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Research on Sparse Targets Detection Methods Based on GLRT in Non-Gaussian Clutter

Received: 29 March 2017     Published: 31 March 2017
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Abstract

For the problem of detecting range-spread target with spare scatterers in non-Gaussian clutter modeled as spherically invariant random vector(SIRV). Firstly, it is assumed that the number of the target scatterers is known and a generalized likelihood ratio test detector based on scatterers number (SN-GLRT) is proposed. Then a sparse scatterers target detector based on GLRT (SST-GLRT) is proposed for unknowing the number of scatterers. The detection statistic of the SSR-GLRT is the weighted sum of the detection statistic of the SN-GLRT. The SSD-SST-GLRT and the NSSD-SST-GLRT are proposed based on the density of the scatterers. The analytical expression relating false alarm probability to detection threshold is deduced and the CFAR property of the SSD-SST-GLRT and the NSSD-SST-GLRT is proved. The results show that the detection performance of NSSD-SST-GLRT is better than the NSDD-GLRT. The detection performance of the SSD-SST-GLRT is better than the SDD-GLRT when the number of scatterers is known. The robustness of the SSD-SST-GLRT is better than the MSDD when the number of scatterers is unknown.

Published in Science Discovery (Volume 5, Issue 1)
DOI 10.11648/j.sd.20170501.15
Page(s) 25-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Non-Gaussian Clutter, Range-Spread Target, Constant False Alarm Rate, Detection

References
[1] 何友,关键,彭应宁,等.雷达自动检测与恒虚警处理 [M], 北京, 清华大学出版社, 1999。
[2] Kelly E J. An adaptive detection algorithm [J]. IEEE Trans-actions on Aerospace and Electronic Systems, 1986, 22(1): 115-127.
[3] 何友,关键,孟祥伟,等.雷达自动检测和CFAR处理方法综述 [J]. 系统工程与电子技术, 2001, 23(1): 9-14,85。
[4] Shui P L, Liu H W, Bao Z. Range-spread target detection based on cross time-frequency distribution reatures of two adjacent received signals [J]. IEEE Transactions on Signal Processing, 2009, 57(10): 3733-3745.
[5] 简涛,何友,苏峰,等.高距离分辨率雷达目标检测研究现状与进展[J]. 宇航学报, 2010, 31(12): 2623-2628. [Jian Tao, He You, Su Feng, et al. Overview of high range resolution radar target detection [J]. Journal of Astronautics, 2010, 31(12): 2623-2628.]。
[6] Hughes P K. A high-resolution radar detection strategy [J]. IEEE Transactions on Aerospace and Electronic Systems, 1983, (19): 663-667.
[7] Gerlach K. Spatially distributed target detection in non-Gaussian clutter [J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(3): 926-934.
[8] Xu S, Shui P, Yan X. CFAR detection of range-spread target in white Gaussian noise using waveform entropy [J]. Electronics Letters, 2010, 46(9): 647-649.
[9] Bandiera F, Orlando, D, Ricci G. CFAR detection strategies for distributed targets under conic constraints [J]. IEEE Transactions on Signal Processing, 2009, 57(9): 3305-3316.
[10] Shui P L, Xu S W, Liu H W. Range-spread target detection using consecutive HRRPs [J]. IEEE Transactions on Aer-ospace and Electronic Systems, 2011, 47(1): 647-665.
[11] He Y, Jian T, Su F, et al. Two adaptive detectors for range-spread targets in non-Gaussian clutter. Sci China Ser F-Inf Sci, 2011, 54: 386–395.
[12] He Y, Jian T, Su F, et al. Novel range-spread target detec-tors in non-Gaussian clutter [J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46: 1312-1328.
[13] Gerlach K. Spatially distributed target detection in non-Gaussian clutter [J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(3): 926-934.
[14] Barton D K. Radar Systems Analysis [M]. Boston: Artech House, 1979.
[15] 简涛,何友,苏峰,等.非高斯杂波下修正的SDD距离扩展目标检测器 [J]. 电子学报, 2009, 37(12): 2662-2667。
[16] Strong Scatterers Integrator Based on ADT in Non-Gaussian Cluter. Gu X. F., Hao X. L., Yang G. L. et.al. Science Discover, 2016,vol 4(1):26-30.
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  • APA Style

    Gu Xinfeng, Yan Shuqiang, Hao Xiaolin, Huang Kun. (2017). Research on Sparse Targets Detection Methods Based on GLRT in Non-Gaussian Clutter. Science Discovery, 5(1), 25-32. https://doi.org/10.11648/j.sd.20170501.15

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    ACS Style

    Gu Xinfeng; Yan Shuqiang; Hao Xiaolin; Huang Kun. Research on Sparse Targets Detection Methods Based on GLRT in Non-Gaussian Clutter. Sci. Discov. 2017, 5(1), 25-32. doi: 10.11648/j.sd.20170501.15

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    AMA Style

    Gu Xinfeng, Yan Shuqiang, Hao Xiaolin, Huang Kun. Research on Sparse Targets Detection Methods Based on GLRT in Non-Gaussian Clutter. Sci Discov. 2017;5(1):25-32. doi: 10.11648/j.sd.20170501.15

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  • @article{10.11648/j.sd.20170501.15,
      author = {Gu Xinfeng and Yan Shuqiang and Hao Xiaolin and Huang Kun},
      title = {Research on Sparse Targets Detection Methods Based on GLRT in Non-Gaussian Clutter},
      journal = {Science Discovery},
      volume = {5},
      number = {1},
      pages = {25-32},
      doi = {10.11648/j.sd.20170501.15},
      url = {https://doi.org/10.11648/j.sd.20170501.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170501.15},
      abstract = {For the problem of detecting range-spread target with spare scatterers in non-Gaussian clutter modeled as spherically invariant random vector(SIRV). Firstly, it is assumed that the number of the target scatterers is known and a generalized likelihood ratio test detector based on scatterers number (SN-GLRT) is proposed. Then a sparse scatterers target detector based on GLRT (SST-GLRT) is proposed for unknowing the number of scatterers. The detection statistic of the SSR-GLRT is the weighted sum of the detection statistic of the SN-GLRT. The SSD-SST-GLRT and the NSSD-SST-GLRT are proposed based on the density of the scatterers. The analytical expression relating false alarm probability to detection threshold is deduced and the CFAR property of the SSD-SST-GLRT and the NSSD-SST-GLRT is proved. The results show that the detection performance of NSSD-SST-GLRT is better than the NSDD-GLRT. The detection performance of the SSD-SST-GLRT is better than the SDD-GLRT when the number of scatterers is known. The robustness of the SSD-SST-GLRT is better than the MSDD when the number of scatterers is unknown.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Research on Sparse Targets Detection Methods Based on GLRT in Non-Gaussian Clutter
    AU  - Gu Xinfeng
    AU  - Yan Shuqiang
    AU  - Hao Xiaolin
    AU  - Huang Kun
    Y1  - 2017/03/31
    PY  - 2017
    N1  - https://doi.org/10.11648/j.sd.20170501.15
    DO  - 10.11648/j.sd.20170501.15
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 25
    EP  - 32
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20170501.15
    AB  - For the problem of detecting range-spread target with spare scatterers in non-Gaussian clutter modeled as spherically invariant random vector(SIRV). Firstly, it is assumed that the number of the target scatterers is known and a generalized likelihood ratio test detector based on scatterers number (SN-GLRT) is proposed. Then a sparse scatterers target detector based on GLRT (SST-GLRT) is proposed for unknowing the number of scatterers. The detection statistic of the SSR-GLRT is the weighted sum of the detection statistic of the SN-GLRT. The SSD-SST-GLRT and the NSSD-SST-GLRT are proposed based on the density of the scatterers. The analytical expression relating false alarm probability to detection threshold is deduced and the CFAR property of the SSD-SST-GLRT and the NSSD-SST-GLRT is proved. The results show that the detection performance of NSSD-SST-GLRT is better than the NSDD-GLRT. The detection performance of the SSD-SST-GLRT is better than the SDD-GLRT when the number of scatterers is known. The robustness of the SSD-SST-GLRT is better than the MSDD when the number of scatterers is unknown.
    VL  - 5
    IS  - 1
    ER  - 

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Author Information
  • China Satellite Maritime Tracking & Control Department, Jiangyin, China

  • China Satellite Maritime Tracking & Control Department, Jiangyin, China

  • Yantai Electricity and Economy Technical Institute, Yantai, China

  • China Satellite Maritime Tracking & Control Department, Jiangyin, China

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