Volume 5 Issue 1
Feb.  2016
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Article Navigation >  Journal of Radars  > 2016  >  5(1): 42-56
Zhang Zenghui, Yu Wenxian. Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images[J]. Journal of Radars, 2016, 5(1): 42-56. doi: 10.12000/JR15097
Citation: Zhang Zenghui, Yu Wenxian. Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images[J]. Journal of Radars, 2016, 5(1): 42-56. doi: 10.12000/JR15097
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Feature Understanding and Target Detection for Sparse Microwave Synthetic Aperture Radar Images

doi: 10.12000/JR15097

  • Shanghai Key Laboratory of Intelligent Sensing and Recognition, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds:

The National Natural Science Foundation of China (61331015), The National Basic Research Program of China (2010CB731904)

  • Received Date: 2015-08-15
  • Rev Recd Date: 2015-10-19
  • Publish Date: 2016-02-28
    Abstract
  • Sparse microwave imaging using sparse priors of observed scenes in space, time, frequency, or polarization domain and echo data with sampling rate smaller than the traditional Nyquist rate as well as optimization algorithms for reconstructing the microwave images of observed scenes has many advantages over traditional microwave imaging systems. In sparse microwave imaging, image acquisition and representation vary; therefore, new feature analysis and cognitive interpretation theories and methods should be developed based on current research results. In this study, we analyze the statistical properties of sparse Synthetic Aperture Radar (SAR) images and changes in point, line and regional features induced by sparse reconstruction. For SAR images recovered by the spatial sparse model, the statistical distribution degrades, whereas points and lines can be accurately extracted by low sampling rates. Furthermore, the target detection method based on sparse SAR images is studied. Owing to a weak background noise, target detection is easier using sparse SAR images than traditional ones.

     

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