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: ZHU Dan, XU Weiyuan, CHEN Wenjuan, et al. Distributed multi-target localization system based on optical wavelength division multiplexing network[J]. Journal of Radars, 2019, 8(2): 171–177. doi: 10.12000/JR19028

Distributed Multi-target Localization System Based on Optical Wavelength Division Multiplexing Network

DOI: 10.12000/JR19028
Funds:  The Natural Science Foundation of Jiangsu Province (BK20160082), The Jiangsu Provincial Program for High-level Talents in Six Areas (DZXX-030), The Jiangsu Province "333" Project (BRA2018042), The Fundamental Research Funds for Central Universities (NE2017002, NC2018005)
More Information
  • Corresponding author: PAN Shilong, pans@nuaa.edu.cn
  • Received Date: 2019-02-21
  • Rev Recd Date: 2019-04-03
  • Available Online: 2019-04-15
  • Publish Date: 2019-04-01
  • A distributed multi-target localization system based on optical Wavelength Division Multiplexing (WDM) network is demonstrated. The wideband orthogonal waveforms are generated by introducing the chaotic OptoElectronic Oscillator (OEO). The optical WDM network is introduced to transmit the wideband signals from multiple distributed transmitting and receiving units to the central station for processing, and the accurate localization of multiple targets is achieved based on the time of arrival localization method. The multiple optical carriers are generated at the central station, the complex processing to achieve the high-precision of the target localization is supported by the resources at the central station, and the remote transmitting and receiving units are simplified. Moreover, a proof of concept of the distributed multi-target localization system based on optical WDM network is obtained. The localization system comprising two transmitters and two receivers is experimentally established. The orthogonal chaotic waveforms with the frequency range of 3.1~10.6 GHz are successfully generated from the chaotic OEOs. The two-dimensional localization of two targets is realized via the maximum positioning error of 7.09 cm. Additionally, the reconfiguration of the system is experimentally verified.

     

  • [1]
    SHEN J Y, MOLISCH A F, and SALMI J. Accurate passive location estimation using TOA measurements[J]. IEEE Transactions on Wireless Communications, 2012, 11(6): 2182–2192. doi: 10.1109/TWC.2012.040412.110697
    [2]
    GRODENSKY D, KRAVITZ D, and ZADOK A. Ultra-wideband microwave-photonic noise radar based on optical waveform generation[J]. IEEE Photonics Technology Letters, 2012, 24(10): 839–841. doi: 10.1109/LPT.2012.2188889
    [3]
    ZHENG Jianyu, WANG Hui, FU Jianbin, et al. Fiber-distributed ultra-wideband noise radar with steerable power spectrum and colorless base station[J]. Optics Express, 2014, 22(5): 4896–4907. doi: 10.1364/OE.22.004896
    [4]
    LLORENTE R, MORANT M, AMIOT N, et al. Novel photonic analog-to-digital converter architecture for precise localization of ultra-wide band radio transmitters[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(6): 1321–1327. doi: 10.1109/JSAC.2011.110619
    [5]
    FU Jianbin and PAN Shilong. Fiber-connected UWB sensor network for high-resolution localization using optical time-division multiplexing[J]. Optics Express, 2013, 21(18): 21218–21223. doi: 10.1364/OE.21.021218
    [6]
    YAO Tingfeng, ZHU Dan, LIU Shifeng, et al. Wavelength-division multiplexed fiber-connected sensor network for SLource localization[J]. IEEE Photonics Technology Letters, 2014, 26(18): 1874–1877. doi: 10.1109/LPT.2014.2336796
    [7]
    ZHANG Mingjiang, JI Yongning, ZHANG Yongning, et al. Remote radar based on chaos generation and radio over fiber[J]. IEEE Photonics Journal, 2014, 6(5): 7902412. doi: 10.1109/JPHOT.2014.2352628
    [8]
    KANNO A and KAWANISHI T. Broadband frequency-modulated continuous-wave signal generation by optical modulation technique[J]. Journal of Lightwave Technology, 2014, 32(20): 3566–3572. doi: 10.1109/JLT.2014.2318724
    [9]
    FU Jianbin, ZHANG Fangzheng, ZHU Dan, et al. Fiber-distributed ultra-wideband radar network based on wavelength reusing transceivers[J]. Optics Express, 2018, 26(14): 18457–18469. doi: 10.1364/OE.26.018457
    [10]
    YAO Tingfeng, ZHU Dan, BEN De, et al. Distributed MIMO chaotic radar based on wavelength-division multiplexing technology[J]. Optics Letters, 2015, 40(8): 1631–1634. doi: 10.1364/OL.40.001631
    [11]
    徐威远. 基于光纤网络架构的分布式多目标定位系统[D]. [硕士论文], 南京航空航天大学, 2017.

