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Volume 41 Issue 8
Aug.  2019
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Lei YE, Yong WANG, Qiang YANG, Weibo DENG. High Frequency Surface Wave Radar Detector Based on Log-determinant Divergence and Symmetrized Log-determinant Divergence[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1931-1938. doi: 10.11999/JEIT181078
Citation: Lei YE, Yong WANG, Qiang YANG, Weibo DENG. High Frequency Surface Wave Radar Detector Based on Log-determinant Divergence and Symmetrized Log-determinant Divergence[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1931-1938. doi: 10.11999/JEIT181078

High Frequency Surface Wave Radar Detector Based on Log-determinant Divergence and Symmetrized Log-determinant Divergence

doi: 10.11999/JEIT181078
Funds:  The National Natural Science Foundation of China (61701140, 61571159, 61171182), The Fundamental Research Funds for the Central Universities (HIT.MKSTISP.2016 13, HIT.MKSTISP.2016 26)
  • Received Date: 2018-11-23
  • Rev Recd Date: 2019-04-23
  • Available Online: 2019-04-28
  • Publish Date: 2019-08-01
  • High Frequency Surface Wave Radar (HFSWR) utilizes electromagnetic wave diffracting along the earth to detect targets over the horizon. However, the increase of target distance decreases the received echo energy, and this degrades the detection capability. A joint domain matrix Constant False Alarm Rate (CFAR) detector is proposed to improve the detection performance. It employs the multi-dimensional information of signal in azimuth, Doppler velocity and range domain to detect target, and Log-Determinant Divergence (LDD) and Symmetrized Log-Determinant Divergence (SLDD) are used to replace the Riemannian Distance (RD) as the measure of distance. Finally, the experiment results show that the detector presented by the paper can improve the detection performance effectively.
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  • 姚迪, 张鑫, 杨强, 等. 基于空间多波束的高频地波雷达电离层杂波抑制算法[J]. 电子与信息学报, 2017, 39(12): 2827–2833. doi: 10.11999/JEIT170477

    YAO Di, ZHANG Xin, YANG Qiang, et al. Ionospheric clutter suppression algorithm based on space multibeam for high frequency surface wave radar[J]. Journal of Electronics &Information Technology, 2017, 39(12): 2827–2833. doi: 10.11999/JEIT170477
    WANG Yiming, MAO Xingpeng, ZHANG Jie, et al. Detection of vessel targets in sea clutter using in situ sea state measurements with HFSWR[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(2): 302–306. doi: 10.1109/LGRS.2017.2786725
    杨强, 刘永坦. 复杂背景下的二维检测研究[J]. 系统工程与电子技术, 2002, 24(1): 34–37. doi: 10.3321/j.issn:1001-506X.2002.01.010

    YANG Qiang and LIU Yongtan. 2-D detection in complex background[J]. Systems Engineering and Electronics, 2002, 24(1): 34–37. doi: 10.3321/j.issn:1001-506X.2002.01.010
    TURLEY M D E. Hybrid CFAR techniques for HF radar[C]. Radar 97, Edinburgh, UK, 1997: 36–40.
    LI Yang, ZHANG Ning, and YANG Qiang. Characteristic-knowledge-aided spectral detection of high frequency first-order sea echo[J]. Journal of Systems Engineering and Electronics, 2009, 20(4): 718–725.
    WANG Hong and CAI Lujing. On adaptive spatial-temporal processing for airborne surveillance radar systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 660–670. doi: 10.1109/7.303737
    ZHANG Xin, SU Yanhua, YANG Qiang, et al. Space-time adaptive processing-based algorithm for meteor trail suppression in high-frequency surface wave radar[J]. IET Radar, Sonar & Navigation, 2015, 9(4): 429–436. doi: 10.1049/iet-rsn.2014.0300
    衣春雷. 船载高频地波雷达海杂波抑制方法研究[D].[博士论文], 哈尔滨工业大学, 2017.

    YI Chunlei. Study on sea clutter suppression for shipborne high frequency surface wave radar[D]. [Ph.D. dissertation], Harbin Institute of Technology, 2017.
    RADHAKRISHNA RAO C. Information and the accuracy attainable in the estimation of statistical parameters[J]. Bulletin of the Calcutta Mathematical Society, 1945, 37(3): 81–91.
    WANG Meng, NING Zhenhu, XIAO Chuangbai, et al. Sentiment classification based on information geometry and deep belief networks[J]. IEEE Access, 2018, 6: 35206–35213. doi: 10.1109/ACCESS.2018.2848298
    黎湘, 程永强, 王宏强, 等. 雷达信号处理的信息几何方法[M]. 北京: 科学出版社, 2014: 13: 18.
    ZHANG Fode, NG H K T, and SHI Yimin. Information geometry on the curved q-exponential family with application to survival data analysis[J]. Physica A: Statistical Mechanics and its Applications, 2018, 512: 788–802. doi: 10.1016/j.physa.2018.08.143
    TRANSTRUM M K, SARIĆ A T, and STANKOVIĆ A M. Information geometry approach to verification of dynamic models in power systems[J]. IEEE Transactions on Power Systems, 2018, 33(1): 440–450. doi: 10.1109/TPWRS.2017.2692523
    HIAI F and PETZ D. Riemannian metrics on positive definite matrices related to means. II[J]. Linear Algebra and Its Applications, 2012, 436(7): 2117–2136. doi: 10.1016/j.laa.2011.10.029
    刘俊凯, 王雪松, 王涛, 等. 信息几何在脉冲多普勒雷达目标检测中的应用[J]. 国防科技大学学报, 2011, 33(2): 77–80. doi: 10.3969/j.issn.1001-2486.2011.02.018

    LIU Junkai, WANG Xuesong, WANG Tao, et al. Application of information geometry to target detection for pulsed-Doppler radar[J]. Journal of National University of Defense Technology, 2011, 33(2): 77–80. doi: 10.3969/j.issn.1001-2486.2011.02.018
    STEIN C. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution[C]. The Third Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, USA, 1956: 197–206.
    HUA Xiaoqiang, CHENG Yongqiang, WANG Hongqiang, et al. Geometric means and medians with applications to target detection[J]. IET Signal Processing, 2017, 11(6): 711–720. doi: 10.1049/iet-spr.2016.0547
    LENGLET C, ROUSSON M, DERICHE R, et al. Statistics on the manifold of multivariate normal distributions: theory and application to diffusion tensor MRI processing[J]. Journal of Mathematical Imaging and Vision, 2006, 25(3): 423–444. doi: 10.1007/s10851-006-6897-z
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