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
Citation: LI Bo, CEN Zongjun, and TANG Jun. A new method of target detection for passive radar based on information accumulation[J]. Journal of Radars, 2020, 9(6): 959–966. doi: 10.12000/JR20023

A New Method of Target Detection for Passive Radar Based on Information Accumulation

DOI: 10.12000/JR20023
Funds:  The National Ministry Foundation of China (19-163-11-ZD-019-006-02)
More Information
  • Corresponding author: TANG Jun, tangj_ee@tsinghua.edu.cn
  • Received Date: 2020-03-23
  • Rev Recd Date: 2020-05-22
  • Available Online: 2020-06-18
  • Publish Date: 2020-12-28
  • Owing to their strong anti-stealth performance, good concealment and strong survivability, passive radar systems have a wide range of applications in both military and civilian fields. We propose a method of target detection for passive radar systems which is based on the characteristics of these systems and the track-before-detect concept. This method accumulates information to effectively detect weak targets with low signal-to-noise ratios and meet real-time requirements. First, we discretize the state space, then perform recursive Bayesian filtering to transfer and accumulate target-state information between multiple frames. Lastly, the information entropy is used to determine whether the target exists, thereby avoiding reliance on a prior assumption about the transition probability model between the existence and the absence of the target. This method is simple to implement and has low computational complexity and high parallelism. The experimental results indicate that the proposed method has a short running time and strong real-time performance, as well as good detection performance and robustness.

     

  • [1]
    GRIFFITHS H D and BAKER C J. An Introduction to Passive Radar[M]. Boston, US: Artech House, 2017: 1–25.
    [2]
    WILLIS N J. Bistatic Radar[M]. Raleigh, US: SciTech Publishing, 2005: 15–57.
    [3]
    KULPA K and MALANOWSKI M. From Klein Heidelberg to modern multistatic passive radar[C]. The 2019 20th International Radar Symposium (IRS), Ulm, Germany, 2019: 1–9. doi: 10.23919/IRS.2019.8768176.
    [4]
    何友, 关键, 孟祥伟, 等. 雷达目标检测与恒虚警处理[M]. 2版. 北京: 清华大学出版社, 2011: 36–40.

    HE You, GUAN Jian, MENG Xiangwei, et al. Radar Target Detection and CFAR Processing[M]. 2nd ed. Beijing: Tsinghua University Press, 2011: 36–40.
    [5]
    CARLSON B D, EVANS E D, and WILSON S L. Search radar detection and track with the Hough transform. I. system concept[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(1): 102–108. doi: 10.1109/7.250410
    [6]
    BARNIV Y. Dynamic programming solution for detecting dim moving targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 1985, AES–21(1): 144–156. doi: 10.1109/TAES.1985.310548
    [7]
    YI Wei, JIANG Haichao, KIRUBARAJAN T, et al. Track-before-detect strategies for radar detection in G0-distributed clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(5): 2516–2533. doi: 10.1109/TAES.2017.2702259
    [8]
    SALMOND D J and BIRCH H. A particle filter for track-before-detect[C]. 2001 American Control Conference, Arlington, USA, 2001: 3755–3760. doi: 10.1109/ACC.2001.946220.
    [9]
    RUTTEN M G, GORDON N J, and MASKELL S. Recursive track-before-detect with target amplitude fluctuations[J]. IEE Proceedings-Radar, Sonar and Navigation, 2005, 152(5): 345–352. doi: 10.1049/ip-rsn:20045041
    [10]
    RUTTEN M G, GORDON N J, and MASKELL S. Efficient particle-based track-before-detect in Rayleigh noise[C]. The 7th International Conference on Information Fusion, Stockholm, Sweden, 2004: 693–700.
