Volume 4 Issue 1
Apr.  2015
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Article Navigation >  Journal of Radars  > 2015  >  4(1): 20-28
Yang Jin-long, Liu Feng-mei, Wang Dong, Ge Hong-wei. Affinity Propagation Based Measurement Partition Algorithm for Multiple Extended Target Tracking[J]. Journal of Radars, 2015, 4(4): 452-459. doi: 10.12000/JR15003
Citation: Shi Jun, Zhang Xiao-ling, Wei Shun-jun, Xiang Gao, Yang Jian-yu. An Optimal DEM Reconstruction Method for Linear Array Synthetic Aperture Radar Based on Variational Model[J]. Journal of Radars, 2015, 4(1): 20-28. doi: 10.12000/JR14136
shu

An Optimal DEM Reconstruction Method for Linear Array Synthetic Aperture Radar Based on Variational Model

DOI: 10.12000/JR14136

  • (School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Received Date: 2014-11-20
  • Rev Recd Date: 2015-01-25
  • Publish Date: 2015-02-28
    Abstract
  • Downward-looking Linear Array Synthetic Aperture Radar (LASAR) has many potential applications in the topographic mapping, disaster monitoring and reconnaissance applications, especially in the mountainous area. However, limited by the sizes of platforms, its resolution in the linear array direction is always far lower than those in the range and azimuth directions. This disadvantage leads to the blurring of Three-Dimensional (3D) images in the linear array direction, and restricts the application of LASAR. To date, the research on 3D SAR image enhancement has focused on the sparse recovery technique. In this case, the one-to-one mapping of Digital Elevation Model (DEM) brakes down. To overcome this, an optimal DEM reconstruction method for LASAR based on the variational model is discussed in an effort to optimize the DEM and the associated scattering coefficient map, and to minimize the Mean Square Error (MSE). Using simulation experiments, it is found that the variational model is more suitable for DEM enhancement applications to all kinds of terrains compared with the Orthogonal Matching Pursuit (OMP)and Least Absolute Shrinkage and Selection Operator (LASSO) methods.

     

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    • References(15)
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