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Sidelobe Suppression Techniques for Near-Field Multistatic SAR.

George A J Price1, Chris Moate1, Daniel Andre2

  • 1Radar & Electronic Warfare, QinetiQ, Malvern WR14 3PS, UK.

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Summary

Multirotor Unmanned Air Systems (UAS) enhance Synthetic Aperture Radar (SAR) imaging. Novel algorithms effectively suppress sidelobes in near-field multistatic SAR data, improving image quality for systems like RIBI.

Keywords:
SARUAScompressive sensingmultistatic

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Aerospace Engineering

Background:

  • Multirotor Unmanned Air Systems (UAS) offer enhanced Synthetic Aperture Radar (SAR) imaging capabilities over fixed-wing platforms.
  • Swarming UAS enable significant measurement diversity through spatial and frequency collection variations.
  • Near-field processing is crucial for managing strong sidelobes in multistatic SAR imaging from UAS.

Purpose of the Study:

  • To assess image reconstruction algorithms for simulated near-field multistatic SAR data.
  • To investigate techniques for suppressing sidelobes in the QinetiQ RIBI system.
  • To develop a novel processing approach for improved SAR image reconstruction.

Main Methods:

  • Application of traditional SAR processing, regularised linear regression, and compressive sensing algorithms.
  • Simulation of near-field multistatic SAR data.
  • Evaluation of Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding for sidelobe suppression and scatterer RCS accuracy.

Main Results:

  • Elastic net, Orthogonal Matched Pursuit, and Iterative Hard Thresholding demonstrated effective sidelobe suppression.
  • These algorithms preserved the accuracy of scatterer Radar Cross Section (RCS).
  • A novel combined processing approach was developed, mitigating individual algorithm weaknesses.

Conclusions:

  • Advanced algorithms can successfully suppress sidelobes in near-field multistatic SAR imagery from UAS.
  • The developed processing approach offers improved SAR image reconstruction.
  • Findings are applicable to real-world SAR imaging challenges with complex data.