Volume 13 Issue 3
Jun.  2024
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2024  >  13(3): 646-666
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
Citation: ZHANG Yin, ZHANG Ping, TUO Xingyu, et al. Sparse targets angular super-resolution reconstruction method under unknown antenna pattern errors for scanning radar[J]. Journal of Radars, 2024, 13(3): 646–666. doi: 10.12000/JR23208
shu

Sparse Targets Angular Super-resolution Reconstruction Method under Unknown Antenna Pattern Errors for Scanning Radar

DOI: 10.12000/JR23208
  • 1.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China

Funds:  The Natural Science Foundation of Sichuan Province (2023NSFSC1970, 2022NSFSC0950), The Municipal Government of Quzhou under Grant Number (2022D0011, 2022D036, 2023D026)
More Information
  • Corresponding author: ZHANG Yin, yinzhang@uestc.edu.cn
  • Received Date: 2023-10-29
  • Rev Recd Date: 2024-02-17
    • Available Online: 2024-03-02
  • Publish Date: 2024-03-15
    Abstract
  • Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm.

     

    • FullText(HTML)
    • References(34)
  • [1]
    Rigelsford J. Introduction to airborne radar[J]. Sensor Review, 2002, 22(3): 265–266.
    [2]
    张良, 祝欢, 吴涛. 机载预警雷达系统架构发展路径研究[J]. 现代雷达, 2015, 37(12): 11–18. doi: 10.16592/j.cnki.1004-7859.2015.12.003.

    ZHANG Liang, ZHU Huan, and WU Tao. A study on the evolution way of the system architecture of AEW radar[J]. Modern Radar, 2015, 37(12): 11–18. doi: 10.16592/j.cnki.1004-7859.2015.12.003.
    [3]
    CLARKE J, 徐映和, 译. 英国预警雷达的发展概况[J]. 现代雷达, 1987, 9(2): 1–6. doi: 10.16592/j.cnki.1004-7859.1987.02.001.

