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WANG Junjie, FENG Dejun, WANG Zhisong, et al. Synthetic aperture rader imaging characteristics of electronically controlled time-varying electromagnetic materials[J]. Journal of Radars, 2021, 10(6): 865–873. doi: 10.12000/JR21104
Citation: GUAN Haoliang, ZHANG Shunsheng, and WANG Wenqin. Passive localization countermeasure based on frequency diverse array[J]. Journal of Radars, 2021, 10(6): 833–841. doi: 10.12000/JR21091

Passive Localization Countermeasure Based on Frequency Diverse Array

DOI: 10.12000/JR21091
Funds:  The National Ministries Foundation
More Information
  • Corresponding author: ZHANG Shunsheng, zhangss@uestc.edu.cn
  • Received Date: 2021-07-01
  • Rev Recd Date: 2021-08-21
  • Available Online: 2021-09-08
  • Publish Date: 2021-09-08
  • Passive localization technology is an integral part of electronic warfare. However, most methods for countering passive localization systems, such as radio frequency stealth and electronic interference, have limitations. This paper proposes a new passive localization countermeasure method based on Frequency Diverse Array (FDA). The unique beam scanning property reduces dwell time at a certain azimuth direction, making it difficult for a passive localization system to intercept FDA signals for a long time. In contrast, the time-varying characteristics of the FDA considerably reduce the signal-to-noise ratio of the received signal, increasing the difficulty in accurately detecting the localization information of the radiation source. Thus, using this new technology, the electronic system platform can perceive the external environment through the signals radiated by the FDA antenna while deceiving the enemy’s passive localization system. Both theoretical analysis and numerical results showed that FDA transmitted signal achieved significantly better localization countermeasure performance in direction finding by an interferometer, frequency difference of arrival, and time difference of arrival, which are particularly useful for a new generation of electronic systems with reconnaissance detection and passive localization countermeasures capability.

     

  • [1]
    YOUSSEF M, MAH M, and AGRAWALA A. Challenges: Device-free passive localization for wireless environments[C]. Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking, Québec, Canada, 2007: 222–229.
    [2]
    FRIEDLANDER B. A passive localization algorithm and its accuracy analysis[J]. IEEE Journal of Oceanic Engineering, 1987, 12(1): 234–245. doi: 10.1109/JOE.1987.1145216
    [3]
    ZEKAVAT S A and BUEHRER M. Handbook of Position Location: Theory, Practice and Advances[M]. Oxford: Wiley-Blackwell, 2011: 28–40.
    [4]
    GEZICI S, TIAN Zhi, GIANNAKIS G B, et al. Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks[J]. IEEE Signal Processing Magazine, 2005, 22(4): 70–84. doi: 10.1109/MSP.2005.1458289
    [5]
    杨林森. 目标辐射源无源定位中的时/频差估计[D]. 西安: 西安电子科技大学, 2017.

    YANG Linsen. TDOA/FDOA estimation in passive emitter localization[D]. Xi’an: Xidian University, 2017.
    [6]
    高向颖, 赵拥军, 刘智鑫, 等. 存在站址误差下的时频差稳健定位算法[J]. 雷达学报, 2020, 9(5): 916–924. doi: 10.12000/JR20039

    GAO Xiangying, ZHAO Yongjun, LIU Zhixin, et al. Robust source localization using TDOA and FDOA with receiver location errors[J]. Journal of Radars, 2020, 9(5): 916–924. doi: 10.12000/JR20039
    [7]
    ZOU Yanbin and LIU Huaping. TDOA localization with unknown signal propagation speed and sensor position errors[J]. IEEE Communications Letters, 2020, 24(5): 1024–1027. doi: 10.1109/LCOMM.2020.2968434
    [8]
    GIARETTA A, BALASUBRAMANIAM S, and CONTI M. Security vulnerabilities and countermeasures for target localization in bio-NanoThings communication networks[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(4): 665–676. doi: 10.1109/TIFS.2015.2505632
    [9]
    王诗蕾. 基于预处理的无源定位对抗技术研究[D]. [硕士论文], 电子科技大学, 2016.

    WANG Shilei. Research on preprocessing-based locating countermeasures technology for passive locating system[D]. [Master dissertation], University of Electronic Science and Technology of China, 2016.
    [10]
    SHI Xiaoran, ZHOU Feng, ZHAO Bo, et al. Deception jamming method based on micro-Doppler effect for vehicle target[J]. IET Radar, Sonar & Navigation, 2016, 10(6): 1071–1079. doi: 10.1049/iet-rsn.2015.0371
    [11]
    WANG Fei, SELLATHURAI M, LIU Weigang, et al. Security information factor based airborne radar RF stealth[J]. Journal of Systems Engineering and Electronics, 2015, 26(2): 258–266. doi: 10.1109/JSEE.2015.00031
    [12]
    WANG Wenqin. Moving-target tracking by cognitive RF stealth radar using frequency diverse array antenna[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 3764–3773. doi: 10.1109/TGRS.2016.2527057
    [13]
    LYNCH JR D. Introduction to RF Stealth[M]. Raleigh: SciTech, 2004: 8–12.
    [14]
    XU Jingwei, LIAO Guisheng, ZHU Shengqi, et al. Deceptive jamming suppression with frequency diverse MIMO radar[J]. Signal Processing, 2015, 113: 9–17. doi: 10.1016/j.sigpro.2015.01.014
    [15]
    周超, 刘泉华, 胡程. 间歇采样转发式干扰的时频域辨识与抑制[J]. 雷达学报, 2019, 8(1): 100–106. doi: 10.12000/JR18080

    ZHOU Chao, LIU Quanhua, and HU Cheng. Time-frequency analysis techniques for recognition and suppression of interrupted sampling repeater jamming[J]. Journal of Radars, 2019, 8(1): 100–106. doi: 10.12000/JR18080
    [16]
    许京伟, 朱圣棋, 廖桂生, 等. 频率分集阵雷达技术探讨[J]. 雷达学报, 2018, 7(2): 167–182. doi: 10.12000/JR18023

    XU Jingwei, ZHU Shengqi, LIAO Guisheng, et al. An overview of frequency diverse array radar technology[J]. Journal of Radars, 2018, 7(2): 167–182. doi: 10.12000/JR18023
    [17]
    王文钦, 邵怀宗, 陈慧. 频控阵雷达: 概念、原理与应用[J]. 电子与信息学报, 2016, 38(4): 1000–1011. doi: 10.11999/JEIT151235

    WANG Wenqin, SHAO Huaizong, and CHEN Hui. Frequency diverse array radar: Concept, principle and application[J]. Journal of Electronics &Information Technology, 2016, 38(4): 1000–1011. doi: 10.11999/JEIT151235
    [18]
    XIANG Zhe, CHEN Baixiao, and YANG Minglei. Statistical method with dual-polarized MIMO array for target discrimination[J]. IEEE Antennas and Wireless Propagation Letters, 2016, 16: 1313–1316. doi: 10.1109/LAWP.2016.2633433
    [19]
    尹光辉. 基于频率分集阵列的保密通信技术研究[D]. [硕士论文], 西安电子科技大学, 2020.

    YIN Guanghui. Research on secure communication technology based on frequency diversity array[D]. [Master dissertation], Xidian University, 2020.
    [20]
    SAMMARTINO P F, BAKER C J, and GRIFFITHS H D. Frequency diverse MIMO techniques for radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 201–222. doi: 10.1109/TAES.2013.6404099
    [21]
    WANG Yongbing, WANG Wenqin, and SHAO Huaizong. Frequency diverse array radar Cramér-Rao lower bounds for estimating direction, range, and velocity[J]. International Journal of Antennas and Propagation, 2014, 2014: 830869. doi: 10.1155/2014/830869
    [22]
    巩朋成, 刘刚, 黄禾, 等. 频控阵MIMO雷达中基于稀疏迭代的多维信息联合估计方法[J]. 雷达学报, 2018, 7(2): 194–201. doi: 10.12000/JR16121

    GONG Pengcheng, LIU Gang, HUANG He, et al. Multidimensional parameter estimation method based on sparse iteration in FDA-MIMO radar[J]. Journal of Radars, 2018, 7(2): 194–201. doi: 10.12000/JR16121
    [23]
    WANG Wenqin and SHAO Huaizong. Range-angle localization of targets by a double-pulse frequency diverse array radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(1): 106–114. doi: 10.1109/JSTSP.2013.2285528
    [24]
    WANG Wenqin and SO H C. Transmit subaperturing for range and angle estimation in frequency diverse array radar[J]. IEEE Transactions on Signal Processing, 2014, 62(8): 2000–2011. doi: 10.1109/TSP.2014.2305638
    [25]
    桂荣华. 频控阵雷达自适应处理关键技术研究[D]. [博士论文], 电子科技大学, 2020.

    GUI Ronghua. Research on adaptive processing technology for frequency diverse array radar[D]. [Ph.D. dissertation], University of Electronic Science and Technology of China, 2020.
    [26]
    GAO Kuandong, WANG Wenqin, and CAI Jingye. Frequency diverse array and MIMO hybrid radar transmitter design via Cramér-Rao lower bound minimisation[J]. IET Radar, Sonar & Navigation, 2016, 10(9): 1660–1670.
    [27]
    何杰. 飞机射频隐身性能评估指标研究与软件实现[D]. [硕士论文], 南京航空航天大学, 2016.

    HE Jie. Research on aircraft RF stealth performance evaluation indexes and simulation system implementation[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2016.
    [28]
    WANG Liu, WANG Wenqin, GUAN Haoliang, et al. LPI property of FDA transmitted signal[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, in press. doi: 10.1109/TAES.2021.3083402
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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