    [11]
    JISHY K and LEHMANN F. A Bayesian track-before-detect procedure for passive radars[J]. EURASIP Journal on Advances in Signal Processing, 2013, 2013(1): 45. doi: 10.1186/1687-6180-2013-45
    [12]
    ROLLASON M and SALMOND D. Particle filter for track-before-detect of a target with unknown amplitude viewed against a structured scene[J]. IET Radar, Sonar & Navigation, 2018, 12(6): 603–609. doi: 10.1049/iet-rsn.2017.0483
    [13]
    STONE L D, CORWIN T L, and BARLOW C A. Bayesian Multiple Target Tracking[M]. Boston, US: Artech House, 2013: 65–77.
    [14]
    ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188. doi: 10.1109/78.978374
    [15]
    MAHAFZA B R. Introduction to Radar Analysis[M]. Boca Raton, US: CRC Press, 1998: 267–287.
    [16]
    DAVEY S J, RUTTEN M G, and CHEUNG B. A comparison of detection performance for several track-before-detect algorithms[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2008(1): 428036. doi: 10.1155/2008/428036
  • Relative Articles

    [1]LI Meilin, HAN Chong. Terahertz Communication and Sensing Framework Based on Orthogonal Delay-Doppler Division Multiplexing Modulation[J]. Journal of Radars. doi: 10.12000/JR24238
    [2]ZHANG Peng, YAN Junkun, GAO Chang, LI Kang, LIU Hongwei. Integrated Transmission Resource Management Scheme for Multifunctional Radars in Dynamic Electromagnetic Environments[J]. Journal of Radars, 2025, 14(2): 456-469. doi: 10.12000/JR24230
    [3]LIU Fan, LU Shihang, CHEN Zihao. MIMO-ISAC Precoding Design Toward Random Signals[J]. Journal of Radars. doi: 10.12000/JR25019
    [4]TANG Aimin, WANG Shuhan, QU Wenze. Reference Signal Design in OFDM ISAC for Long-range and High-speed UAV Detection[J]. Journal of Radars. doi: 10.12000/JR24240
    [5]HE Yaping, SHI Longfei, WANG Dong, TANG Jianglan, CHEN Junxian, MA Jiazhi, LIU Jialei. Research Progress on Dual Function Radar and Communication Signal Design and its Application in Typical Detection Scenarios[J]. Journal of Radars. doi: 10.12000/JR24213
    [6]HE Zhuoyuan, CHEN Shengyao, ZHU Han, XI Feng, LI Hongtao, LIU Zhong. Transmit Waveform Design for Symbol-Level Precoding-based One-Bit Dual-Functional Radar-Communication[J]. Journal of Radars. doi: 10.12000/JR24217
    [7]CHEN Zirui, JI Yifei, LIU Xiwang, ZHANG Yongsheng, DONG Zhen, CHEN Alei, LIU Weijian. Transient Interference Suppression Algorithm Based on Time Frequency Sparse Prior for Skywave OTHR[J]. Journal of Radars, 2024, 13(6): 1157-1169. doi: 10.12000/JR24188
    [8]LIU Yan, WAN Xianrong, YI Jianxin. OFDM Waveform Design for Joint Radar-communication Based on Data Distortion[J]. Journal of Radars, 2024, 13(1): 160-173. doi: 10.12000/JR23205
    [9]LIU Liu, LIANG Xingdong, LI Yanlei, ZENG Zhiyuan, TANG Haibo. A Novel Joint Radar-communication Waveform Design Method Based on Distributed Aperture[J]. Journal of Radars, 2023, 12(2): 297-311. doi: 10.12000/JR23019
