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Summary
This summary is machine-generated.

This study compares raw data (RD-SIM) and complex image (CI-SIM) sparse SAR imaging methods. RD-SIM offers superior performance in down-sampling and ambiguity suppression scenarios.

Keywords:
Lq regularizationSAR imagingazimuth ambiguityazimuth-range decoupledisplaced phase center antenna (DPCA)down-sampling

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Remote Sensing

Background:

  • Sparse signal processing enhances Synthetic Aperture Radar (SAR) imaging.
  • Existing methods use raw data or complex images for sparse SAR imaging.

Purpose of the Study:

  • To compare and analyze the relationship between raw data-based sparse SAR imaging (RD-SIM) and complex image-based sparse SAR imaging (CI-SIM).
  • To guide the selection of appropriate sparse SAR imaging algorithms for different scenarios.

Main Methods:

  • Comparative analysis of RD-SIM and CI-SIM.
  • Evaluation of imaging performance, noise suppression, sidelobe reduction, and target-to-background ratio (TBR) or azimuth ambiguity-to-signal ratio (AASR).

Main Results:

  • RD-SIM and CI-SIM are equivalent when SAR raw data is fully sampled.
  • Both methods outperform traditional matched filtering (MF).
  • RD-SIM demonstrates superior TBR/AASR performance in down-sampling, non-uniform sampling, and ambiguity suppression.

Conclusions:

  • The choice between RD-SIM and CI-SIM depends on data sampling conditions.
  • RD-SIM is preferred for scenarios involving data undersampling or azimuth ambiguity.
  • Sparse SAR imaging significantly improves performance over MF, with RD-SIM offering advantages in specific challenging conditions.