    CLARKE J, XU Yinghe, translation. Overview of the development of early warning radar in the UK[J]. Modern Radar, 1987, 9(2): 1–6. doi: 10.16592/j.cnki.1004-7859.1987.02.001.
    [4]
    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.
    [5]
    LING Hao. Novel radar techniques and applications[J]. IEEE Antennas and Propagation Magazine, 2018, 60(1): 132–134. doi: 10.1109/MAP.2017.2776153.
    [6]
    BEKKADAL F. Novel radar technology and applications[C]. 17th International Conference on Applied Electromagnetics and Communications, Dubrovnik, Croatia, 2003: 6–12. doi: 10.1109/ICECOM.2003.1290942.
    [7]
    ZHANG Yongchao, ZHANG Yin, LI Wenchao, et al. Super-resolution surface mapping for scanning radar: Inverse filtering based on the fast iterative adaptive approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 127–144. doi: 10.1109/TGRS.2017.2743263.
    [8]
    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.
    [9]
    CHEN Rui, LI Wenchao, LI Kefeng, et al. A super-resolution scheme for multichannel radar forward-looking imaging considering failure channels and motion error[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 3501305. doi: 10.1109/LGRS.2023.3234264.
    [10]
    KANG Yao, ZHANG Yin, MAO Deqing, et al. Bayesian azimuth super-resolution of sea-surface target in forward-looking imaging[C]. 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020: 1–5. doi: 10.1109/RadarConf2043947.2020.9266692.
    [11]
    CHEN Hongmeng, WANG Zeyu, ZHANG Yingjie, et al. Data-driven airborne Bayesian forward-looking superresolution imaging based on generalized Gaussian distribution[J]. Frontiers in Signal Processing, 2023, 3: 1093203. doi: 10.3389/frsip.2023.1093203.
    [12]
    MAO Deqing, ZHANG Yin, ZHANG Yongchao, et al. Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation[J]. Journal of Applied Remote Sensing, 2018, 12(1): 015020. doi: 10.1117/1.JRS.12.015020.
    [13]
    LIU Sijia and PAN Minghai. Research on a forward-looking scanning imaging algorithm for a high-speed radar platform[J]. IET Signal Processing, 2023, 17(6): e12221. doi: 10.1049/sil2.12221.
    [14]
    MAO Deqing, YANG Jianyu, TUO Xingyu, et al. Angular superresolution of real aperture radar for target scale measurement using a generalized hybrid regularization approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5109314. doi: 10.1109/TGRS.2023.3315310.
    [15]
    TUO Xingyu, MAO Deqing, ZHANG Yin, et al. Radar forward-looking super-resolution imaging using a two-step regularization strategy[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 4218–4231. doi: 10.1109/JSTARS.2023.3270309.
    [16]
    YOUNG P. Alternative Recursive Approaches to Time-series Analysis[M]. YOUNG P. Recursive Estimation and Time-Series Analysis: An Introduction. Berlin, Heidelberg: Springer, 1984: 205–230.
    [17]
    RICHARDS M A. Iterative noncoherent angular superresolution (radar)[C]. 1988 IEEE National Radar Conference, Ann Arbor, USA, 1988: 100–105. doi: 10.1109/NRC.1988.10940.
    [18]
    LI Dongye, HUANG Yulin, and YANG Jianyu. Real beam radar imaging based on adaptive Lucy-Richardson algorithm[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 1437–1440. doi: 10.1109/CIE-Radar.2011.6159830.
    [19]
    TAN Ke, LU Xingyu, YANG Jianchao, et al. A novel Bayesian super-resolution method for radar forward-looking imaging based on Markov random field model[J]. Remote Sensing, 2021, 13(20): 4115. doi: 10.3390/rs13204115.
    [20]
    CHEN Hongmeng, LI Yachao, GAO Wenquan, et al. Bayesian forward-looking super-resolution 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.
    [21]
    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.
    [22]
    CAPON J. High-resolution frequency-wavenumber spectrum analysis[J]. Proceedings of the IEEE, 1969, 57(8): 1408–1418. doi: 10.1109/PROC.1969.7278.
    [23]
    ZHANG Yongchao, LI Wenchao, ZHANG Yin, et al. A fast iterative adaptive approach for scanning radar angular superresolution[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(11): 5336–5345. doi: 10.1109/JSTARS.2015.2449090.
    [24]
    LI Yueli, LIU Jianguo, JIANG Xiaoqing, et al. Angular superresolution 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.
    [25]
    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.
    [26]
    HUO Weibo, TUO Xingyu, ZHANG Yin, et al. Balanced tikhonov and total variation deconvolution approach for radar forward-looking super-resolution imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3505805. doi: 10.1109/LGRS.2021.3072389.
    [27]
    TANG Junkui, LIU Zheng, RAN Lei, et al. Enhancing forward-looking image resolution: Combining low-rank and sparsity priors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5100812. doi: 10.1109/TGRS.2023.3237332.
    [28]
    KOZAKOFF D J. Analysis of radome-enclosed antennas[J]. Artech House, 1997 (685): 217–227.
    [29]
    PERSSON K and GUSTAFSSON M. Reconstruction of equivalent currents using a near-field data transformation-with radome applications[J]. Progress in Electromagnetics Research, 2005, 54: 179–198. doi: 10.2528/PIER04111602.
    [30]
    Fierro R D, Golub G H, Hansen P C, et al. Regularization by truncated total least squares[J]. SIAM Journal on Scientific Computing, 1997, 18(4): 1223–1241. doi: 10.1137/S106482759426383.
    [31]
    GOLUB G H, HEATH M, and WAHBA G. Generalized cross-validation as a method for choosing a good ridge parameter[J]. Technometrics, 1979, 21(2): 215–223. doi: 10.1080/00401706.1979.10489751.
    [32]
    JOHNSTON P R and GULRAJANI R M. Selecting the corner in the L-curve approach to Tikhonov regularization[J]. IEEE Transactions on Biomedical Engineering, 2000, 47(9): 1293–1296. doi: 10.1109/10.867966.
    [33]
    ENGL H W. Discrepancy principles for Tikhonov regularization of ill-posed problems leading to optimal convergence rates[J]. Journal of optimization theory and applications, 1987, 52: 209–215. doi: 10.1007/BF00941281.
    [34]
    RUDIN L I, OSHER S, and FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1/4): 259–268. doi: 10.1016/0167-2789(92)90242-F.
    • Relative Articles

  • Relative Articles

    [1]NIE Lin, WEI Shunjun, LI Jiahui, ZHANG Hao, SHI Jun, WANG Mou, CHEN Siyuan, ZHANG Xinyan. Active Blanket Jamming Suppression Method for Spaceborne SAR Images Based on Regional Feature Refinement Perceptual Learning[J]. Journal of Radars, 2024, 13(5): 985-1003. doi: 10.12000/JR24072
    [2]WANG Huiqin, YANG Fadong, HE Yongqiang, LIU Bincan, LIU Xin. Detection of Common Underground Targets in Ground Penetrating Radar Images Using the GDS-YOLOv8n Model[J]. Journal of Radars, 2024, 13(6): 1170-1183. doi: 10.12000/JR24160
    [3]WANG Xiang, WANG Yumiao, CHEN Xingyu, ZANG Chuanfei, CUI Guolong. Deep Learning-based Marine Target Detection Method with Multiple Feature Fusion[J]. Journal of Radars, 2024, 13(3): 554-564. doi: 10.12000/JR23105
    [4]CHEN Xiang, WANG Liandong, XU Xiong, SHEN Xujian, FENG Yuntian. A Review of Radio Frequency Fingerprinting Methods Based on Raw I/Q and Deep Learning[J]. Journal of Radars, 2023, 12(1): 214-234. doi: 10.12000/JR22140
    [5]TIAN Ye, DING Chibiao, ZHANG Fubo, SHI Min’an. SAR Building Area Layover Detection Based on Deep Learning[J]. Journal of Radars, 2023, 12(2): 441-455. doi: 10.12000/JR23033
    [6]DING Zihang, XIE Junwei, WANG Bo. Missing Covariance Matrix Recovery with the FDA-MIMO Radar Using Deep Learning Method[J]. Journal of Radars, 2023, 12(5): 1112-1124. doi: 10.12000/JR23002
    [7]HE Mi, PING Qinwen, DAI Ran. Fall Detection Based on Deep Learning Fusing Ultrawideband Radar Spectrograms[J]. Journal of Radars, 2023, 12(2): 343-355. doi: 10.12000/JR22169
    [8]LYU Xiaoling, QIU Xiaolan, YU Wenming, XU Feng. Simulation-assisted SAR Target Classification Based on Unsupervised Domain Adaptation and Model Interpretability Analysis[J]. Journal of Radars, 2022, 11(1): 168-182. doi: 10.12000/JR21179
    [9]HUANG Zhongling, YAO Xiwen, HAN Junwei. Progress and Perspective on Physically Explainable Deep Learning for Synthetic Aperture Radar Image Interpretation（in English）[J]. Journal of Radars, 2022, 11(1): 107-125. doi: 10.12000/JR21165
    [10]MA Lin, PAN Zongxu, HUANG Zhongling, HAN Bing, HU Yuxin, ZHOU Xiao, LEI Bin. Multichannel False-target Discrimination in SAR Images Based on Sub-aperture and Full-aperture Feature Learning[J]. Journal of Radars, 2021, 10(1): 159-172. doi: 10.12000/JR20106
    [11]CUI Xingchao, SU Yi, CHEN Siwei. Polarimetric SAR Ship Detection Based on Polarimetric Rotation Domain Features and Superpixel Technique[J]. Journal of Radars, 2021, 10(1): 35-48. doi: 10.12000/JR20147
    [12]ZHOU Xueke, LIU Chang, ZHOU Bin. Ship Detection in SAR Images Based on Multiscale Feature Fusion and Channel Relation Calibration of Features[J]. Journal of Radars, 2021, 10(4): 531-543. doi: 10.12000/JR21021
    [13]GUO Weiwei, ZHANG Zenghui, YU Wenxian, SUN Xiaohua. Perspective on Explainable SAR Target Recognition[J]. Journal of Radars, 2020, 9(3): 462-476. doi: 10.12000/JR20059
    [14]Zhao Feixiang, Liu Yongxiang, Huo Kai. A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine[J]. Journal of Radars, 2018, 7(5): 613-621. doi: 10.12000/JR18048
    [15]Wang Jun, Zheng Tong, Lei Peng, Wei Shaoming. Study on Deep Learning in Radar[J]. Journal of Radars, 2018, 7(4): 395-411. doi: 10.12000/JR18040
    [16]Qian Lichang, Xu Jia, Hu Guoxu. Long-time Integration of a Multi-waveform for Weak Target Detection in Non-cooperative Passive Bistatic Radar[J]. Journal of Radars, 2017, 6(3): 259-266. doi: 10.12000/JR16137
    [17]Xu Feng, Wang Haipeng, Jin Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136-148. doi: 10.12000/JR16130
    [18]Kang Miao, Ji Kefeng, Leng Xiangguang, Xing Xiangwei, Zou Huanxin. SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder[J]. Journal of Radars, 2017, 6(2): 167-176. doi: 10.12000/JR16112
    [19]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
    [20]Xu Jia, Peng Ying-ning, Xia Xiang-gen, Long Teng, Mao Er-ke. Radar Signal Processing Method of Space-Time-Frequency Focus-Before-Detects[J]. Journal of Radars, 2014, 3(2): 129-141. doi: 10.3724/SP.J.1300.2014.14023
    • Supplements(0)
    • Cited By

  • Cited by

    Periodical cited type(3)

    1. 徐文静，刘杰，于君明，冯晓峰，范睿嘉，尹良. 基于极化对比增强和模板匹配的全极化SAR目标分类方法. 无线电工程. 2025(01): 138-145 .
    2. 何永鹏，杨艺，程志君，程洋，张陆进，王泉. 反辐射导弹发展的挑战、现状及展望. 空天防御. 2024(04): 38-46 .
    3. 杨政，程永强，吴昊，杨阳，黎湘，王宏强. 基于流形变换的信息几何雷达目标检测方法. 电子与信息学报. 2024(11): 4317-4327 .

    Other cited types(0)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040255075100125
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 30.9 %FULLTEXT: 30.9 %META: 57.6 %META: 57.6 %PDF: 11.6 %PDF: 11.6 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 8.3 %其他: 8.3 %其他: 6.5 %其他: 6.5 %China: 0.1 %China: 0.1 %San Jose: 0.1 %San Jose: 0.1 %San Lorenzo: 0.3 %San Lorenzo: 0.3 %Thane: 0.3 %Thane: 0.3 %上海: 1.7 %上海: 1.7 %东京: 1.2 %东京: 1.2 %东京都: 0.1 %东京都: 0.1 %东莞: 0.1 %东莞: 0.1 %临沂: 0.2 %临沂: 0.2 %九江: 0.2 %九江: 0.2 %伦敦: 0.1 %伦敦: 0.1 %六安: 0.3 %六安: 0.3 %兰辛: 0.1 %兰辛: 0.1 %内江: 0.2 %内江: 0.2 %北京: 8.3 %北京: 8.3 %十堰: 0.1 %十堰: 0.1 %南京: 2.4 %南京: 2.4 %南充: 0.1 %南充: 0.1 %南昌: 0.2 %南昌: 0.2 %卧龙岗: 0.5 %卧龙岗: 0.5 %厦门: 0.1 %厦门: 0.1 %台北: 0.2 %台北: 0.2 %台州: 0.5 %台州: 0.5 %合肥: 0.3 %合肥: 0.3 %呼和浩特: 0.3 %呼和浩特: 0.3 %哈尔滨: 0.2 %哈尔滨: 0.2 %嘉兴: 0.2 %嘉兴: 0.2 %天水围: 0.1 %天水围: 0.1 %天津: 0.8 %天津: 0.8 %太原: 0.2 %太原: 0.2 %威海: 0.3 %威海: 0.3 %安康: 0.4 %安康: 0.4 %宝鸡: 0.6 %宝鸡: 0.6 %巴中: 0.1 %巴中: 0.1 %巴音郭楞: 0.1 %巴音郭楞: 0.1 %常州: 0.6 %常州: 0.6 %平顶山: 0.1 %平顶山: 0.1 %广州: 2.9 %广州: 2.9 %开封: 1.7 %开封: 1.7 %张家口: 4.4 %张家口: 4.4 %徐州: 0.3 %徐州: 0.3 %德罕: 0.1 %德罕: 0.1 %惠州: 0.3 %惠州: 0.3 %成都: 3.2 %成都: 3.2 %扬州: 0.3 %扬州: 0.3 %新德里: 0.3 %新德里: 0.3 %昆明: 2.2 %昆明: 2.2 %朝阳: 0.6 %朝阳: 0.6 %杭州: 0.8 %杭州: 0.8 %松原: 0.3 %松原: 0.3 %桂林: 0.2 %桂林: 0.2 %武汉: 0.6 %武汉: 0.6 %沧州: 0.1 %沧州: 0.1 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.5 %济南: 0.5 %淮南: 0.1 %淮南: 0.1 %深圳: 1.0 %深圳: 1.0 %温州: 0.3 %温州: 0.3 %湘潭: 0.2 %湘潭: 0.2 %漯河: 0.9 %漯河: 0.9 %濮阳: 0.2 %濮阳: 0.2 %烟台: 0.1 %烟台: 0.1 %珠海: 0.1 %珠海: 0.1 %白城: 0.2 %白城: 0.2 %石家庄: 0.3 %石家庄: 0.3 %福州: 0.2 %福州: 0.2 %纽约: 0.1 %纽约: 0.1 %绵阳: 0.1 %绵阳: 0.1 %芒廷维尤: 22.8 %芒廷维尤: 22.8 %芜湖: 0.2 %芜湖: 0.2 %芝加哥: 0.7 %芝加哥: 0.7 %苏州: 0.2 %苏州: 0.2 %衡水: 0.2 %衡水: 0.2 %衡阳: 0.2 %衡阳: 0.2 %衢州: 0.3 %衢州: 0.3 %西宁: 7.2 %西宁: 7.2 %西安: 1.6 %西安: 1.6 %诺沃克: 3.3 %诺沃克: 3.3 %贵阳: 0.2 %贵阳: 0.2 %运城: 0.5 %运城: 0.5 %连云港: 0.1 %连云港: 0.1 %邯郸: 0.2 %邯郸: 0.2 %郑州: 0.2 %郑州: 0.2 %鄂州: 0.1 %鄂州: 0.1 %重庆: 1.1 %重庆: 1.1 %镇江: 0.1 %镇江: 0.1 %长沙: 1.4 %长沙: 1.4 %随州: 0.3 %随州: 0.3 %雷德蒙德: 0.2 %雷德蒙德: 0.2 %青岛: 0.4 %青岛: 0.4 %香港: 0.1 %香港: 0.1 %马鞍山: 0.3 %马鞍山: 0.3 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %其他其他ChinaSan JoseSan LorenzoThane上海东京东京都东莞临沂九江伦敦六安兰辛内江北京十堰南京南充南昌卧龙岗厦门台北台州合肥呼和浩特哈尔滨嘉兴天水围天津太原威海安康宝鸡巴中巴音郭楞常州平顶山广州开封张家口徐州德罕惠州成都扬州新德里昆明朝阳杭州松原桂林武汉沧州洛阳济南淮南深圳温州湘潭漯河濮阳烟台珠海白城石家庄福州纽约绵阳芒廷维尤芜湖芝加哥苏州衡水衡阳衢州西宁西安诺沃克贵阳运城连云港邯郸郑州鄂州重庆镇江长沙随州雷德蒙德青岛香港马鞍山齐齐哈尔

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(685) PDF downloads(138) Cited by(3)
    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