Volume 12 Issue 6
Dec.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(6): 1125-1137
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: CHEN Hongmeng, YU Jizhou, ZHANG Wenjie, et al. 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
shu

Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario

DOI: 10.12000/JR23080 CSTR: 32380.14.JR23080
  • 1.

    Beijing Institute of Radio Measurement, Beijing 100854, China

  • 2.

    National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (62101396, 62171337)
More Information
  • Corresponding author: YU Jizhou, 2917161774@qq.com; LU Yaobing, luyaobing65@163.com
  • Received Date: 2023-05-09
  • Rev Recd Date: 2023-08-15
    • Available Online: 2023-08-15
  • Publish Date: 2023-09-06
    Abstract
  • Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.

     

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  • [1]
    SKOLNIK M I. Radar Handbook[M]. 3rd ed. New York, USA: McGraw-Hill, 2008: 23.1–23.36.
    [2]
    保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005, 2–18.

    BAO Zheng, XING Mengdao, and WANG Tong. Radar Imaging Technique[M]. Beijing, China: Publishing House of Electronics Industry, 2005, 2–18.
    [3]
    RICHARDS M A. Fundamentals of Radar Signal Processing[M]. New York, USA: McGraw-Hill, 2005, 385–401.
    [4]
    CUMMING I G and WONG F H. Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation[M]. Boston, USA: Artech House, 2005, 3–17.
    [5]
    LONG Teng, LU Zheng, DING Zegang, et al. A DBS Doppler centroid estimation algorithm based on entropy minimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3703–3712. doi: 10.1109/TGRS.2011.2142316
    [6]
    李亚超, 王家东, 张廷豪, 等. 弹载雷达成像技术发展现状与趋势[J]. 雷达学报, 2022, 11(6): 943–973. doi: 10.12000/JR22119

    LI Yachao, WANG Jiadong, ZHANG Tinghao, et al. Present situation and prospect of missile-borne radar imaging technology[J]. Journal of Radars, 2022, 11(6): 943–973. doi: 10.12000/JR22119
    [7]
    CHEN Hongmeng, LI Ming, WANG Zeyu, et al. Cross-range resolution enhancement for DBS imaging in a scan mode using aperture-extrapolated sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1459–1463. doi: 10.1109/LGRS.2017.2710082
    [8]
    MOREIRA A and HUANG Yonghong. Airborne SAR processing of highly squinted data using a chirp scaling approach with integrated motion compensation[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1029–1040. doi: 10.1109/36.312891
    [9]
    MOREIRA A, PRATS-IRAOLA P, YOUNISM, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6–43. doi: 10.1109/MGRS.2013.2248301
    [10]
    LI Zhenyu, XING Mengdao, LIANG Yi, et al. A frequency-domain imaging algorithm for highly squinted SAR mounted on maneuvering platforms with nonlinear trajectory[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 4023–4038. doi: 10.1109/TGRS.2016.2535391
    [11]
    RAN Lei, LIU Zheng, ZHANG Lei, et al. An autofocus algorithm for estimating residual trajectory deviations in synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3408–3425. doi: 10.1109/TGRS.2017.2670785
    [12]
    SUN Guangcai, LIU Yanbin, XIANG Jixiang, et al. Spaceborne synthetic aperture radar imaging algorithms: An overview[J]. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(1): 161–184. doi: 10.1109/MGRS.2021.3097894
    [13]
    LU Jingyue, ZHANG Lei, WEI Shaopeng, et al. Resolution enhancement for forwarding looking multi-channel SAR imagery with exploiting space-time sparsity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5200617. doi: 10.1109/TGRS.2022.3232392
    [14]
    WU Junjie, LI Zhongyu, HUANG Yulin, et al. Focusing bistatic forward-looking SAR with stationary transmitter based on Keystone transform and nonlinear chirp scaling[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 148–152. doi: 10.1109/LGRS.2013.2250904
    [15]
    ZENG Tao, WANG Rui, LI Feng, et al. A modified nonlinear chirp scaling algorithm for spaceborne/stationary bistatic SAR based on series reversion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 3108–3118. doi: 10.1109/TGRS.2012.2219057
    [16]
    MENG Ziqiang, LI Yachao, LI Chunbiao, et al. A raw data simulator for bistatic forward-looking high-speed maneuvering-platform SAR[J]. Signal Processing, 2015, 117: 151–164. doi: 10.1016/j.sigpro.2015.05.008
    [17]
    PU Wei, WU Junjie, HUANG Yulin, et al. Fast factorized backprojection imaging algorithm integrated with motion trajectory estimation for bistatic forward-looking SAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(10): 3949–3965. doi: 10.1109/JSTARS.2019.2945118
    [18]
    梅海文, 孟自强, 李亚超, 等. 双基前视SAR几何定位及同步误差分析[J]. 电子与信息学报, 2018, 40(4): 882–889. doi: 10.11999/JEIT170677

    MEI Haiwen, MENG Ziqiang, LI Yachao, et al. Bistatic forward-looking SAR geometrical positioning and analysis of synchronization error[J]. Journal of Electronics &Information Technology, 2018, 40(4): 882–889. doi: 10.11999/JEIT170677
    [19]
    LI Yachao, ZHANG Tinghao, MEI Haiwen, et al. Focusing translational-variant bistatic forward-looking SAR data using the modified Omega-K algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5203916. doi: 10.1109/TGRS.2021.3063780
    [20]
    LIU Zhutian, LI Zhongyu, YU Huaiqin, et al. Bistatic forward-looking SAR moving target detection method based on joint clutter cancellation in echo-image domain with three receiving channels[J]. Sensors, 2018, 18(11): 3835. doi: 10.3390/s18113835
    [21]
    LI Yachao, XU Gaotian, ZHOU Song, et al. A novel CFFBP algorithm with noninterpolation image merging for bistatic forward-looking SAR focusing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5225916. doi: 10.1109/TGRS.2022.3162230
    [22]
    吴迪, 杨成杰, 朱岱寅, 等. 一种用于单脉冲成像的自聚焦算法[J]. 电子学报, 2016, 44(8): 1962–1968. doi: 10.3969/j.issn.0372-2112.2016.08.027

    WU Di, YANG Chengjie, ZHU Daiyin, et al. An autofocusing algorithm for monopulse imaging[J]. Acta Electronica Sinica, 2016, 44(8): 1962–1968. doi: 10.3969/j.issn.0372-2112.2016.08.027
    [23]
    CHEN Hongmeng, LU Yaobing, MU Heqiang, et al. Knowledge-aided mono-pulse forward-looking imaging for airborne radar by exploiting the antenna pattern information[J]. Electronics Letters, 2017, 53(8): 566–568. doi: 10.1049/el.2017.0324
    [24]
    李悦丽, 马萌恩, 赵崇辉, 等. 基于单脉冲雷达和差通道多普勒估计的前视成像[J]. 雷达学报, 2021, 10(1): 131–142. doi: 10.12000/JR20111

    LI Yueli, MA Meng’en, ZHAO Chonghui, et al. Forward-looking imaging via Doppler estimates of sum-difference measurements in scanning monopulse radar[J]. Journal of Radars, 2021, 10(1): 131–142. doi: 10.12000/JR20111
    [25]
    杨志伟, 贺顺, 廖桂生. 机载单通道雷达实波束扫描的前视探测[J]. 航空学报, 2012, 33(12): 2240–2245.

    YANG Zhiwei, HE Shun, and LIAO Guisheng. Forward-looking detection for airborne single-channel radar with beam scanning[J]. Acta Aeronauticaet AstronauticaSinica, 2012, 33(12): 2240–2245.
    [26]
    王军, 赵宜楠, 乔晓林. 基于压缩感知的雷达前视向稀疏目标分辨[J]. 电子与信息学报, 2014, 36(8): 1978–1984. doi: 10.3724/SP.J.1146.2013.01936

    WANG Jun, ZHAO Yin’an, and QIAO Xiaolin. A sparse target-scenario determination strategy based on compressive sensing for active radar in the line of sight[J]. Journal of Electronics &Information Technology, 2014, 36(8): 1978–1984. doi: 10.3724/SP.J.1146.2013.01936
    [27]
    温晓杨, 匡纲要, 胡杰民, 等. 基于实波束扫描的相控阵雷达前视成像[J]. 航空学报, 2014, 35(7): 1977–1991. doi: 10.7527/S1000-6893.2013.0545

    WEN Xiaoyang, KUANG Gangyao, HU Jiemin, et al. Forward-looking imaging based on real beam scanning phased array radars[J]. Acta Aeronauticaet AstronauticaSinica, 2014, 35(7): 1977–1991. doi: 10.7527/S1000-6893.2013.0545
    [28]
    RICHARDS M A. Iterative noncoherent angular superresolution (radar)[C]. 1988 IEEE National Radar Conference, Ann Arbor, USA, 1988: 100–105.
    [29]
    李悦丽, 梁甸农, 黄晓涛. 一种单脉冲雷达多通道解卷积前视成像方法[J]. 信号处理, 2007, 23(5): 699–703. doi: 10.3969/j.issn.1003-0530.2007.05.013

    LI Yueli, LIANG Diannong, and HUANG Xiaotao. A multi-channel deconvolution based on forword-looking imaging method in monopulse radar[J]. Signal Processing, 2007, 23(5): 699–703. doi: 10.3969/j.issn.1003-0530.2007.05.013
    [30]
    陈洪猛, 李明, 王泽玉, 等. 基于多帧数据联合处理的机载单通道雷达贝叶斯前视成像[J]. 电子与信息学报, 2015, 37(10): 2328–2334. doi: 10.11999/JEIT150153

    CHEN Hongmeng, LI Ming, WANG Zeyu, et al. Bayesian forward-looking imaging for airborne single-channel radar based on combined multiple frames data[J]. Journal of Electronics &Information Technology, 2015, 37(10): 2328–2334. doi: 10.11999/JEIT150153
    [31]
    GAMBARDELLA A and MIGLIACCIO M. On the superresolution of microwave scanning radiometer measurements[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 796–800. doi: 10.1109/LGRS.2008.2006285
    [32]
    CHEN Hongmeng, LI Ming, WANG Zeyu, et al. Sparse super-resolution imaging for airborne single channel forward-looking radar in expanded beam space via l p regularisation[J]. Electronics Letters, 2015, 51(11): 863–865. doi: 10.1049/el.2014.3978
    [33]
    ZHANG Yongchao, JAKOBSSON A, ZHANG Yin, et al. Wideband sparse reconstruction for scanning radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 6055–6068. doi: 10.1109/TGRS.2018.2830100
    [34]
    LI Yueli, LIU Jianguo, JIANG Xiaoqing, et al. Angular superresol for signal model in coherent scanning radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(6): 3103–3116. doi: 10.1109/TAES.2019.2900133
    [35]
    HUANG Yulin, ZHA Yuebo, WANG Yue, et al. Forward looking radar imaging by truncated singular value decomposition and its application for adverse weather aircraft landing[J]. Sensors, 2015, 15(6): 14397–14414. doi: 10.3390/s150614397
    [36]
    TUO Xingyu, ZHANG Yin, HUANG Yulin, et al. Fast sparse-TSVD super-resolution method of real aperture radar forward-looking imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(8): 6609–6620. doi: 10.1109/TGRS.2020.3027053
    [37]
    ZHANG Yongchao, ZHANG Yin, LI Wenchao, et al. Super-resolution surfacemapping for scanning radar: Inverse filtering based on the fast iterativeadaptive approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 127–144. doi: 10.1109/TGRS.2017.2743263
    [38]
    ÇETIN M and KARL W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization[J]. IEEE Transactions on Image Processing, 2001, 10(4): 623–631. doi: 10.1109/83.913596
    [39]
    ZHANG Yin, TUO Xingyu, HUANG Yulin, et al. A TV forward-looking super-resolution imaging method based on TSVD strategy for scanning radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4517–4528. doi: 10.1109/TGRS.2019.2958085
    [40]
    ZHANG Qiping, ZHANG Yin, HUANG Yulin, et al. TV-sparse super-resolution method for radar forward-looking imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(9): 6534–6549. doi: 10.1109/TGRS.2020.2977719
    [41]
    YANG Jianyu, KANG Yao, ZHANG Yin, et al. A Bayesian angular superresolution method with lognormal constraint for sea-surface target[J]. IEEE Access, 2020, 8: 13419–13428. doi: 10.1109/ACCESS.2020.2965973
    [42]
    ZHANG Yin, ZHANG Qiping, LI Changlin, et al. Sea-Surface target angular superresolution in forward-looking radar imaging based on maximum a posteriori algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8): 2822–2834. doi: 10.1109/JSTARS.2019.2918189
    [43]
    ZHANG Qiping, ZHANG Yin, HUANG Yulin, et al. Azimuth superresolutionof forward-looking radar imaging which relies on linearized Bregman[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(7): 2032–2043. doi: 10.1109/JSTARS.2019.2912993
    [44]
    CHEN Hongmeng, LI Yachao, GAO Wenquan, et al. Bayesian forward-looking superresolution imaging using Doppler deconvolution in expanded beam space for high-speed platform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5105113. doi: 10.1109/TGRS.2021.3107717
    [45]
    LI Weixin, LI Ming, ZUO Lei, et al. Real aperture radar forward-looking imaging based on variational Bayesian in presence of outliers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5117113. doi: 10.1109/TGRS.2022.3203807
    [46]
    ZHANG Yin, SHEN Jiahao, TUO Xingyu, et al. Scanning radar forward-looking superresolution imaging based on the Weibull distribution for a sea-surface target[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5116111. doi: 10.1109/TGRS.2022.3194118
    [47]
    LI Weixin, LI Ming, ZUO Lei, et al. A computationally efficient airborne forward-looking super-resolution imaging method based on sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5102613. doi: 10.1109/TGRS.2023.3260094
    [48]
    XU Gang, XING Mengdao, XIA Xianggen, et al. Sparse regularization of interferometric phase and amplitude for InSAR image formation based on Bayesian representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2123–2136. doi: 10.1109/TGRS.2014.2355592
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    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

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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