    [10]LI Wanlu, XIANG Zheng, REN Peng. Filter Bank Multi-carrier Waveform Design for Low Probability of Intercepting Joint Radar and Communication System[J]. Journal of Radars, 2023, 12(2): 287-296. doi: 10.12000/JR22064
    [11]WANG Jiahuan, FAN Pingzhi, SHI Qiao, ZHOU Zhengchun. Doppler Resilient Integrated Sensing and Communication Waveforms Design[J]. Journal of Radars, 2023, 12(2): 275-286. doi: 10.12000/JR22155
    [12]YU Xianxiang, YAO Xue, YANG Jing, LU Jun, CUI Guolong, KONG Lingjiang. Radar-centric DFRC Signal Design: Overview and Future Research Avenues[J]. Journal of Radars, 2023, 12(2): 247-261. doi: 10.12000/JR23015
    [13]WU Wenjun, TANG Bo, TANG Jun, HU Yuankui. Waveform Design for Dual-function Radar-communication Systems in Clutter[J]. Journal of Radars, 2022, 11(4): 570-580. doi: 10.12000/JR22105
    [14]MA Dingyou, LIU Xiang, HUANG Tianyao, LIU Yimin. Joint Radar and Communications: Shared Waveform Designs and Performance Bounds[J]. Journal of Radars, 2022, 11(2): 198-212. doi: 10.12000/JR21146
    [15]LAN Lan, LIAO Guisheng, XU Jingwei, ZHU Shengqi, ZENG Cao, ZHANG Yuhong. Waveform Design and Signal Processing Method of a Multifunctional Integrated System Based on a Frequency Diverse Array(in English)[J]. Journal of Radars, 2022, 11(5): 850-870. doi: 10.12000/JR22163
    [16]WAN Huan, YU Xianxiang, QUAN Zhi, LIAO Bin. Constant Modulus Waveform Design for Low-resolution Quantization MIMO Radar Based on an Alternating Direction Penalty Method[J]. Journal of Radars, 2022, 11(4): 557-569. doi: 10.12000/JR22072
    [17]LIU Fan, YUAN Weijie, YUAN Jinhong, ZHANG J. Andrew, FEI Zesong, ZHOU Jianming. Radar-communication Spectrum Sharing and Integration: Overview and Prospect[J]. Journal of Radars, 2021, 10(3): 467-484. doi: 10.12000/JR20113
    [18]ZHAO Yuzhen, CHEN Longyong, ZHANG Fubo, LI Yanlei, WU Yirong. A New Method of Joint Radar and Communication Waveform Design and Signal Processing Based on OFDM-chirp[J]. Journal of Radars, 2021, 10(3): 453-466. doi: 10.12000/JR21028
    [19]DENG Likang, ZHANG Shuanghui, ZHANG Chi, LIU Yongxiang. A Multiple-Input Multiple-Output Inverse Synthetic Aperture Radar Imaging Method Based on Multidimensional Alternating Direction Method of Multipliers[J]. Journal of Radars, 2021, 10(3): 416-431. doi: 10.12000/JR20132
    [20]Li Lei, Li Guo-lin, Liu Run-jie. Novel Direction Of Arrival Estimation Method Based on Coherent Accumulation Matrix Reconstruction[J]. Journal of Radars, 2015, 4(2): 178-184. doi: 10.12000/JR14116
  • Cited by

    Periodical cited type(30)

    1. 连静,杨勇,谢晓霞,王雪松. 大掠射角对海雷达导引头实测回波特性分析. 系统工程与电子技术. 2024(05): 1535-1543 .
    2. 韩喆璇,于恒力,王中训,刘宁波,孙艳丽. 基于相对多普勒峰高特征的OS-CFAR改进方法. 海军航空大学学报. 2024(04): 475-484 .
    3. 关键,姜星宇,刘宁波,丁昊,黄勇. 海杂波背景下的双极化最大特征值目标检测. 系统工程与电子技术. 2024(11): 3715-3725 .
    4. 张梦雨,王中训,李飞,刘宁波,董云龙. CNN海况等级分类方法的性能. 烟台大学学报(自然科学与工程版). 2023(02): 196-203 .
    5. 关键,刘宁波,王国庆,丁昊,董云龙,黄勇,田凯祥,张梦雨. 雷达对海探测试验与目标特性数据获取——海上目标双极化多海况散射特性数据集. 雷达学报. 2023(02): 456-469 . 本站查看
    6. 许述文,焦银萍,白晓惠,蒋俊正. 基于频域多通道图特征感知的海面小目标检测. 电子与信息学报. 2023(05): 1567-1574 .
