Volume 9 Issue 2
May  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(2): 335-342
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: HUANG Yinli, SUN Lu, GUO Liang, et al. Ship detection algorithm based on spatially variant apodization sidelobe suppression and order statistic-constant false alarm rate[J]. Journal of Radars, 2020, 9(2): 335–342. doi: 10.12000/JR19082
shu

Ship Detection Algorithm Based on Spatially Variant Apodization Sidelobe Suppression and Order Statistic-Constant False Alarm Rate

DOI: 10.12000/JR19082
  • 1.

    School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, China

  • 2.

    93128 troops of the Chinese people’s Liberation Army, Beijing 100085, China

  • 3.

    National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • 4.

    College of Geomatics, Xi’an University of Science and Technology, Xi’an 710000, China

  • 5.

    State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China

Funds:  The National Natural Science Foundation of China (61001210), The National Key R&D Program of China (2017YFC1405600), The Natural Science Fundamental of Shaanxi Province (2017JQ6021), The Fundamental Research Funds for the Central Universities (JB180213), Open Research Fund of State Key Laboratory of Pulsed Power Laser Technology (SKL2018KF06), The Research Plan Project of National University of Defense Technology (ZK180102)
More Information
  • Corresponding author: GUO Liang, lguo@mail.xidian.edu.cn
  • Received Date: 2019-09-10
  • Rev Recd Date: 2019-12-09
    • Available Online: 2019-12-30
  • Publish Date: 2020-04-01
    Abstract
  • The special imaging mechanism of the Synthetic Aperture Radar (SAR) causes the sidelobe effect on SAR images. In target detection, the sidelobe effect changes the shapes of strong reflective targets, which results in the problems of localization difficulty and localization error. To solve this problem, this paper proposes a ship detection algorithm based on Spatially Variant Apodization (SVA) and Order Statistic-Constant False Alarm Rate (OS-CFAR). First, the global-CFAR algorithm is used to prescreen the potential target points, which reduces the computational burden of the following steps. Second, the SVA algorithm is modified to improve the speed of sidelobe suppression and applied to the raw complex image data. Then, the nonlinear method OS-CFAR is used to detect the targets on the processed image, and the morphological dilation processing is used to make up for the wrong suppressed points caused by the SVA algorithm. Finally, the GF-3 SAR images are used to test the algorithm and the comparison of the image contrast and detected numbers in the results with SVA and without SVA verifies the effectiveness of the proposed algorithm.

     

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    • References(16)
  • [1]
    FAWZY Z M, EL-SAMIE F E A, and FOUAD M. Processing of synthetic aperture radar data using frequency modulated signals[J]. Wireless Personal Communications, 2019, 107(2): 1061–1076. doi: 10.1007/s11277-019-06317-x
    [2]
    KERR D E. Propagation of Short Radio Waves[M]. New York: McGraw-Hill, 1951.
    [3]
    MOREIRA A, PRATS-IRAOLA P, YOUNIS M, et al. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6–43. doi: 10.1109/MGRS.2013.2248301
    [4]
    HARRIS F J. On the use of windows for harmonic analysis with the discrete Fourier transform[J]. Proceedings of the IEEE, 1978, 66(1): 51–83. doi: 10.1109/PROC.1978.10837
    [5]
    STANKWITZ H C, DALLAIRE R J, and FIENUP J R. Nonlinear apodization for sidelobe control in SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(1): 267–279. doi: 10.1109/7.366309
    [6]
    王一丁, 纪慧波, 洪峻. 变量切趾技术在SAR/ISAR图像处理中的应用[J]. 电子与信息学报, 2003, 25(12): 1622–1627.

    WANG Yiding, JI Huibo, and HONG Jun. Application of apodization method in SAR/ISAR processing[J]. Journal of Electronics &Information Technology, 2003, 25(12): 1622–1627.
    [7]
    SMITH B H. Generalization of spatially variant apodization to noninteger nyquist sampling rates[J]. IEEE Transactions on Image Processing, 2000, 9(6): 1088–1093. doi: 10.1109/83.846250
    [8]
    CASTILLO-RUBIO C, LLORENTE-ROMANO S, and BURGOS-GARCIA M. Robust SVA method for every sampling rate condition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(2): 571–580. doi: 10.1109/TAES.2007.4285354
    [9]
    NI Chong, WANG Yanfei, XU Xianghui, et al. A SAR sidelobe suppression algorithm based on modified spatially variant apodization[J]. Science China Technological Sciences, 2010, 53(9): 2542–2551. doi: 10.1007/s11431-010-4035-z
    [10]
    LIU Min, LI Zhou, and LIU Lu. A novel sidelobe reduction algorithm based on two-dimensional sidelobe correction using D-SVA for squint SAR images[J]. Sensors, 2018, 18(3): 783. doi: 10.3390/s18030783
    [11]
    El-DARYMLI K, MCGUIRE P, POWER D, et al. Target detection in synthetic aperture radar imagery: A state-of-the-art survey[J]. Journal of Applied Remote Sensing, 2013, 7(7): 071598.
    [12]
    ZHAO Bo, CHEN Li, ZHOU Xiaoyang, et al. Target detection from SAR images based on wavelet transform de-noise and improved CFAR[C]. Proceedings of SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, Yichang, China, 2009: 749539.
    [13]
    KAPLAN L M. Improved SAR target detection via extended fractal features[J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 436–451. doi: 10.1109/7.937460
    [14]
    XING X W, CHEN Z L, ZOU H X, et al. A fast algorithm based on two-stage CFAR for detecting ships in SAR images[C]. The 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar, Shanxi, China, 2010.
    [15]
    SMITH M E and VARSHNEY P K. VI-CFAR: A novel CFAR algorithm based on data variability[C]. 1997 IEEE National Radar Conference, Syracuse, USA, 1997.
    [16]
    GANDHI P P and KASSAM S A. Analysis of CFAR processors in nonhomogeneous background[J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 427–445. doi: 10.1109/7.7185
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