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Volume 38 Issue 12
Jan.  2017
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NIE Xin. Research on Key Technique of Highly Squinted Sliding Spotlight SAR Imaging with Varied Receiving Range Bin[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3122-3128. doi: 10.11999/JEIT160812
Citation: NIE Xin. Research on Key Technique of Highly Squinted Sliding Spotlight SAR Imaging with Varied Receiving Range Bin[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3122-3128. doi: 10.11999/JEIT160812

Research on Key Technique of Highly Squinted Sliding Spotlight SAR Imaging with Varied Receiving Range Bin

doi: 10.11999/JEIT160812
  • Received Date: 2016-07-28
  • Rev Recd Date: 2016-11-09
  • Publish Date: 2016-12-19
  • In highly squinted and high resolution SAR, the data receiving range bin can be continually adjusted to minimize the sampled data in echo collection, but the staggered range cell migration needs extra processing to be eliminated. To enlarge the imaging scene size with a relatively high resolution, sliding spotlight working mode could be used, yet this mode may cause azimuth ambiguity. In this paper, the key technique of highly squinted sliding spotlight SAR imaging with varied receiving range bin is investigated, a sub-aperture based upsampling ambiguity-resolving method is utilized and a new beam segmentation based 2-step PFA method is proposed, which incorporates the stagger compensation to the procedure of motion compensation. The method performs beam segmentation via digital spotlighting preprocessing firstly to generate multiple full-resolution phase histories of smaller image patches, which allow the approximation of planar wavefront in traditional PFA. PFA is used to produce focused fine resolution image for each small patch.Finally, all focused image patches are seamed together to get a full image. This divide-and-conquer approach breaks the image size limit in traditional PFA, and extensively enlarges the valid focused scene suitable for sliding spotlight mode. This new algorithm is validated to be effective and efficient via real data experiments.
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