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GPR Clutter Removal Based on Weighted Nuclear Norm Minimization for Nonparallel Cases.

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A new ground-penetrating radar (GPR) clutter removal method effectively enhances underground target detection. This weighted nuclear norm minimization (WNNM) technique improves signal clarity in nonparallel antenna-ground scenarios.

Keywords:
clutter removalground-penetrating radarlow-rank and sparse decompositionweighted nuclear norm minimization

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Area of Science:

  • Geophysics
  • Electromagnetic Methods
  • Signal Processing

Background:

  • Ground-penetrating radar (GPR) is crucial for underground target detection.
  • Strong clutter often obscures target signals, degrading detection performance.
  • Existing methods struggle with nonparallel antenna-ground surface conditions.

Purpose of the Study:

  • To develop a novel GPR clutter-removal method for nonparallel antenna-ground scenarios.
  • To improve the accuracy and efficiency of underground target detection using GPR.
  • To address the limitations of current clutter-removal techniques.

Main Methods:

  • Proposed a novel GPR clutter-removal method based on weighted nuclear norm minimization (WNNM).
  • Decomposed GPR B-scan images into low-rank clutter and sparse target matrices.
  • Utilized a non-convex weighted nuclear norm with differential singular value weighting.

Main Results:

  • The WNNM method demonstrated superior performance in numerical simulations and real GPR experiments.
  • Comparative analysis showed higher peak signal-to-noise ratio (PSNR) and improvement factor (IF) than state-of-the-art methods.
  • The proposed method is approximately five times faster than Robust Principal Component Analysis (RPCA).

Conclusions:

  • The WNNM method effectively removes clutter in nonparallel GPR scenarios.
  • This technique significantly enhances underground target detection performance.
  • The method's speed and effectiveness make it suitable for practical GPR applications.