    7. 赵迪,行鸿彦,王海峰,阎妍. 基于SAE-GA-XGBoost算法的海面小目标检测. 雷达科学与技术. 2023(01): 88-96 .
    8. 关键,姜星宇,刘宁波,黄勇,丁昊. 海杂波中目标分数域谱范数特征检测方法. 电子与信息学报. 2023(06): 2162-2170 .
    9. 刘照标,张友益,陈翰. 舰载近程搜索雷达时空二维海杂波建模与仿真. 舰船电子对抗. 2023(03): 70-74 .
    10. 丁昊,朱晨光,刘宁波,王国庆. 高海况条件下海面漂浮小目标特征提取与分析. 海军航空大学学报. 2023(04): 301-312 .
    11. 李宏武,王燊燊,徐秦,祁华峰. 海杂波对机载雷达探测影响研究. 现代电子技术. 2023(20): 101-106 .
    12. 杜延磊,杨晓峰,汪胜,殷君君,杨会章,杨健. 海面雷达散射及其杂波幅度统计特性的空间遍历性数值仿真研究. 系统工程与电子技术. 2023(12): 3806-3818 .
    13. 关键,伍僖杰,丁昊,刘宁波,董云龙,张鹏飞. 基于对角积分双谱的海面慢速小目标检测方法. 电子与信息学报. 2022(07): 2449-2460 .
    14. 董云龙,刘洋,刘宁波,丁昊,关键. 基于雷达方程修正的目标探测距离评估方法. 信号处理. 2022(10): 2102-2113 .
    15. 刘宁波,丁昊,黄勇,董云龙,王国庆,董凯. X波段雷达对海探测试验与数据获取年度进展. 雷达学报. 2021(01): 173-182 . 本站查看
    16. 丁斌,夏雪,梁雪峰. 基于深度生成对抗网络的海杂波数据增强方法. 电子与信息学报. 2021(07): 1985-1991 .
    17. 时艳玲,刘子鹏,贾邦玲. 样本不平衡下的海杂波弱目标分类研究. 信号处理. 2021(09): 1781-1789 .
    18. 伍僖杰,丁昊,刘宁波,关键. 基于时频脊-Radon变换的海面小目标检测方法. 信号处理. 2021(09): 1599-1611 .
    19. 刘宁波,姜星宇,丁昊,关键. 雷达大擦地角海杂波特性与目标检测研究综述. 电子与信息学报. 2021(10): 2771-2780 .
    20. 杜延磊,高帆,刘涛,杨健. 基于数值仿真的X波段极化SAR海杂波统计建模与特性分析. 系统工程与电子技术. 2021(10): 2742-2755 .
    21. 时艳玲,姚婷婷,郭亚星. 基于图连通密度的海面漂浮小目标检测. 电子与信息学报. 2021(11): 3185-3192 .
    22. 刘用功,尹勇. 目标船感知技术综述. 广州航海学院学报. 2021(04): 1-4+30 .
    23. 陈世超,高鹤婷,罗丰. 基于极化联合特征的海面目标检测方法. 雷达学报. 2020(04): 664-673 . 本站查看
    24. 关键. 雷达海上目标特性综述. 雷达学报. 2020(04): 674-683 . 本站查看
    25. 唐先慧,李东,粟嘉,程婉儒,任金芝,李秀琴. 基于AlexNet的自适应杂波智能抑制方法. 信号处理. 2020(12): 2032-2042 .
    26. 王超,孙芹东,张林,王文龙,张小川. 水下声学滑翔机海上目标探测试验与性能评估. 信号处理. 2020(12): 2043-2051 .
    27. 曹成会,张杰,张晰,孟俊敏,毛兴鹏. 低掠射微波雷达的海杂波多方位幅度特性分析. 信号处理. 2020(12): 2085-2098 .
    28. 刘宁波,董云龙,王国庆,丁昊,黄勇,关键,陈小龙,何友. X波段雷达对海探测试验与数据获取. 雷达学报. 2019(05): 656-667 . 本站查看
    29. 于涵,水鹏朗,施赛楠,杨春娇. 广义Pareto分布海杂波模型参数的组合双分位点估计方法. 电子与信息学报. 2019(12): 2836-2843 .
    30. 王国庆,王朝铺,刘传辉,刘宁波,丁昊. 利用神经网络的海杂波幅度分布参数估计方法. 海军航空工程学院学报. 2019(06): 480-487 .

    Other cited types(23)

  • 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: 28.1 %FULLTEXT: 28.1 %META: 61.4 %META: 61.4 %PDF: 10.5 %PDF: 10.5 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 7.1 %其他: 7.1 %其他: 1.3 %其他: 1.3 %China: 0.1 %China: 0.1 %Matawan: 0.0 %Matawan: 0.0 %Research: 0.1 %Research: 0.1 %San Jose: 0.1 %San Jose: 0.1 %Seattle: 0.1 %Seattle: 0.1 %Thane: 0.1 %Thane: 0.1 %[]: 0.6 %[]: 0.6 %三明: 0.0 %三明: 0.0 %上海: 1.7 %上海: 1.7 %东京都: 0.0 %东京都: 0.0 %东莞: 0.1 %东莞: 0.1 %中卫: 0.1 %中卫: 0.1 %丹东: 0.0 %丹东: 0.0 %乌兰察布: 0.0 %乌兰察布: 0.0 %乌鲁木齐: 0.0 %乌鲁木齐: 0.0 %伊利诺伊州: 0.1 %伊利诺伊州: 0.1 %伊春: 0.0 %伊春: 0.0 %伦敦: 0.0 %伦敦: 0.0 %佛山: 0.0 %佛山: 0.0 %六安: 0.0 %六安: 0.0 %内江: 0.1 %内江: 0.1 %加利福尼亚州: 0.1 %加利福尼亚州: 0.1 %北京: 8.8 %北京: 8.8 %北海: 0.0 %北海: 0.0 %十堰: 0.1 %十堰: 0.1 %南京: 3.5 %南京: 3.5 %南充: 0.1 %南充: 0.1 %南宁: 0.2 %南宁: 0.2 %南昌: 0.2 %南昌: 0.2 %南通: 0.3 %南通: 0.3 %南阳: 0.0 %南阳: 0.0 %卡拉奇: 0.0 %卡拉奇: 0.0 %厦门: 0.4 %厦门: 0.4 %台北: 0.0 %台北: 0.0 %台州: 0.0 %台州: 0.0 %台湾: 0.2 %台湾: 0.2 %合肥: 1.0 %合肥: 1.0 %呼和浩特: 0.1 %呼和浩特: 0.1 %咸阳: 0.2 %咸阳: 0.2 %哈尔滨: 0.4 %哈尔滨: 0.4 %哥伦布: 0.0 %哥伦布: 0.0 %商洛: 0.2 %商洛: 0.2 %喀什: 0.1 %喀什: 0.1 %嘉兴: 0.1 %嘉兴: 0.1 %大同: 0.0 %大同: 0.0 %大连: 0.2 %大连: 0.2 %天津: 0.9 %天津: 0.9 %太原: 0.0 %太原: 0.0 %奥尔巴尼: 0.0 %奥尔巴尼: 0.0 %威海: 0.4 %威海: 0.4 %宁波: 0.0 %宁波: 0.0 %安康: 0.3 %安康: 0.3 %安阳: 0.0 %安阳: 0.0 %宣城: 0.6 %宣城: 0.6 %宿州: 0.0 %宿州: 0.0 %宿迁: 0.0 %宿迁: 0.0 %崇左: 0.1 %崇左: 0.1 %巴中: 0.1 %巴中: 0.1 %巴伐利亚州: 0.1 %巴伐利亚州: 0.1 %巴音郭楞: 0.0 %巴音郭楞: 0.0 %布加勒斯特: 0.1 %布加勒斯特: 0.1 %常州: 0.1 %常州: 0.1 %常德: 0.0 %常德: 0.0 %平顶山: 0.1 %平顶山: 0.1 %广州: 1.9 %广州: 1.9 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %开封: 0.4 %开封: 0.4 %张家口: 1.6 %张家口: 1.6 %徐州: 0.0 %徐州: 0.0 %德州: 0.0 %德州: 0.0 %德罕: 0.0 %德罕: 0.0 %成都: 3.8 %成都: 3.8 %扬州: 0.2 %扬州: 0.2 %抚州: 0.0 %抚州: 0.0 %揭阳: 0.0 %揭阳: 0.0 %文昌: 0.1 %文昌: 0.1 %斯德哥尔摩: 0.1 %斯德哥尔摩: 0.1 %新德里: 0.0 %新德里: 0.0 %新竹: 0.1 %新竹: 0.1 %无锡: 0.4 %无锡: 0.4 %昆明: 1.2 %昆明: 1.2 %晋中: 0.0 %晋中: 0.0 %晋城: 0.0 %晋城: 0.0 %朝阳: 0.1 %朝阳: 0.1 %来宾: 0.0 %来宾: 0.0 %杭州: 0.5 %杭州: 0.5 %松原: 0.1 %松原: 0.1 %林芝: 0.0 %林芝: 0.0 %枣庄: 0.0 %枣庄: 0.0 %柳州: 0.0 %柳州: 0.0 %格兰特县: 0.1 %格兰特县: 0.1 %桂林: 0.2 %桂林: 0.2 %楚雄: 0.0 %楚雄: 0.0 %榆林: 0.1 %榆林: 0.1 %武汉: 1.4 %武汉: 1.4 %汕头: 0.0 %汕头: 0.0 %沈阳: 0.1 %沈阳: 0.1 %沧州: 0.2 %沧州: 0.2 %泉州: 0.1 %泉州: 0.1 %法兰克福: 0.1 %法兰克福: 0.1 %泰州: 0.0 %泰州: 0.0 %洛杉矶: 0.1 %洛杉矶: 0.1 %洛阳: 0.2 %洛阳: 0.2 %济南: 0.5 %济南: 0.5 %济宁: 0.0 %济宁: 0.0 %淄博: 0.0 %淄博: 0.0 %淮北: 0.3 %淮北: 0.3 %淮南: 0.0 %淮南: 0.0 %深圳: 2.4 %深圳: 2.4 %温州: 0.2 %温州: 0.2 %渭南: 0.0 %渭南: 0.0 %湖州: 0.1 %湖州: 0.1 %湛江: 0.0 %湛江: 0.0 %漯河: 0.9 %漯河: 0.9 %潍坊: 0.0 %潍坊: 0.0 %澳门: 0.1 %澳门: 0.1 %濮阳: 0.1 %濮阳: 0.1 %烟台: 0.2 %烟台: 0.2 %珠海: 0.0 %珠海: 0.0 %甘南: 0.1 %甘南: 0.1 %石嘴山: 0.2 %石嘴山: 0.2 %石家庄: 0.5 %石家庄: 0.5 %福州: 0.2 %福州: 0.2 %科隆: 0.1 %科隆: 0.1 %秦皇岛: 0.5 %秦皇岛: 0.5 %纽约: 0.2 %纽约: 0.2 %绍兴: 0.2 %绍兴: 0.2 %绵阳: 0.2 %绵阳: 0.2 %聊城: 0.1 %聊城: 0.1 %自贡: 0.1 %自贡: 0.1 %舟山: 0.1 %舟山: 0.1 %芒廷维尤: 17.5 %芒廷维尤: 17.5 %芜湖: 0.1 %芜湖: 0.1 %芝加哥: 0.6 %芝加哥: 0.6 %苏州: 0.3 %苏州: 0.3 %荆州: 0.0 %荆州: 0.0 %葫芦岛: 0.1 %葫芦岛: 0.1 %衡水: 0.3 %衡水: 0.3 %衡阳: 0.2 %衡阳: 0.2 %衢州: 0.2 %衢州: 0.2 %西孟加拉: 0.1 %西孟加拉: 0.1 %西宁: 19.1 %西宁: 19.1 %西安: 3.3 %西安: 3.3 %西雅图: 0.1 %西雅图: 0.1 %诺沃克: 0.1 %诺沃克: 0.1 %谢利夫: 0.1 %谢利夫: 0.1 %贵阳: 0.2 %贵阳: 0.2 %赣州: 0.1 %赣州: 0.1 %达州: 0.1 %达州: 0.1 %运城: 0.3 %运城: 0.3 %连云港: 0.0 %连云港: 0.0 %邯郸: 0.2 %邯郸: 0.2 %邵阳: 0.1 %邵阳: 0.1 %郑州: 1.5 %郑州: 1.5 %鄂尔多斯: 0.0 %鄂尔多斯: 0.0 %酒泉: 0.0 %酒泉: 0.0 %重庆: 0.8 %重庆: 0.8 %金华: 0.1 %金华: 0.1 %银川: 0.0 %银川: 0.0 %镇江: 0.1 %镇江: 0.1 %长春: 0.3 %长春: 0.3 %长沙: 0.9 %长沙: 0.9 %长治: 0.1 %长治: 0.1 %阜阳: 0.1 %阜阳: 0.1 %阿什本: 0.0 %阿什本: 0.0 %阿姆斯特丹: 0.0 %阿姆斯特丹: 0.0 %随州: 0.1 %随州: 0.1 %青岛: 0.6 %青岛: 0.6 %首尔: 0.1 %首尔: 0.1 %香港: 0.1 %香港: 0.1 %马鞍山: 0.1 %马鞍山: 0.1 %黄冈: 0.0 %黄冈: 0.0 %黄山: 0.1 %黄山: 0.1 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %其他其他ChinaMatawanResearchSan JoseSeattleThane[]三明上海东京都东莞中卫丹东乌兰察布乌鲁木齐伊利诺伊州伊春伦敦佛山六安内江加利福尼亚州北京北海十堰南京南充南宁南昌南通南阳卡拉奇厦门台北台州台湾合肥呼和浩特咸阳哈尔滨哥伦布商洛喀什嘉兴大同大连天津太原奥尔巴尼威海宁波安康安阳宣城宿州宿迁崇左巴中巴伐利亚州巴音郭楞布加勒斯特常州常德平顶山广州库比蒂诺开封张家口徐州德州德罕成都扬州抚州揭阳文昌斯德哥尔摩新德里新竹无锡昆明晋中晋城朝阳来宾杭州松原林芝枣庄柳州格兰特县桂林楚雄榆林武汉汕头沈阳沧州泉州法兰克福泰州洛杉矶洛阳济南济宁淄博淮北淮南深圳温州渭南湖州湛江漯河潍坊澳门濮阳烟台珠海甘南石嘴山石家庄福州科隆秦皇岛纽约绍兴绵阳聊城自贡舟山芒廷维尤芜湖芝加哥苏州荆州葫芦岛衡水衡阳衢州西孟加拉西宁西安西雅图诺沃克谢利夫贵阳赣州达州运城连云港邯郸邵阳郑州鄂尔多斯酒泉重庆金华银川镇江长春长沙长治阜阳阿什本阿姆斯特丹随州青岛首尔香港马鞍山黄冈黄山齐齐哈尔

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
    Article views(3609) PDF downloads(250) Cited by(53)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint