3D imaging Dataset 3.0[EB/OL]. Journal of Radars. https://radars.ac.cn/web/data/getData?newsColumnId=2f2748db-10ef-4ad0-bcc4-f087ce59b6f8&pageType=en, 2024.
    [16]
    徐牧, 王雪松, 肖顺平. 基于改善极化相似性的极化SAR目标增强新方法[J]. 电子与信息学报, 2008, 30(5): 1047–1051. doi: 10.3724/SP.J.1146.2007.00754.

    XU Mu, WANG Xuesong, and XIAO Shunping. Target enhancement in POL-SAR imagery based on the improvement of polarization characteristics similarity[J]. Journal of Electronics & Information Technology, 2008, 30(5): 1047–1051. doi: 10.3724/SP.J.1146.2007.00754.
    [17]
    JIANG Sha, QIU Xiaolan, HAN Bing, et al. A quality assessment method based on common distributed targets for GF-3 polarimetric SAR data[J]. Sensors, 2018, 18(3): 807. doi: 10.3390/s18030807.
    [18]
    LEE J S and POTTIER E. Polarimetric Radar Imaging: From Basics to Applications[M]. Boca Raton: CRC Press, 2009. doi: 10.1201/9781420054989.
    [19]
    SONG Shujie, QIU Xiaolan, and SHANGGUAN Songtao. Study on the three dimension imaging methods of fully-polarised array InSAR[C]. IET International Radar Conference, Chongqing, China, 2023: 2999–3003. doi: 10.1049/icp.2024.1571.
    [20]
    SONG Shujie and QIU Xiaolan. A sparse Bayesian learning 3d imaging methodology based on polarimetric energy maximum in urban area for pol-array-insar[C]. IGARSS Conference, 2024, Athens, Greece, 2024. doi: 10.1109/IGARSS53475.2024.10641994.
  • Relative Articles

    [1]YUAN Weijie, WU Jun, SHI Yuye. Multi-UAV Collaborative Covert Communications: An ISAC-Based Approach[J]. Journal of Radars. doi: 10.12000/JR25018
    [2]REN Hang, SUN Zhichao, YANG Jianyu, WU Junjie. A Task Allocation Method for Swarm UAV SAR Based on Low Redundancy Chromosome Encoding[J]. Journal of Radars. doi: 10.12000/JR24218
    [3]LIANG Xiao, YE Shengbo, SONG Chenyang, YUAN Yubing, ZHANG Qunying, LIU Xiaojun, JIANG Hejun, LI Hong. Automatic Multitarget Detection Method Based on Distributed Through-wall Radar[J]. Journal of Radars, 2025, 14(1): 28-44. doi: 10.12000/JR24127
    [4]LI Zhi, TANG Chengyao, DAI Yongpeng, JIN Tian. Multirotor UAV-borne Vital Signs Sensing Using 4D Imaging Radar[J]. Journal of Radars, 2025, 14(1): 62-72. doi: 10.12000/JR24128
    [5]YANG Xiaopeng, GAO Weicheng, QU Xiaodong. Human Anomalous Gait Termination Recognition via Through-the-wall Radar Based on Micro-Doppler Corner Features and Non-Local Mechanism[J]. Journal of Radars, 2024, 13(1): 68-86. doi: 10.12000/JR23181
    [6]XING Mengdao, MA Penghui, LOU Yishan, SUN Guangcai, LIN Hao. Review of Fast Back Projection Algorithms in Synthetic Aperture Radar[J]. Journal of Radars, 2024, 13(1): 1-22. doi: 10.12000/JR23183
    [7]CHEN Yifan, LIU Jiangang, JIA Yong, GUO Shisheng, CUI Guolong. High-resolution Imaging Method for Through-the-wall Radar Based on Transfer Learning with Simulation Samples[J]. Journal of Radars, 2024, 13(4): 807-821. doi: 10.12000/JR24049
    [8]SHI Chenguang, WANG Yijie, DAI Xiangrong, ZHOU Jianjiang. Joint Transmit Resources and Trajectory Planning for Target Tracking in Airborne Radar Networks[J]. Journal of Radars, 2022, 11(5): 778-793. doi: 10.12000/JR22005
    [9]ZENG Tao, WEN Yuhan, WANG Yan, DING Zegang, WEI Yangkai, YUAN Tiaotiao. Research Progress on Synthetic Aperture Radar Parametric Imaging Methods[J]. Journal of Radars, 2021, 10(3): 327-341. doi: 10.12000/JR21004
    [10]WEI Yangkai, ZENG Tao, CHEN Xinliang, DING Zegang, FAN Yujie, WEN Yuhan. Parametric SAR Imaging for Typical Lines and Surfaces[J]. Journal of Radars, 2020, 9(1): 143-153. doi: 10.12000/JR19077
    [11]LI Xiaofeng, ZHANG Biao, YANG Xiaofeng. Remote Sensing of Sea Surface Wind and Wave from Spaceborne Synthetic Aperture Radar[J]. Journal of Radars, 2020, 9(3): 425-443. doi: 10.12000/JR20079
    [12]LI Yongzhen, HUANG Datong, XING Shiqi, WANG Xuesong. A Review of Synthetic Aperture Radar Jamming Technique[J]. Journal of Radars, 2020, 9(5): 753-764. doi: 10.12000/JR20087
    [13]HUANG Yan, ZHAO Bo, TAO Mingliang, CHEN Zhanye, HONG Wei. Review of Synthetic Aperture Radar Interference Suppression[J]. Journal of Radars, 2020, 9(1): 86-106. doi: 10.12000/JR19113
    [14]XING Mengdao, LIN Hao, CHEN Jianlai, SUN Guangcai, YAN Bangbang. A Review of Imaging Algorithms in Multi-platform-borne Synthetic Aperture Radar[J]. Journal of Radars, 2019, 8(6): 732-757. doi: 10.12000/JR19102
    [15]Liu Yuqi, Yi Jianxin, Wan Xianrong, Cheng Feng, Rao Yunhua, Gong Ziping. Experimental Research on Micro-Doppler Effect of Multi-rotor Drone with Digital Television Based Passive Radar[J]. Journal of Radars, 2018, 7(5): 585-592. doi: 10.12000/JR18062
    [16]Zhang Pengfei, Li Gang, Huo Chaoying, Yin Hongcheng. Classification of Drones Based on Micro-Doppler Radar Signatures Using Dual Radar Sensors[J]. Journal of Radars, 2018, 7(5): 557-564. doi: 10.12000/JR18061
    [17]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
    [18]Bai Yang, Wu Yang, Yin Hongcheng, Que Xiaofeng. Indoor Measurement Research on Polarimetric Scattering Characteristics of UAV[J]. Journal of Radars, 2016, 5(6): 647-657. doi: 10.12000/JR16032
    [19]Wang Yanfei, Liu Chang, Zhan Xueli, Han Song. Technology and Applications of UAV Synthetic Aperture Radar System[J]. Journal of Radars, 2016, 5(4): 333-349. doi: 10.12000/JR16089
    [20]Jin Tian. An Enhanced Imaging Method for Foliage Penetration Synthetic Aperture Radar[J]. Journal of Radars, 2015, 4(5): 503-508. doi: 10.12000/JR15114
  • Cited by

    Periodical cited type(9)

    1. 朱梦韬,张露瑶,李瑞,杨静. 基于HMM的逆雷达辐射源状态识别推理方法. 北京理工大学学报. 2024(02): 200-209 .
    2. 王沙飞,朱梦韬,李云杰,杨健,李岩. 对先进多功能雷达系统行为的识别、推理与预测:综述与展望. 信号处理. 2024(01): 17-55 .
    3. 付雨欣,黄洁,王建涛,党同心,李一鸣,孙震宇. 多功能相控阵雷达行为辨识综述. 电讯技术. 2024(04): 643-654 .
    4. 娄雨璇,孙闽红,尹帅. 基于近端策略优化算法和Mask-TIT网络的多功能雷达干扰决策方法. 数据采集与处理. 2024(06): 1355-1369 .
    5. 罗健,仇洪冰,周陬,顾宇,王若楠,费文浩. 基于SOM聚类平滑图信号生成的MFR工作模式识别方法. 桂林电子科技大学学报. 2023(02): 120-127 .
    6. 罗健. 一种基于图卷积网络的雷达工作模式识别方法. 成组技术与生产现代化. 2023(02): 14-19 .
    7. 廖艳苹,谢榕浩. 基于双层强化学习的多功能雷达认知干扰决策方法. 应用科技. 2023(06): 56-62 .
    8. 袁硕,拓世英,尚文秀,罗政昊,刘章孟. 电子侦察脉冲列中重频信息提取与应用综述. 信息对抗技术. 2023(06): 17-28 .
    9. 蒋能,张红敏,李一鸣. 基于分步变门限孤立森林的MFR波形单元无监督提取. 信息对抗技术. 2022(03): 76-85 .

    Other cited types(13)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04020406080100
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 30.7 %FULLTEXT: 30.7 %META: 52.6 %META: 52.6 %PDF: 16.7 %PDF: 16.7 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 9.4 %其他: 9.4 %其他: 1.0 %其他: 1.0 %Central District: 0.1 %Central District: 0.1 %China: 0.2 %China: 0.2 %Ecole-Valentin: 0.2 %Ecole-Valentin: 0.2 %Falls Church: 0.3 %Falls Church: 0.3 %Herndon: 0.1 %Herndon: 0.1 %Russian Federation: 0.1 %Russian Federation: 0.1 %上海: 3.0 %上海: 3.0 %东莞: 0.1 %东莞: 0.1 %伦敦: 0.1 %伦敦: 0.1 %佛山: 0.3 %佛山: 0.3 %六安: 0.1 %六安: 0.1 %兰州: 0.2 %兰州: 0.2 %内江: 0.1 %内江: 0.1 %列克星敦: 0.1 %列克星敦: 0.1 %加利福尼亚州: 0.4 %加利福尼亚州: 0.4 %北京: 18.2 %北京: 18.2 %十堰: 0.2 %十堰: 0.2 %南京: 3.9 %南京: 3.9 %南宁: 0.1 %南宁: 0.1 %南昌: 0.6 %南昌: 0.6 %南通: 0.1 %南通: 0.1 %印多尔: 0.1 %印多尔: 0.1 %厦门: 0.3 %厦门: 0.3 %双鸭山: 0.1 %双鸭山: 0.1 %台北: 0.2 %台北: 0.2 %台州: 0.2 %台州: 0.2 %合肥: 0.9 %合肥: 0.9 %吉安: 0.1 %吉安: 0.1 %呼和浩特: 0.2 %呼和浩特: 0.2 %哈尔滨: 0.5 %哈尔滨: 0.5 %哥伦布: 0.2 %哥伦布: 0.2 %嘉兴: 0.8 %嘉兴: 0.8 %圣克拉拉: 0.1 %圣克拉拉: 0.1 %圣安东尼奥: 0.1 %圣安东尼奥: 0.1 %大理: 0.3 %大理: 0.3 %大连: 0.2 %大连: 0.2 %大阪: 0.1 %大阪: 0.1 %天津: 0.6 %天津: 0.6 %太原: 0.3 %太原: 0.3 %威海: 0.3 %威海: 0.3 %宁波: 0.1 %宁波: 0.1 %安康: 0.3 %安康: 0.3 %安顺: 0.1 %安顺: 0.1 %宜春: 0.1 %宜春: 0.1 %宣城: 0.3 %宣城: 0.3 %宿州: 0.1 %宿州: 0.1 %常州: 0.5 %常州: 0.5 %常德: 0.1 %常德: 0.1 %广安: 0.1 %广安: 0.1 %广州: 1.2 %广州: 1.2 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %延安: 0.1 %延安: 0.1 %开封: 0.8 %开封: 0.8 %张家口: 1.0 %张家口: 1.0 %张家界: 0.2 %张家界: 0.2 %德里: 0.1 %德里: 0.1 %德黑兰: 0.1 %德黑兰: 0.1 %慕尼黑: 0.1 %慕尼黑: 0.1 %成都: 2.9 %成都: 2.9 %扬州: 0.4 %扬州: 0.4 %揭阳: 0.1 %揭阳: 0.1 %新乡: 0.1 %新乡: 0.1 %新余: 0.1 %新余: 0.1 %无锡: 0.1 %无锡: 0.1 %昆明: 1.9 %昆明: 1.9 %朝阳: 0.2 %朝阳: 0.2 %本溪: 0.1 %本溪: 0.1 %杭州: 2.2 %杭州: 2.2 %格林维尔: 0.1 %格林维尔: 0.1 %武汉: 0.6 %武汉: 0.6 %永州: 0.1 %永州: 0.1 %汕头: 0.4 %汕头: 0.4 %江门: 0.1 %江门: 0.1 %沈阳: 0.2 %沈阳: 0.2 %沧州: 0.1 %沧州: 0.1 %河源: 0.2 %河源: 0.2 %洛杉矶: 0.1 %洛杉矶: 0.1 %洛阳: 0.2 %洛阳: 0.2 %济南: 0.3 %济南: 0.3 %海口: 0.2 %海口: 0.2 %淄博: 0.1 %淄博: 0.1 %淮南: 0.1 %淮南: 0.1 %深圳: 2.2 %深圳: 2.2 %温州: 0.5 %温州: 0.5 %湖州: 0.1 %湖州: 0.1 %湘潭: 0.1 %湘潭: 0.1 %漯河: 0.7 %漯河: 0.7 %潍坊: 0.1 %潍坊: 0.1 %烟台: 0.1 %烟台: 0.1 %珠海: 0.4 %珠海: 0.4 %白城: 0.1 %白城: 0.1 %百色: 0.2 %百色: 0.2 %石家庄: 0.3 %石家庄: 0.3 %福州: 0.2 %福州: 0.2 %纽约: 0.6 %纽约: 0.6 %绵阳: 1.4 %绵阳: 1.4 %罗马: 0.2 %罗马: 0.2 %芒廷维尤: 10.5 %芒廷维尤: 10.5 %芝加哥: 1.1 %芝加哥: 1.1 %苏州: 0.5 %苏州: 0.5 %莫斯科: 0.3 %莫斯科: 0.3 %营口: 0.1 %营口: 0.1 %衡水: 0.5 %衡水: 0.5 %衡阳: 0.2 %衡阳: 0.2 %衢州: 0.2 %衢州: 0.2 %襄阳: 0.1 %襄阳: 0.1 %西宁: 3.2 %西宁: 3.2 %西安: 3.8 %西安: 3.8 %诺沃克: 5.5 %诺沃克: 5.5 %贵阳: 0.3 %贵阳: 0.3 %赣州: 0.1 %赣州: 0.1 %运城: 0.3 %运城: 0.3 %通辽: 0.1 %通辽: 0.1 %遵义: 0.1 %遵义: 0.1 %邯郸: 0.2 %邯郸: 0.2 %邵阳: 0.2 %邵阳: 0.2 %郑州: 0.3 %郑州: 0.3 %重庆: 1.1 %重庆: 1.1 %金昌: 0.1 %金昌: 0.1 %长春: 0.2 %长春: 0.2 %长沙: 2.2 %长沙: 2.2 %阜新: 0.1 %阜新: 0.1 %阿什本: 0.2 %阿什本: 0.2 %阿姆斯特丹: 0.1 %阿姆斯特丹: 0.1 %陇南: 0.1 %陇南: 0.1 %陵水: 0.1 %陵水: 0.1 %青岛: 0.8 %青岛: 0.8 %首尔特别: 0.2 %首尔特别: 0.2 %香港: 0.2 %香港: 0.2 %马尔默: 0.1 %马尔默: 0.1 %马尼拉: 0.2 %马尼拉: 0.2 %驻马店: 0.1 %驻马店: 0.1 %黄石: 0.1 %黄石: 0.1 %齐齐哈尔: 0.5 %齐齐哈尔: 0.5 %其他其他Central DistrictChinaEcole-ValentinFalls ChurchHerndonRussian Federation上海东莞伦敦佛山六安兰州内江列克星敦加利福尼亚州北京十堰南京南宁南昌南通印多尔厦门双鸭山台北台州合肥吉安呼和浩特哈尔滨哥伦布嘉兴圣克拉拉圣安东尼奥大理大连大阪天津太原威海宁波安康安顺宜春宣城宿州常州常德广安广州库比蒂诺延安开封张家口张家界德里德黑兰慕尼黑成都扬州揭阳新乡新余无锡昆明朝阳本溪杭州格林维尔武汉永州汕头江门沈阳沧州河源洛杉矶洛阳济南海口淄博淮南深圳温州湖州湘潭漯河潍坊烟台珠海白城百色石家庄福州纽约绵阳罗马芒廷维尤芝加哥苏州莫斯科营口衡水衡阳衢州襄阳西宁西安诺沃克贵阳赣州运城通辽遵义邯郸邵阳郑州重庆金昌长春长沙阜新阿什本阿姆斯特丹陇南陵水青岛首尔特别香港马尔默马尼拉驻马店黄石齐齐哈尔

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
    Article views(1580) PDF downloads(517) Cited by(22)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint