Volume 4 Issue 5
Nov.  2015
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Article Navigation >  Journal of Radars  > 2015  >  4(5): 600-607
Qing Ji-ming, Xu Hao-yu, Liang Xing-dong, Li Yan-lei. An Improved Phase Gradient Autofocus Algorithm Used in Real-time Processing[J]. Journal of Radars, 2015, 4(5): 600-607. doi: 10.12000/JR15037
Citation: Qing Ji-ming, Xu Hao-yu, Liang Xing-dong, Li Yan-lei. An Improved Phase Gradient Autofocus Algorithm Used in Real-time Processing[J]. Journal of Radars, 2015, 4(5): 600-607. doi: 10.12000/JR15037
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An Improved Phase Gradient Autofocus Algorithm Used in Real-time Processing

DOI: 10.12000/JR15037
  • 1.

    (National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

  • 2.

    (University of Chinese Academy of Sciences, Beijing 100049, China)

  • 3.

    (Shanghai Advanced Researched Institute, University of Chinese Academy of Sciences, Shanghai 201203, China)

Funds:

National 863 Plan Ship-carried UAV Ocean Observation System (2013AA092105), Surveying and Mapping Geographic Information Public Service Industry Research Projects (201412002), and Shanghai Science and Technology Commission Funded Projects (13511503200)

  • Received Date: 2015-04-03
  • Rev Recd Date: 2015-08-11
  • Publish Date: 2015-10-28
    Abstract
  • The Phase Gradient Autofocus (PGA) algorithm can remove the high order phase error effectively, which is of great significance to get high resolution images in real-time processing. While PGA usually needs iteration, which necessitates long working hours. In addition, the performances of the algorithm are not stable in different scene applications. This severely constrains the application of PGA in real-time processing. Isolated scatter selection and windowing are two important algorithmic steps of Phase Gradient Autofocus Algorithm. Therefore, this paper presents an isolated scatter selection method based on sample mean and a windowing method based on pulse envelope. These two methods are highly adaptable to data, which would make the algorithm obtain better stability and need less iteration. The adaptability of the improved PGA is demonstrated with the experimental results of real radar data.

     

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