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A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic

Guanqi Tong1, Xingyu Lu1, Jianchao Yang1

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This study introduces a novel joint sparse recovery algorithm to suppress multiple types of radio frequency interference (RFI) in synthetic aperture radar (SAR) images. The method effectively removes interference artifacts without needing original echo data, improving image quality.

Keywords:
alternating direction method of multipliers (ADMM)radio frequency interference (RFI)sparse recoverysynthetic aperture radar (SAR)

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Area of Science:

  • Remote Sensing
  • Signal Processing
  • Electromagnetics

Background:

  • Level-1 synthetic aperture radar (SAR) images are widely used but often corrupted by radio frequency interference (RFI), primarily from ground-based Linear Frequency Modulation (LFM) signals.
  • Existing RFI suppression techniques for SAR images have limitations, including overlooking interference parameters and inability to handle multiple LFM interference types simultaneously.
  • The unavailability of original echo data in some scenarios necessitates RFI suppression directly within the SAR image domain.

Purpose of the Study:

  • To propose a novel joint sparse recovery algorithm for suppressing multiple LFM interference types directly in the SAR image domain.
  • To address the limitations of existing methods by effectively utilizing interference parameters and handling multi-type LFM interference.
  • To reduce image loss while suppressing interference, even when original echo data is unavailable.

Main Methods:

  • A joint sparse recovery algorithm is developed for RFI suppression in the SAR image domain.
  • Focusing operators are constructed for LFM interference based on range-dependent parameter variations and azimuth consistency.
  • An optimization model with a regularization term is employed to suppress multi-LFM interference and minimize image degradation.

Main Results:

  • The proposed algorithm effectively suppresses multiple LFM interference artifacts in SAR images.
  • The method demonstrates superior performance in various simulated scenarios compared to existing approaches.
  • The joint sparse recovery algorithm successfully reduces image loss during interference suppression.

Conclusions:

  • The developed joint sparse recovery algorithm offers an effective solution for multi-LFM interference suppression in SAR images.
  • This approach provides a valuable tool for enhancing the quality of level-1 SAR data, particularly when original echo data is inaccessible.
  • The method's ability to handle multiple interference types and minimize image loss signifies a significant advancement in SAR signal processing.