Volume 4 Issue 2
Jul.  2015
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Article Navigation >  Journal of Radars  > 2015  >  4(2): 172-177
Xu Zhen, Wang Robert, Li Ning, et al.. A novel approach to change detection in SAR images with CNN classification[J]. Journal of Radars, 2017, 6(5): 483–491. DOI: 10.12000/JR17075
Citation: Xing Shu-guang, Lü Xiao-de, Ding Chi-biao. Research on Radar Cross Section Measurement Based on Near-field Imaging of Cylindrical Scanning[J]. Journal of Radars, 2015, 4(2): 172-177. doi: 10.12000/JR14100
shu

Research on Radar Cross Section Measurement Based on Near-field Imaging of Cylindrical Scanning

DOI: 10.12000/JR14100
  • 1.

    (Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

  • 2.

    (Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China)

  • Received Date: 2014-07-07
  • Rev Recd Date: 2014-10-17
  • Publish Date: 2015-04-28
    Abstract
  • A new method of Radar Cross Section (RCS) measurement based on near-field imaging of cylindrical scanning surface is proposed. The method is based on the core assumption that the target consists of ideal isotropic scattered centers. Three-dimensional radar scattered images are obtained by using the proposed method, and then to obtain the RCS of the target, the scattered far field is calculated by summing the fields generated by the equivalent scattered centers. Not only three dimensional radar reflectivity images but also the RCS of targets in certain three dimensional angle areas can be obtained. Compared with circular scanning that can only obtain twodimensional radar reflectivity images and RCS results in two-dimensional angle areas, cylindrical scanning can provide more information about the scattering properties of the targets. The method has strong practicability and its validity is verified by simulations.

     

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    • References(14)
  • [1]
    吴鹏飞, 许小剑. 地面平面场RCS测量异地定标误差分析[J]. 雷达学报, 2012, 1(1): 58-62. Wu Peng-fei and Xu Xiao-jian. Error analysis of relative calibration for RCS measurement on ground plane range[J]. Journal of Radars, 2012, 1(1): 58-62.
    [2]
    Amin F, Mueed A, and Xu Jia-dong. Implementation and results of an RCS measurement system in CATR[C]. IEEE Asia-Pacific Conference on Applied Electromagnetics, Melaka, Malaysia, 2012: 262-267.
    [3]
    Yu Jun-sheng, Liu Xiao-ming, and Yao Yuan. The design and manufacture of a high frequency CATR[C]. Millimeter Waves and THz Technology Workshop, Rome, 2013: 1-2.
    [4]
    Ford K L, Bennett J C, and Holtby D G. Use of a plane-wave synthesis technique to obtain target RCS from near-field measurements with selective feature extraction capability[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(4): 2051-2057.
    [5]
    Qureshi M A, Schmidt C H, and Eibert T F. Efficient near-field far-filed transformation for nonredundant sampling representation on arbitrary surfaces in near-filed antenna measurements[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(4): 2025-2033.
    [6]
    Gao Chao, Yuan Xiao-feng, and Bai Yang. An approach for extrapolating far field radar cross-section from near field measurement[C]. IEEE International Conference on Green Computing and Communications, Beijing, 2013: 1604-1607.
    [7]
    Cown B J and Ryan C E. Near-field scattering measurements for determining complex target RCS[J]. IEEE Transactions on Antennas and Propagation, 1989, 37(5): 576-585.
    [8]
    Odendaal J W and Joubert J. Radar cross measurements using near-field imaging[J]. IEEE Transactions on Instrument Measurement, 1996, 45(6): 948-954.
    [9]
    Broquetas A, Palau J, Jofre L, et al.. Spherical wave near-field imaging and radar cross-section measurement[J]. IEEE Transactions on Antennas and Propagation, 1998, 46(5): 730-735.
    [10]
    Vaupel T and Eibert T F. Comparison and application of near-field isar imaging techniques for far-field radar cross section determination[J]. IEEE Transactions on Antennas and Propagation, 2006, 54(1): 144-151.
    [11]
    Nicholson K J and Wang C H. Improved near-field radar cross-section measurement technique[J]. IEEE Antennas and Wireless Propagation Letters, 2009, 8: 1103-1106.
    [12]
    Li S, Zhu B, Sun H, et al.. NUFFT-Based near-field imaging technique for far-field radar cross section calculation[J]. IEEE Antennas and Wireless Propagation Letters, 2010, 9: 550-553.
    [13]
    Kobayashi H, Osipov A, Suzuki H, et al.. An improved imagebased near-field-to-far-field transformation for cylindrical scanning surfaces[C]. General Assembly and Scientific Symposium, Istanbul, Turkey, 2011: 1-4.
    [14]
    Osipov A, Kobayashi H, Suzuki H, et al.. An improved imaged-based circular near-field-to-far-field transformation[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(2): 989-993.
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