Volume 11 Issue 5
Oct.  2022
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2022  >  11(5): 794-804
Yang Jin-long, Liu Feng-mei, Wang Dong, Ge Hong-wei. Affinity Propagation Based Measurement Partition Algorithm for Multiple Extended Target Tracking[J]. Journal of Radars, 2015, 4(4): 452-459. doi: 10.12000/JR15003
Citation: WAN Xianrong, XIE Deqiang, YI Jianxin, et al. Micro-Doppler clutter removal method based on the cancelation of sliding STFT spectrogram[J]. Journal of Radars, 2022, 11(5): 794–804. doi: 10.12000/JR22157
shu

Micro-Doppler Clutter Removal Method Based on the Cancelation of Sliding STFT Spectrogram

DOI: 10.12000/JR22157

  • School of Electronic Information, Wuhan University, Wuhan 430072, China

Funds:  The National Natural Science Foundation of China (61931015, 62071335), The Technological Innovation Project of Hubei Province of China (2019AAA061), The Innovation Group Project of Natural Science Foundation of Hubei Province (2021CFA002)
More Information
  • Corresponding author: WAN Xianrong, xrwan@whu.edu.cn
  • Received Date: 2022-07-21
  • Rev Recd Date: 2022-09-09
    • Available Online: 2022-09-14
  • Publish Date: 2022-09-29
    Abstract
  • Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo.

     

    • FullText(HTML)
    • References(27)
  • [1]
    CHEN V C, LI Fayin, HO S S, et al. Micro-Doppler effect in radar: Phenomenon, model, and simulation study[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 2–21. doi: 10.1109/TAES.2006.1603402
    [2]
    张群, 胡健, 罗迎, 等. 微动目标雷达特征提取、成像与识别研究进展[J]. 雷达学报, 2018, 7(5): 531–547. doi: 10.12000/JR18049

    ZHANG Qun, HU Jian, LUO Ying, et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions[J]. Journal of Radars, 2018, 7(5): 531–547. doi: 10.12000/JR18049
    [3]
    苏宁远, 陈小龙, 关键, 等. 基于卷积神经网络的海上微动目标检测与分类方法[J]. 雷达学报, 2018, 7(5): 565–574. doi: 10.12000/JR18077

    SU Ningyuan, CHEN Xiaolong, GUAN Jian, et al. Detection and classification of maritime target with micro-motion based on CNNs[J]. Journal of Radars, 2018, 7(5): 565–574. doi: 10.12000/JR18077
    [4]
    LI Gang, ZHANG Rui, RITCHIE M, et al. Sparsity-driven micro-Doppler feature extraction for dynamic hand gesture recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(2): 655–665. doi: 10.1109/TAES.2017.2761229
    [5]
    JACKSON C A. Windfarm characteristics and their effect on radar systems[C]. 2007 IET International Conference on Radar Systems, Edinburgh, UK, 2007: 1–6.
    [6]
    何炜琨, 吴仁彪, 王晓亮, 等. 风电场对雷达设备的影响评估与干扰抑制技术研究现状与展望[J]. 电子与信息学报, 2017, 39(7): 1748–1758. doi: 10.11999/JEIT161004

    HE Weikun, WU Renbiao, WANG Xiaoliang, et al. The review and prospect on the influence evaluation and interference suppression of wind farms on the radar equipment[J]. Journal of Electronic &Information Technology, 2017, 39(7): 1748–1758. doi: 10.11999/JEIT161004
    [7]
    TUSZYNSKI M, WOJTKIEWICZ A, and KLEMBOWSKI W. Bimodal clutter MTI filter for staggered PRF radars[C]. IEEE International Conference on Radar, Arlington, USA, 1990: 176–180.
    [8]
    COLONE F, O’HAGAN D W, LOMBARDO P, et al. A multistage processing algorithm for disturbance removal and target detection in passive bistatic radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(2): 698–722. doi: 10.1109/TAES.2009.5089551
    [9]
    ZHAN Weijie, YI Jianxin, and WAN Xianrong. Recognition and mitigation of micro-Doppler clutter in radar systems via support vector machine[J]. IEEE Sensors Journal, 2020, 20(2): 918–930. doi: 10.1109/JSEN.2019.2943152
    [10]
    夏鹏, 万显荣, 易建新. 外辐射源雷达目标旋转部件微动参数估计[J]. 电波科学学报, 2016, 31(4): 676–682. doi: 10.13443/j.cjors.2015082101

    XIA Peng, WAN Xianrong, and YI Jianxin. Micromotion parameters estimation for rotating structures on target in passive radar[J]. Chinese Journal of Radio Science, 2016, 31(4): 676–682. doi: 10.13443/j.cjors.2015082101
    [11]
    胡旭超, 谭贤四, 曲智国, 等. 风电场雷达杂波动态重构抑制方法[J]. 航空学报, 2020, 41(1): 323269. doi: 10.7527/S1000-6893.2019.23269

    HU Xuchao, TAN Xiansi, QU Zhiguo, et al. Wind turbine clutter suppression method based on dynamic reconstruction[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(1): 323269. doi: 10.7527/S1000-6893.2019.23269
    [12]
    STANKOVIC L, DAKOVIC M, THAYAPARAN T, et al. Inverse radon transform-based micro-Doppler analysis from a reduced set of observations[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 1155–1169. doi: 10.1109/TAES.2014.140098
    [13]
    何其芳, 张群, 罗迎, 等. 正弦调频Fourier-Bessel变换及其在微动目标特征提取中的应用[J]. 雷达学报, 2018, 7(5): 593–601. doi: 10.12000/JR17069

    HE Qifang, ZHANG Qun, LUO Ying, et al. A sinusoidal frequency modulation Fourier-Bessel transform and its application to micro-Doppler feature extraction[J]. Journal of Radars, 2018, 7(5): 593–601. doi: 10.12000/JR17069
    [14]
    LI Hongzhi, ZHANG Wenxuan, WANG Yong, et al. Method for micro-Doppler separation in ISAR imaging based on discrete sinusoidal frequency-modulated transform[J]. Journal of Applied Remote Sensing, 2020, 14(3): 036502. doi: 10.1117/1.JRS.14.036502
    [15]
    BAI Xueru, XING Mengdao, ZHOU Feng, et al. Imaging of micromotion targets with rotating parts based on empirical-mode decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3514–3523. doi: 10.1109/TGRS.2008.2002322
    [16]
    YUAN Bin, CHEN Zengping, and XU Shiyou. Micro-Doppler analysis and separation based on complex local mean decomposition for aircraft with fast-rotating parts in ISAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1285–1298. doi: 10.1109/TGRS.2013.2249588
    [17]
    CHOI I, KANG K, KIM K, et al. Use of ICA to separate micro-Doppler signatures in ISAR images of aircraft that has fast-rotating parts[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(1): 234–246. doi: 10.1109/TAES.2021.3098110
    [18]
    罗亚宗, 冯有前, 李松, 等. 基于距离最近原则的微多普勒曲线分离和特征提取[J]. 科学技术与工程, 2014, 14(30): 186–190. doi: 10.3969/j.issn.1671-1815.2014.30.038

    LUO Yazong, FENG Youqian, LI Song, et al. Micro-Doppler curve separation and feature extraction based on the principle of the closest distance[J]. Science Technoloy and Engineering, 2014, 14(30): 186–190. doi: 10.3969/j.issn.1671-1815.2014.30.038
    [19]
    彭正红, 杨德贵, 王行, 等. 基于趋势估计的微多普勒分离与特征提取算法[J]. 系统工程与电子技术, 2021, 43(12): 3452–3461. doi: 10.12305/j.issn.1001-506X.2021.12.05

    PENG Zhenghong, YANG Degui, WANG Xing, et al. Micro-Doppler separation and feature extraction algorithm based on trend estimation[J]. System Engineering and Electronic, 2021, 43(12): 3452–3461. doi: 10.12305/j.issn.1001-506X.2021.12.05
    [20]
    STANKOVIC L, THAYAPARAN T, DAKOVIC M, et al. Micro-Doppler removal in the radar imaging analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2): 1234–1250. doi: 10.1109/TAES.2013.6494410
    [21]
    STANKOVIĆ L, POPOVIĆ-BUGARIN V, and RADENOVIĆ F. Genetic algorithm for rigid body reconstruction after micro-Doppler removal in the radar imaging analysis[J]. Signal Processing, 2013, 93(7): 1921–1932. doi: 10.1016/j.sigpro.2013.01.005
    [22]
    ALLEN J. Short term spectral analysis, synthesis, and modification by discrete Fourier transform[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1977, 25(3): 235–238. doi: 10.1109/TASSP.1977.1162950
    [23]
    STANKOVIĆ L, DAKOVIĆ M, and THAYAPARAN T. Time-Frequency signal analysis with applications[M]. Boston: Artech House, 2013: 81–96.
    [24]
    BOASHASH B. Estimating and interpreting the instantaneous frequency of a signal-I. Fundamentals[J]. Proceedings of the IEEE, 1992, 80(4): 520–538. doi: 10.1109/5.135376
    [25]
    陈永彬, 李少东, 杨军, 等. 旋翼叶片回波建模与闪烁现象机理分析[J]. 物理学报, 2016, 65(13): 138401. doi: 10.7498/aps.65.138401

    CHEN Yongbin, LI Shaodong, YANG Jun, et al. Rotor blades echo modeling and mechanism analysis of flashes phenomena[J]. Acta Physica Sinica, 2016, 65(13): 138401. doi: 10.7498/aps.65.138401
    [26]
    占伟杰, 万显荣, 易建新, 等. 外辐射源雷达目标扇叶微多普勒效应实验研究[J]. 系统工程与电子技术, 2021, 43(6): 1468–1476. doi: 10.12305/j.issn.1001-506X.2021.06.03

    ZHAN Weijie, WAN Xianrong, YI Jianxin, et al. Experimental study on micro-Doppler effect of target blades in passive radar[J]. Systems Engineering and Electronics, 2021, 43(6): 1468–1476. doi: 10.12305/j.issn.1001-506X.2021.06.03
    [27]
    CRESPO-BALLESTEROS M, ANTONIOU M, and CHERNIAKOV M. Wind turbine blade radar signatures in the near field: Modeling and experimental confirmation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(4): 1916–1931. doi: 10.1109/TAES.2017.2675241
    • Relative Articles

  • Relative Articles

    [1]WANG Bohong, SHEN Biao, MU Wenxing, LIU Tao. Research on Super-resolution Methods for Radar Targets Based on Bat-inspired Spectrogram Correlation and Transformation Models[J]. Journal of Radars, 2025, 14(2): 293-308. doi: 10.12000/JR24239
    [2]GUO Zhongyi, WANG Yanzhe, WANG Yunlai, GUO Kai. Research Advances on the Rotational Doppler Effect of Vortex Electromagnetic Waves[J]. Journal of Radars, 2021, 10(5): 725-739. doi: 10.12000/JR21109
    [3]PEI Jiazheng, HUANG Yong, CHEN Baoxin, GUAN Jian, CAI Mi, CHEN Xiaolong. Long Time Coherent Integration Method Based on Combining Pulse Compression and Radon-Fourier Transform[J]. Journal of Radars, 2021, 10(6): 956-969. doi: 10.12000/JR21068
    [4]WEI Wei, ZHU Daiyin, WU Di. Wavenumber Domain Algorithm Based on the Principle of Chirp Scaling for SAR Imaging[J]. Journal of Radars, 2020, 9(2): 354-362. doi: 10.12000/JR19112
    [5]Li Yuqian, Yi Jianxin, Wan Xianrong, Liu Yuqi, Zhan Weijie. Helicopter Rotor Parameter Estimation Method for Passive Radar[J]. Journal of Radars, 2018, 7(3): 313-319. doi: 10.12000/JR17125
    [6]He Qifang, Zhang Qun, Luo Ying, Li Kaiming. A Sinusoidal Frequency Modulation Fourier-Bessel Transform and its Application to Micro-Doppler Feature Extraction[J]. Journal of Radars, 2018, 7(5): 593-601. doi: 10.12000/JR17069
    [7]Chen Fangxiang, Yi Wei, Zhou Tao, Kong Lingjiang. Passive Direct Location Determination for Multiple Sources Based on FRFT[J]. Journal of Radars, 2018, 7(4): 523-530. doi: 10.12000/JR18027
    [8]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
    [9]Liu Xiangyang, Yang Jungang, Meng Jin, Zhang Xiao, Niu Dezhi. Sparse Three-dimensional Imaging Based on Hough Transform for Forward-looking Array SAR in Low SNR[J]. Journal of Radars, 2017, 6(3): 316-323. doi: 10.12000/JR17011
    [10]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
    [11]Lin Chunfeng, Huang Chunlin, Su Yi. Target Integration and Detection with the Radon-Fourier Transform for Bistatic Radar[J]. Journal of Radars, 2016, 5(5): 526-530. doi: 10.12000/JR16049
    [12]Qin Yao, Huang Chun-lin, Lu Min, Xu Wei. Adaptive clutter reduction based on wavelet transform and principal component analysis for ground penetrating radar[J]. Journal of Radars, 2015, 4(4): 445-451. doi: 10.12000/JR15013
    [13]Du Lan, Li Lin-sen, Li Wei-lu, Wang Bao-shuai, Shi Hui-ruo. Aircraft Target Classification Based on Correlation Features from Time-domain Echoes[J]. Journal of Radars, 2015, 4(6): 621-629. doi: 10.12000/JR15117
    [14]Tian Rui-qi, Bao Qing-long, Wang Ding-he, Chen Zeng-ping. An Algorithm for Target Parameter Estimation Based on Fractional Fourier and Keystone Transforms[J]. Journal of Radars, 2014, 3(5): 511-517. doi: 10.3724/SP.J.1300.2014.14058
    [15]Zhe Xiao-qiang, Chou Xiao-lan, Han Bing, Lei Bin. An Improved Doppler Rate Estimation Approach for Sliding Spotlight SAR Data Based on the Transposition Domain[J]. Journal of Radars, 2014, 3(4): 419-427. doi: 10.3724/SP.J.1300.2014.14008
    [16]Lu Chuan-guo, Feng Xin-xi, Kong Yun-bo, Zeng Rong, Li Hong-Ying. Track Initiation Based on Parallel Hough Transform[J]. Journal of Radars, 2013, 2(3): 292-299. doi: 10.3724/SP.J.1300.2013.13036
    [17]Guan Xin, Zhong Li-hua, Hu Dong-hui, Ding Chi-biao. A Compensation Algorithm Based on RSPWVD-Hough Transform for Doppler Expansion in Passive Radar[J]. Journal of Radars, 2013, 2(4): 430-438. doi: 10.3724/SP.J.1300.2013.13073
    [18]Deng Dong-hu, Zhang Qun, Luo Ying, Li Song, Zhu Ren-Fei. Resolution and Micro-Doppler Effect in Bi-ISAR System (in English)[J]. Journal of Radars, 2013, 2(2): 152-167. doi: 10.3724/SP.J.1300.2013.13039
    [19]Zheng Jin, You Hong-jian. Change Detection with SAR Images Based on Radon Transform and Jeffrey Divergence[J]. Journal of Radars, 2012, 1(2): 182-189. doi: 10.3724/SP.J.1300.2012.10068
    [20]Yu Bin-bin, Liu Chang, Wang Yan-fei. A Non-linear Scaling Algorithm Based on chirp-z Transform for Squint Mode FMCW-SAR[J]. Journal of Radars, 2012, 1(1): 69-75. doi: 10.3724/SP.J.1300.2012.20005
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(3)

    1. 张美晨，赵丽娟，李明昊，田震. 基于双向耦合法的采煤机螺旋滚筒振动特性分析. 煤炭科学技术. 2024(03): 200-216 .
    2. 李中余，桂亮，海宇，武俊杰，王党卫，王安乐，杨建宇. 基于变分模态分解与优选的超高分辨ISAR成像微多普勒抑制方法. 雷达学报. 2024(04): 852-865 . 本站查看
    3. 卓智海，祝文胜，王双龙. 基于双注意力机制的FMCW雷达人体行为识别. 北京信息科技大学学报(自然科学版). 2024(05): 58-66 .

    Other cited types(6)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04020406080
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 30.8 %FULLTEXT: 30.8 %META: 59.9 %META: 59.9 %PDF: 9.3 %PDF: 9.3 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 5.4 %其他: 5.4 %其他: 2.2 %其他: 2.2 %Central District: 0.0 %Central District: 0.0 %China: 0.3 %China: 0.3 %Herndon: 0.0 %Herndon: 0.0 %Kao-sung: 0.0 %Kao-sung: 0.0 %Malvern: 0.0 %Malvern: 0.0 %North Point: 0.1 %North Point: 0.1 %San Lorenzo: 0.1 %San Lorenzo: 0.1 %Seattle: 0.1 %Seattle: 0.1 %[]: 0.4 %[]: 0.4 %三亚: 0.1 %三亚: 0.1 %三明: 0.1 %三明: 0.1 %上海: 3.0 %上海: 3.0 %东京: 0.2 %东京: 0.2 %东京都: 0.0 %东京都: 0.0 %东莞: 0.3 %东莞: 0.3 %中卫: 0.1 %中卫: 0.1 %临汾: 0.1 %临汾: 0.1 %临沂: 0.1 %临沂: 0.1 %丹东: 0.1 %丹东: 0.1 %九江: 0.0 %九江: 0.0 %伊利诺伊州: 0.0 %伊利诺伊州: 0.0 %伦敦: 0.2 %伦敦: 0.2 %佛山: 0.0 %佛山: 0.0 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.0 %兰辛: 0.0 %凤凰城: 0.1 %凤凰城: 0.1 %北京: 10.5 %北京: 10.5 %北海: 0.0 %北海: 0.0 %十堰: 0.0 %十堰: 0.0 %华沙: 0.1 %华沙: 0.1 %南京: 2.1 %南京: 2.1 %南充: 0.0 %南充: 0.0 %南昌: 0.1 %南昌: 0.1 %南通: 0.0 %南通: 0.0 %卡拉奇: 0.0 %卡拉奇: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.9 %台北: 0.9 %台州: 0.0 %台州: 0.0 %合肥: 0.5 %合肥: 0.5 %吉安: 0.0 %吉安: 0.0 %呼和浩特: 0.0 %呼和浩特: 0.0 %咸阳: 0.1 %咸阳: 0.1 %哈密: 0.1 %哈密: 0.1 %哈尔滨: 0.2 %哈尔滨: 0.2 %哥伦布: 0.0 %哥伦布: 0.0 %唐山: 0.3 %唐山: 0.3 %嘉兴: 0.1 %嘉兴: 0.1 %大克罗伊茨: 0.6 %大克罗伊茨: 0.6 %大连: 0.2 %大连: 0.2 %天津: 1.2 %天津: 1.2 %太原: 0.4 %太原: 0.4 %威海: 0.4 %威海: 0.4 %宁波: 0.1 %宁波: 0.1 %安山: 0.1 %安山: 0.1 %安康: 0.3 %安康: 0.3 %宝鸡: 0.0 %宝鸡: 0.0 %宣城: 0.2 %宣城: 0.2 %宿州: 0.0 %宿州: 0.0 %岳阳: 0.1 %岳阳: 0.1 %常州: 0.4 %常州: 0.4 %常德: 0.1 %常德: 0.1 %广元: 0.0 %广元: 0.0 %广州: 1.2 %广州: 1.2 %库比蒂诺: 0.4 %库比蒂诺: 0.4 %廊坊: 0.0 %廊坊: 0.0 %开普敦: 0.1 %开普敦: 0.1 %张家口: 0.3 %张家口: 0.3 %张家界: 0.1 %张家界: 0.1 %徐州: 0.1 %徐州: 0.1 %德里: 0.1 %德里: 0.1 %怀化: 0.0 %怀化: 0.0 %成都: 1.8 %成都: 1.8 %扬州: 0.5 %扬州: 0.5 %抚顺: 0.0 %抚顺: 0.0 %揭阳: 0.0 %揭阳: 0.0 %新乡: 0.0 %新乡: 0.0 %无锡: 0.1 %无锡: 0.1 %昆明: 0.6 %昆明: 0.6 %昌迪加尔: 0.0 %昌迪加尔: 0.0 %晋城: 0.1 %晋城: 0.1 %朝阳: 0.1 %朝阳: 0.1 %杜塞尔多夫: 0.0 %杜塞尔多夫: 0.0 %杭州: 0.9 %杭州: 0.9 %枣庄: 0.0 %枣庄: 0.0 %格兰特县: 0.0 %格兰特县: 0.0 %格林菲尔德: 0.0 %格林菲尔德: 0.0 %桂林: 0.1 %桂林: 0.1 %梅州: 0.0 %梅州: 0.0 %榆林: 0.3 %榆林: 0.3 %武汉: 2.8 %武汉: 2.8 %汕头: 0.1 %汕头: 0.1 %沈阳: 0.2 %沈阳: 0.2 %沧州: 0.1 %沧州: 0.1 %河内: 0.1 %河内: 0.1 %泉州: 0.0 %泉州: 0.0 %法兰克福: 0.1 %法兰克福: 0.1 %泰州: 0.0 %泰州: 0.0 %泰米尔纳德: 0.3 %泰米尔纳德: 0.3 %泸州: 0.1 %泸州: 0.1 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.3 %济南: 0.3 %海口: 0.0 %海口: 0.0 %淄博: 0.2 %淄博: 0.2 %淮南: 0.0 %淮南: 0.0 %淮安: 0.1 %淮安: 0.1 %深圳: 1.7 %深圳: 1.7 %清远: 0.0 %清远: 0.0 %温州: 0.1 %温州: 0.1 %渭南: 0.1 %渭南: 0.1 %湖州: 0.0 %湖州: 0.0 %滁州: 0.0 %滁州: 0.0 %漯河: 0.8 %漯河: 0.8 %烟台: 0.3 %烟台: 0.3 %珠海: 0.1 %珠海: 0.1 %石家庄: 0.2 %石家庄: 0.2 %秦皇岛: 0.1 %秦皇岛: 0.1 %纳什维尔: 0.1 %纳什维尔: 0.1 %纽约: 0.0 %纽约: 0.0 %绍兴: 0.5 %绍兴: 0.5 %绵阳: 0.2 %绵阳: 0.2 %胡志明: 0.1 %胡志明: 0.1 %舟山: 0.0 %舟山: 0.0 %芒廷维尤: 32.6 %芒廷维尤: 32.6 %芝加哥: 0.3 %芝加哥: 0.3 %苏州: 0.4 %苏州: 0.4 %莫斯科: 0.0 %莫斯科: 0.0 %葫芦岛: 0.0 %葫芦岛: 0.0 %蒙彼利埃: 0.0 %蒙彼利埃: 0.0 %衡阳: 0.3 %衡阳: 0.3 %衢州: 0.1 %衢州: 0.1 %西宁: 9.3 %西宁: 9.3 %西安: 2.1 %西安: 2.1 %许昌: 0.0 %许昌: 0.0 %诺沃克: 0.2 %诺沃克: 0.2 %贵阳: 0.1 %贵阳: 0.1 %赣州: 0.1 %赣州: 0.1 %赤峰: 0.0 %赤峰: 0.0 %赫尔辛基: 0.1 %赫尔辛基: 0.1 %达州: 0.1 %达州: 0.1 %运城: 0.4 %运城: 0.4 %遵义: 0.1 %遵义: 0.1 %邢台: 0.0 %邢台: 0.0 %邯郸: 0.1 %邯郸: 0.1 %郑州: 0.7 %郑州: 0.7 %鄂州: 0.1 %鄂州: 0.1 %重庆: 0.4 %重庆: 0.4 %镇江: 0.1 %镇江: 0.1 %长春: 0.0 %长春: 0.0 %长沙: 1.1 %长沙: 1.1 %随州: 0.0 %随州: 0.0 %雷德蒙德: 0.1 %雷德蒙德: 0.1 %青岛: 0.4 %青岛: 0.4 %韦斯特罗斯: 0.0 %韦斯特罗斯: 0.0 %韦科: 0.1 %韦科: 0.1 %首尔: 0.0 %首尔: 0.0 %首尔特别: 0.0 %首尔特别: 0.0 %香港: 0.1 %香港: 0.1 %香港特别行政区: 0.1 %香港特别行政区: 0.1 %马鞍山: 0.0 %马鞍山: 0.0 %驻马店: 0.0 %驻马店: 0.0 %黄冈: 0.1 %黄冈: 0.1 %黄山: 0.0 %黄山: 0.0 %黄石: 0.3 %黄石: 0.3 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %其他其他Central DistrictChinaHerndonKao-sungMalvernNorth PointSan LorenzoSeattle[]三亚三明上海东京东京都东莞中卫临汾临沂丹东九江伊利诺伊州伦敦佛山兰州兰辛凤凰城北京北海十堰华沙南京南充南昌南通卡拉奇厦门台北台州合肥吉安呼和浩特咸阳哈密哈尔滨哥伦布唐山嘉兴大克罗伊茨大连天津太原威海宁波安山安康宝鸡宣城宿州岳阳常州常德广元广州库比蒂诺廊坊开普敦张家口张家界徐州德里怀化成都扬州抚顺揭阳新乡无锡昆明昌迪加尔晋城朝阳杜塞尔多夫杭州枣庄格兰特县格林菲尔德桂林梅州榆林武汉汕头沈阳沧州河内泉州法兰克福泰州泰米尔纳德泸州洛阳济南海口淄博淮南淮安深圳清远温州渭南湖州滁州漯河烟台珠海石家庄秦皇岛纳什维尔纽约绍兴绵阳胡志明舟山芒廷维尤芝加哥苏州莫斯科葫芦岛蒙彼利埃衡阳衢州西宁西安许昌诺沃克贵阳赣州赤峰赫尔辛基达州运城遵义邢台邯郸郑州鄂州重庆镇江长春长沙随州雷德蒙德青岛韦斯特罗斯韦科首尔首尔特别香港香港特别行政区马鞍山驻马店黄冈黄山黄石齐齐哈尔

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
    Get Citation
    PDF
    XML
    Article views(1631) PDF downloads(251) Cited by(9)
    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