    XU Weiyuan. Research on distributed localization system based on optical fiber network[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2017.
    [12]
    姚汀峰. 基于微波光子技术的分布式雷达研究[D]. [硕士论文], 南京航空航天大学, 2015.

    YAO Tingfeng. Research on distributed radar based on microwave photonics technology[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2015.
  • 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
  • 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 Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04010203040
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 13.8 %FULLTEXT: 13.8 %META: 73.2 %META: 73.2 %PDF: 13.0 %PDF: 13.0 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 20.7 %其他: 20.7 %其他: 0.9 %其他: 0.9 %Central District: 0.1 %Central District: 0.1 %China: 0.9 %China: 0.9 %India: 0.1 %India: 0.1 %Matawan: 0.2 %Matawan: 0.2 %Osaka: 0.0 %Osaka: 0.0 %Rochester: 0.1 %Rochester: 0.1 %XX: 0.6 %XX: 0.6 %[]: 0.8 %[]: 0.8 %上海: 1.1 %上海: 1.1 %东京: 0.1 %东京: 0.1 %东京都: 0.1 %东京都: 0.1 %东莞: 0.2 %东莞: 0.2 %佛山: 0.1 %佛山: 0.1 %内蒙古自治区: 0.0 %内蒙古自治区: 0.0 %凤凰城: 0.0 %凤凰城: 0.0 %利斯莫尔: 0.2 %利斯莫尔: 0.2 %包头: 0.0 %包头: 0.0 %北京: 15.5 %北京: 15.5 %十堰: 0.0 %十堰: 0.0 %南京: 1.1 %南京: 1.1 %南宁: 0.2 %南宁: 0.2 %南昌: 0.1 %南昌: 0.1 %南通: 0.0 %南通: 0.0 %厦门: 0.2 %厦门: 0.2 %台北: 0.1 %台北: 0.1 %合肥: 0.1 %合肥: 0.1 %吉林: 0.1 %吉林: 0.1 %呼和浩特: 0.1 %呼和浩特: 0.1 %咸阳: 0.1 %咸阳: 0.1 %哈尔滨: 0.3 %哈尔滨: 0.3 %哥伦布: 0.0 %哥伦布: 0.0 %嘉兴: 0.0 %嘉兴: 0.0 %圣克拉拉: 0.0 %圣克拉拉: 0.0 %圣彼得堡: 0.2 %圣彼得堡: 0.2 %夏尔迦: 0.1 %夏尔迦: 0.1 %大连: 0.0 %大连: 0.0 %天津: 0.3 %天津: 0.3 %太原: 0.0 %太原: 0.0 %娄底: 0.0 %娄底: 0.0 %宁波: 0.1 %宁波: 0.1 %安康: 0.1 %安康: 0.1 %宣城: 0.1 %宣城: 0.1 %宿迁: 0.0 %宿迁: 0.0 %巴中: 0.1 %巴中: 0.1 %常州: 0.2 %常州: 0.2 %平顶山: 0.0 %平顶山: 0.0 %广州: 0.7 %广州: 0.7 %广州市天河区: 0.1 %广州市天河区: 0.1 %库比蒂诺: 0.0 %库比蒂诺: 0.0 %延安: 0.0 %延安: 0.0 %张家口: 0.8 %张家口: 0.8 %徐州: 0.2 %徐州: 0.2 %恩施: 0.0 %恩施: 0.0 %成都: 0.7 %成都: 0.7 %成都市新都区: 0.0 %成都市新都区: 0.0 %扬州: 0.0 %扬州: 0.0 %新德里: 0.1 %新德里: 0.1 %无锡: 0.1 %无锡: 0.1 %昆明: 0.3 %昆明: 0.3 %晋中: 0.1 %晋中: 0.1 %朝阳: 0.1 %朝阳: 0.1 %杭州: 1.2 %杭州: 1.2 %桂林: 0.0 %桂林: 0.0 %武汉: 1.1 %武汉: 1.1 %武汉市: 0.0 %武汉市: 0.0 %汕头: 0.0 %汕头: 0.0 %沈阳: 0.2 %沈阳: 0.2 %泰安: 0.0 %泰安: 0.0 %洛杉矶: 0.2 %洛杉矶: 0.2 %济南: 0.3 %济南: 0.3 %海口: 0.1 %海口: 0.1 %淄博: 0.1 %淄博: 0.1 %淮安: 0.0 %淮安: 0.0 %深圳: 0.5 %深圳: 0.5 %清远: 0.0 %清远: 0.0 %温州: 0.1 %温州: 0.1 %渭南: 0.0 %渭南: 0.0 %湘潭: 0.0 %湘潭: 0.0 %湛江: 0.0 %湛江: 0.0 %漯河: 0.3 %漯河: 0.3 %潍坊: 0.0 %潍坊: 0.0 %珠海: 0.0 %珠海: 0.0 %盘锦: 0.0 %盘锦: 0.0 %石家庄: 0.0 %石家庄: 0.0 %福州: 0.8 %福州: 0.8 %福州市: 0.1 %福州市: 0.1 %纽约: 0.2 %纽约: 0.2 %绵阳: 0.0 %绵阳: 0.0 %美国伊利诺斯芝加哥: 0.2 %美国伊利诺斯芝加哥: 0.2 %芒廷维尤: 14.0 %芒廷维尤: 14.0 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.1 %苏州: 0.1 %英国: 0.0 %英国: 0.0 %荆州: 0.0 %荆州: 0.0 %莆田: 0.1 %莆田: 0.1 %萨克拉门托: 0.0 %萨克拉门托: 0.0 %衢州: 0.0 %衢州: 0.0 %西宁: 27.8 %西宁: 27.8 %西安: 0.8 %西安: 0.8 %诺沃克: 0.0 %诺沃克: 0.0 %贵阳: 0.1 %贵阳: 0.1 %运城: 0.1 %运城: 0.1 %郑州: 0.3 %郑州: 0.3 %重庆: 0.2 %重庆: 0.2 %银川: 0.3 %银川: 0.3 %长沙: 0.6 %长沙: 0.6 %阿勒泰: 0.0 %阿勒泰: 0.0 %青岛: 0.1 %青岛: 0.1 %韶关: 0.0 %韶关: 0.0 %香港: 0.0 %香港: 0.0 %马鞍山: 0.1 %马鞍山: 0.1 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %龙岩: 0.0 %龙岩: 0.0 %其他其他Central DistrictChinaIndiaMatawanOsakaRochesterXX[]上海东京东京都东莞佛山内蒙古自治区凤凰城利斯莫尔包头北京十堰南京南宁南昌南通厦门台北合肥吉林呼和浩特咸阳哈尔滨哥伦布嘉兴圣克拉拉圣彼得堡夏尔迦大连天津太原娄底宁波安康宣城宿迁巴中常州平顶山广州广州市天河区库比蒂诺延安张家口徐州恩施成都成都市新都区扬州新德里无锡昆明晋中朝阳杭州桂林武汉武汉市汕头沈阳泰安洛杉矶济南海口淄博淮安深圳清远温州渭南湘潭湛江漯河潍坊珠海盘锦石家庄福州福州市纽约绵阳美国伊利诺斯芝加哥芒廷维尤芝加哥苏州英国荆州莆田萨克拉门托衢州西宁西安诺沃克贵阳运城郑州重庆银川长沙阿勒泰青岛韶关香港马鞍山齐齐哈尔龙岩

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(2908) PDF downloads(255) Cited by(3)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint