LIU Tao, YANG Ziyuan, JIANG Yanni, et al. Review of ship detection in polarimetric synthetic aperture imagery[J].Journal of Radars, 2021, 10(1): 1–19. doi: 10.12000/JR20155
Citation: XU Shuwen, SHI Xingyu, and SHUI Penglang. An adaptive detector with mismatched signals rejection in compound Gaussian clutter [J]. Journal of Radars, 2019, 8(3): 326–334. doi: 10.12000/JR19030

An Adaptive Detector with Mismatched Signals Rejection in Compound Gaussian Clutter

DOI: 10.12000/JR19030
Funds:  The National Natural Science Foundation of China (61871303), The Foundation of National Key Laboratory of Electromagnetic Environment (6142403180204), The Natural Science Basic Research Plan in Shaanxi Province of China (2017JM6031), Young Talent Fund of University Association for Science and Technology in Shaanxi (20160205), Foreign Scholars in University Research and Teaching Programs (the 111 Project) (B18039)
More Information
  • Corresponding author: XU Shuwen, swxu@mail.xidian.edu.cn
  • Received Date: 2019-02-25
  • Rev Recd Date: 2019-05-05
  • Available Online: 2019-05-20
  • Publish Date: 2019-06-01
  • Because of the improvement in radar resolution and decrease in grazing angle, the amplitude distribution of sea clutter obviously deviates from the Rayleigh distribution and presents a significant non-Gaussian feature. In this case, the compound Gaussian model is widely used. This study investigates the problem of detecting a target when signal mismatches occur in compound Gaussian clutter and proposes a selective detector to reject mismatched signals embedded in compound Gaussian clutter based on the so-called two-step Generalized Likelihood Ratio Test (GLRT). To design the selective detector, we modified the original hypothesis test by injecting a fictitious interference under the null hypothesis. These unwanted signals are assumed to be orthogonal to the nominal steering vector in the whitened subspace. The proposed detector has a Constant False Alarm Rate (CFAR) with respect to the statistics of the texture and covariance matrix. Finally, to demonstrate the effectiveness of the proposed detector, a Monte Carlo simulation is conducted to assess its performance based on the simulated and measured sea clutter data. The experimental results show that the proposed detector effectively improves the selectivity of the mismatched signals together with the detection of matched signals in a range spread target of 1~3 dB.

     

  • [1]
    KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, AES-22(2): 115–127. doi: 10.1109/TAES.1986.310745
    [2]
    ROBEY F C, FUHRMANN D R, KELLY E J, et al. A CFAR adaptive matched filter detector[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 208–216. doi: 10.1109/7.135446
    [3]
    DE MAIO A. Rao test for adaptive detection in Gaussian interference with unknown covariance matrix[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3577–3584. doi: 10.1109/TSP.2007.894238
    [4]
    CONTE E, DE MAIO A, and RICCI G. GLRT-based adaptive detection algorithms for range-spread targets[J]. IEEE Transactions on Signal Processing, 2001, 49(7): 1336–1348. doi: 10.1109/78.928688
    [5]
    GINI F and GRECO M. Texture modelling, estimation and validation using measured sea clutter data[J]. IEE Proceedings - Radar, Sonar and Navigation, 2002, 149(3): 115–124. doi: 10.1049/ip-rsn:20020272
    [6]
    SANGSTON K J, GINI F, GRECO M V, et al. Structures for radar detection in compound Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(2): 445–458. doi: 10.1109/7.766928
    [7]
    XU Shuwen, XUE Jian, and SHUI Penglang. Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture[J]. Digital Signal Processing, 2016, 56: 132–139. doi: 10.1016/j.dsp.2016.06.009
    [8]
    SHANG Xiuqin, SONG Hongjun, WANG Yu, et al. Adaptive detection of distributed targets in compound-Gaussian clutter with inverse gamma texture[J]. Digital Signal Processing, 2012, 22(6): 1024–1030. doi: 10.1016/j.dsp.2012.05.002
    [9]
    SANGSTON K J, GINI F, and GRECO M S. Coherent radar target detection in heavy-tailed compound-Gaussian clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 64–77. doi: 10.1109/TAES.2012.6129621
    [10]
    DONG Y. Optimal coherent radar detection in a K-distributed clutter environment[J]. IET Radar, Sonar & Navigation, 2012, 6(5): 283–292. doi: 10.1049/iet-rsn.2011.0273
    [11]
    SHUI Penglang, LIU Ming, and XU Shuwen. Shape-parameter-dependent coherent radar target detection in K-distributed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 451–465. doi: 10.1109/TAES.2015.140109
    [12]
    PULSONE N B and RADER C M. Adaptive beamformer orthogonal rejection test[J]. IEEE Transactions on Signal Processing, 2001, 49(3): 521–529. doi: 10.1109/78.905870
    [13]
    BANDIERA F, BESSON O, and RICCI G. An ABORT-like detector with improved mismatched signals rejection capabilities[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 14–25. doi: 10.1109/TSP.2007.906690
    [14]
    LIU Weijian, LIU Jun, DU Qinglei, et al. Distributed target detection in partially homogeneous environment when signal mismatch occurs[J]. IEEE Transactions on Signal Processing, 2018, 66(14): 3918–3928. doi: 10.1109/TSP.2018.2841860
    [15]
    RANGASWAMY M, WEINER D D, and OZTURK A. Non-Gaussian random vector identification using spherically invariant random processes[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 111–124. doi: 10.1109/7.249117
    [16]
    YAO K. A representation theorem and its applications to spherically-invariant random processes[J]. IEEE Transactions on Information Theory, 1973, 19(5): 600–608. doi: 10.1109/TIT.1973.1055076
    [17]
    RICHMOND C D. Performance of a class of adaptive detection algorithms in nonhomogeneous environments[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1248–1262. doi: 10.1109/78.839973
    [18]
    RICHMOND C D. Statistics of adaptive nulling and use of the generalized eigenrelation (GER) for modeling inhomogeneities in adaptive processing[J]. IEEE Transactions on Signal Processing, 2000, 48(5): 1263–1273. doi: 10.1109/78.839974
    [19]
    CONTE E and DE MAIO A. Mitigation techniques for non-Gaussian sea clutter[J]. IEEE Journal of Oceanic Engineering, 2004, 29(2): 284–302. doi: 10.1109/JOE.2004.826901
    [20]
    CONTE E, LOPS M, and RICCI G. Adaptive matched filter detection in spherically invariant noise[J]. IEEE Signal Processing Letters, 1996, 3(8): 248–250. doi: 10.1109/97.511809
  • Relative Articles

    [1]YIN Junjun, LUO Jiahao, LI Xiang, DAI Xiaokang, YANG Jian. Ship Detection Based on Polarimetric SAR Gradient and Complex Wishart Classifier[J]. Journal of Radars, 2024, 13(2): 396-410. doi: 10.12000/JR23198
    [2]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
    [3]ZHU Qingtao, YIN Junjun, ZENG Liang, YANG Jian. Polarimetric SAR Image Affine Registration Based on Neighborhood Consensus[J]. Journal of Radars, 2021, 10(1): 49-60. doi: 10.12000/JR20120
    [4]PANG Lei, ZHANG Fengli, WANG Guojun, LIU Na, SHAO Yun, ZHANG Jiameng, ZHAO Yuchuan, PANG Lei. Imaging Simulation and Damage Assessment Feature Analysis of Ku Band Polarized SAR of Buildings[J]. Journal of Radars, 2020, 9(3): 578-587. doi: 10.12000/JR20061
    [5]QIN Xianxiang, YU Wangsheng, WANG Peng, CHEN Tianping, ZOU Huanxin. Weakly Supervised Classification of PolSAR Images Based on Sample Refinement with Complex-Valued Convolutional Neural Network[J]. Journal of Radars, 2020, 9(3): 525-538. doi: 10.12000/JR20062
    [6]DENG Yunkai, YU Weidong, ZHANG Heng, WANG Wei, LIU Dacheng, WANG Robert. Forthcoming Spaceborne SAR Development[J]. Journal of Radars, 2020, 9(1): 1-33. doi: 10.12000/JR20008
    [7]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
    [8]LENG Xiangguang, JI Kefeng, XIONG Boli, KUANG Gangyao. Statistical Modeling Methods of Single-channel Complex-valued SAR Images for Ship Detection[J]. Journal of Radars, 2020, 9(3): 477-496. doi: 10.12000/JR20070
    [9]CHEN Huiyuan, LIU Zeyu, GUO Weiwei, ZHANG Zenghui, YU Wenxian. Fast Detection of Ship Targets for Large-scale Remote Sensing Image Based on a Cascade Convolutional Neural Network[J]. Journal of Radars, 2019, 8(3): 413-424. doi: 10.12000/JR19041
    [10]ZHANG Lamei, ZHANG Siyu, DONG Hongwei, ZHU Sha. Robust Classification of PolSAR Images Based on Pinball loss Support Vector Machine[J]. Journal of Radars, 2019, 8(4): 448-457. doi: 10.12000/JR19055
    [11]ZHANG Xiangrong, YU Xinyuan, TANG Xu, HOU Biao, JIAO Licheng. PolSAR Image Classification Method Based on Markov Discriminant Spectral Clustering[J]. Journal of Radars, 2019, 8(4): 425-435. doi: 10.12000/JR19059
    [12]Chen Siwei, Li Yongzhen, Wang Xuesong, Xiao Shunping. Polarimetric SAR Target Scattering Interpretation in Rotation Domain: Theory and Application[J]. Journal of Radars, 2017, 6(5): 442-455. doi: 10.12000/JR17033
    [13]Zou Huanxin, Luo Tiancheng, Zhang Yue, Zhou Shilin. Combined Conditional Random Fields Model for Supervised PolSAR Images Classification[J]. Journal of Radars, 2017, 6(5): 541-553. doi: 10.12000/JR16109
    [14]Yang Wen, Zhong Neng, Yan Tianheng, Yang Xiangli. Classification of Polarimetric SAR Images Based on the Riemannian Manifold[J]. Journal of Radars, 2017, 6(5): 433-441. doi: 10.12000/JR17031
    [15]Liu Zeyu, Liu Bin, Guo Weiwei, Zhang Zenghui, Zhang Bo, Zhou Yueheng, Ma Gao, Yu Wenxian. Ship Detection in GF-3 NSC Mode SAR Images[J]. Journal of Radars, 2017, 6(5): 473-482. doi: 10.12000/JR17059
    [16]Zhang Jie, Zhang Xi, Fan Chenqing, Meng Junmin. Discussion on Application of Polarimetric Synthetic Aperture Radar in Marine Surveillance[J]. Journal of Radars, 2016, 5(6): 596-606. doi: 10.12000/JR16124
    [17]Hong Wen. Hybrid-polarity Architecture Based Polarimetric SAR: Principles and Applications (in English)[J]. Journal of Radars, 2016, 5(6): 559-595. doi: 10.12000/JR16074
    [18]Xing Yanxiao, Zhang Yi, Li Ning, Wang Yu, Hu Guixiang. Polarimetric SAR Image Supervised Classification Method Integrating Eigenvalues[J]. Journal of Radars, 2016, 5(2): 217-227. doi: 10.12000/JR16019
    [19]Ji Kefeng, Wang Haibo, Leng Xiangguang, Xing Xiangwei, Kang Lihong. Spaceborne Compact Polarimetric Synthetic Aperture Radar for Ship Detection[J]. Journal of Radars, 2016, 5(6): 607-619. doi: 10.12000/JR16083
    [20]Hua Wen-qiang, Wang Shuang, Hou Biao. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart[J]. Journal of Radars, 2015, 4(1): 93-98. doi: 10.12000/JR14138
  • Cited by

    Periodical cited type(15)

    1. 师俞晨. 基于遥感影像水下目标尾迹探测综述. 现代防御技术. 2024(01): 83-91 .
    2. 李煜,杨静飞,张鸿生,李刚,陈杰. 极化合成孔径雷达遥感地物分类研究进展. 遥感学报. 2024(08): 1835-1853 .
    3. 周慧,朱虹,陈澎. 基于可变形的多尺度自注意力特征融合SAR影像舰船识别. 大连海事大学学报. 2024(04): 110-118 .
    4. 林晓晶,肖鹏浩,何良,王海鹏. 基于极化神经网络的雷达舰船检测识别方法. 上海航天(中英文). 2023(01): 53-60 .
    5. 邢世其,全斯农,范晖,王威,黄大通,李永祯,王雪松. 联合数学规划策略和精细极化分解的极化SAR舰船目标检测. 中国科学:信息科学. 2023(03): 585-605 .
    6. 向诚,颜世杰,桂玲. 不平衡SAR图像舰船目标识别模型. 舰船科学技术. 2023(05): 174-177 .
    7. 曹运运,杨子渊,刘维建,刘涛. 雷达极化对角加载检测器的最优权重算法. 雷达科学与技术. 2023(02): 222-230 .
    8. 罗嘉豪,殷君君,杨健. 基于超像素与稀疏重构显著性的极化SAR舰船检测. 工程科学学报. 2023(10): 1684-1692 .
    9. 李郝亮,陈思伟. 海面角反射体电磁散射特性与雷达鉴别研究进展与展望. 雷达学报. 2023(04): 738-761 . 本站查看
    10. 王春华,王方超. 基于改进阈值函数的SAR图像小波去噪方法. 微电子学与计算机. 2022(05): 39-44 .
    11. 王少博,张成,苏迪,冀瑞静. 基于改进YOLOv3和核相关滤波算法的旋转弹目标探测算法. 兵工学报. 2022(05): 1032-1045 .
    12. 王志鹤,行坤,崔宁,喻忠军. 一种基于Radon变换和尾迹模型的尾迹检测算法. 电子设计工程. 2022(12): 1-6 .
    13. 任吉宏,刘畅. 基于自适应超像素的少样本极化SAR图像特征增强方法研究. 电子技术应用. 2022(10): 144-149 .
    14. 张阳,刘小芳,周鹏成. 改进Faster R-CNN的SAR图像船舶检测技术. 无线电工程. 2022(12): 2280-2287 .
    15. 常佳慧,赵建辉,李宁. 一种改进的2P-CFAR SAR舰船检测方法. 国外电子测量技术. 2021(11): 7-12 .

    Other cited types(28)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040102030405060
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 30.9 %FULLTEXT: 30.9 %META: 63.5 %META: 63.5 %PDF: 5.6 %PDF: 5.6 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 5.8 %其他: 5.8 %其他: 0.3 %其他: 0.3 %Central District: 0.1 %Central District: 0.1 %China: 0.8 %China: 0.8 %France: 0.0 %France: 0.0 %India: 0.0 %India: 0.0 %Taiwan, China: 0.1 %Taiwan, China: 0.1 %United States: 0.2 %United States: 0.2 %[]: 0.9 %[]: 0.9 %三亚: 0.0 %三亚: 0.0 %三明: 0.0 %三明: 0.0 %上海: 0.9 %上海: 0.9 %东莞: 0.1 %东莞: 0.1 %中卫: 0.1 %中卫: 0.1 %临汾: 0.2 %临汾: 0.2 %临沂: 0.1 %临沂: 0.1 %丹东: 0.1 %丹东: 0.1 %佛山: 0.1 %佛山: 0.1 %保定: 0.1 %保定: 0.1 %元朗新墟: 0.3 %元朗新墟: 0.3 %六安: 0.0 %六安: 0.0 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.0 %兰辛: 0.0 %凤凰城: 0.0 %凤凰城: 0.0 %加利福尼亚州: 0.0 %加利福尼亚州: 0.0 %包头: 0.0 %包头: 0.0 %北京: 13.0 %北京: 13.0 %北京市: 0.3 %北京市: 0.3 %北海: 0.1 %北海: 0.1 %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: 0.4 %南昌: 0.4 %南通: 0.1 %南通: 0.1 %南阳: 0.0 %南阳: 0.0 %台北: 0.1 %台北: 0.1 %台州: 0.3 %台州: 0.3 %台湾省: 0.0 %台湾省: 0.0 %合肥: 0.4 %合肥: 0.4 %呼和浩特: 0.1 %呼和浩特: 0.1 %咸阳: 0.1 %咸阳: 0.1 %哈尔滨: 0.1 %哈尔滨: 0.1 %哥伦布: 0.1 %哥伦布: 0.1 %商洛: 0.0 %商洛: 0.0 %嘉兴: 0.0 %嘉兴: 0.0 %大庆: 0.0 %大庆: 0.0 %大理: 0.0 %大理: 0.0 %大连: 0.2 %大连: 0.2 %天津: 0.5 %天津: 0.5 %太原: 0.0 %太原: 0.0 %威海: 0.1 %威海: 0.1 %安康: 0.1 %安康: 0.1 %安徽: 0.0 %安徽: 0.0 %宜春: 0.0 %宜春: 0.0 %宝鸡: 0.0 %宝鸡: 0.0 %宿州: 0.1 %宿州: 0.1 %宿迁: 0.0 %宿迁: 0.0 %巴中: 0.1 %巴中: 0.1 %巴彦淖尔: 0.0 %巴彦淖尔: 0.0 %常州: 0.1 %常州: 0.1 %常德: 0.1 %常德: 0.1 %广安: 0.0 %广安: 0.0 %广州: 0.4 %广州: 0.4 %广西壮族自治区南宁: 0.0 %广西壮族自治区南宁: 0.0 %广西壮族自治区桂林: 0.1 %广西壮族自治区桂林: 0.1 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %延安: 0.0 %延安: 0.0 %张家口: 1.5 %张家口: 1.5 %张家口市: 0.1 %张家口市: 0.1 %张掖: 0.2 %张掖: 0.2 %徐州: 0.0 %徐州: 0.0 %成都: 1.6 %成都: 1.6 %扬州: 0.6 %扬州: 0.6 %新乡: 0.3 %新乡: 0.3 %新疆维吾尔自治区哈密: 0.0 %新疆维吾尔自治区哈密: 0.0 %无锡: 0.1 %无锡: 0.1 %昆明: 0.3 %昆明: 0.3 %晋城: 0.0 %晋城: 0.0 %朝阳: 0.0 %朝阳: 0.0 %杭州: 1.2 %杭州: 1.2 %杭州市: 0.0 %杭州市: 0.0 %格兰特县: 0.0 %格兰特县: 0.0 %榆林: 0.1 %榆林: 0.1 %武汉: 1.5 %武汉: 1.5 %池州: 0.0 %池州: 0.0 %沈阳: 0.3 %沈阳: 0.3 %法尔肯施泰因: 0.0 %法尔肯施泰因: 0.0 %洛杉矶: 0.0 %洛杉矶: 0.0 %洛阳: 0.2 %洛阳: 0.2 %济南: 0.6 %济南: 0.6 %海口: 0.0 %海口: 0.0 %淮南: 0.1 %淮南: 0.1 %淮安: 0.0 %淮安: 0.0 %深圳: 0.7 %深圳: 0.7 %温州: 0.0 %温州: 0.0 %渭南: 0.1 %渭南: 0.1 %湖州: 0.2 %湖州: 0.2 %湘潭: 0.0 %湘潭: 0.0 %湛江: 0.0 %湛江: 0.0 %滁州: 0.0 %滁州: 0.0 %漯河: 0.4 %漯河: 0.4 %潮州: 0.1 %潮州: 0.1 %烟台: 0.1 %烟台: 0.1 %焦作: 0.0 %焦作: 0.0 %玉林: 0.1 %玉林: 0.1 %益阳: 0.1 %益阳: 0.1 %盐城: 0.1 %盐城: 0.1 %石家庄: 0.4 %石家庄: 0.4 %福州: 0.1 %福州: 0.1 %绍兴: 0.1 %绍兴: 0.1 %绵阳: 0.0 %绵阳: 0.0 %美国伊利诺斯芝加哥: 0.0 %美国伊利诺斯芝加哥: 0.0 %舟山: 0.0 %舟山: 0.0 %芒廷维尤: 18.9 %芒廷维尤: 18.9 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.6 %苏州: 0.6 %荆门: 0.0 %荆门: 0.0 %莆田: 0.0 %莆田: 0.0 %莱芜: 0.1 %莱芜: 0.1 %葫芦岛: 0.0 %葫芦岛: 0.0 %衡水: 0.1 %衡水: 0.1 %衢州: 0.2 %衢州: 0.2 %西宁: 29.7 %西宁: 29.7 %西安: 4.3 %西安: 4.3 %西安市未央区: 0.1 %西安市未央区: 0.1 %西安市鄠邑区: 0.0 %西安市鄠邑区: 0.0 %贵港: 0.2 %贵港: 0.2 %贵阳: 0.1 %贵阳: 0.1 %赣州: 0.0 %赣州: 0.0 %运城: 0.5 %运城: 0.5 %连云港: 0.1 %连云港: 0.1 %通化: 0.0 %通化: 0.0 %邢台: 0.0 %邢台: 0.0 %邯郸: 0.0 %邯郸: 0.0 %郑州: 1.4 %郑州: 1.4 %重庆: 0.1 %重庆: 0.1 %金华: 0.0 %金华: 0.0 %长春: 0.1 %长春: 0.1 %长沙: 0.7 %长沙: 0.7 %青岛: 0.3 %青岛: 0.3 %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 %马鞍山: 0.0 %马鞍山: 0.0 %其他其他Central DistrictChinaFranceIndiaTaiwan, ChinaUnited States[]三亚三明上海东莞中卫临汾临沂丹东佛山保定元朗新墟六安兰州兰辛凤凰城加利福尼亚州包头北京北京市北海十堰南京南充南昌南通南阳台北台州台湾省合肥呼和浩特咸阳哈尔滨哥伦布商洛嘉兴大庆大理大连天津太原威海安康安徽宜春宝鸡宿州宿迁巴中巴彦淖尔常州常德广安广州广西壮族自治区南宁广西壮族自治区桂林库比蒂诺延安张家口张家口市张掖徐州成都扬州新乡新疆维吾尔自治区哈密无锡昆明晋城朝阳杭州杭州市格兰特县榆林武汉池州沈阳法尔肯施泰因洛杉矶洛阳济南海口淮南淮安深圳温州渭南湖州湘潭湛江滁州漯河潮州烟台焦作玉林益阳盐城石家庄福州绍兴绵阳美国伊利诺斯芝加哥舟山芒廷维尤芝加哥苏州荆门莆田莱芜葫芦岛衡水衢州西宁西安西安市未央区西安市鄠邑区贵港贵阳赣州运城连云港通化邢台邯郸郑州重庆金华长春长沙青岛香港香港特别行政区马鞍山

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(2431) PDF downloads(199) Cited by(43)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint