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A Clutter-Analysis-Based STAP for Moving FOD Detection on Runways.

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This study introduces a new method using clutter analysis and Space-time Adaptive Processing (STAP) to detect foreign object debris (FOD) on runways. The advanced technique significantly improves clutter suppression for enhanced aviation safety.

Keywords:
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Area of Science:

  • Radar Signal Processing
  • Aerospace Engineering
  • Remote Sensing

Background:

  • Foreign Object Debris (FOD) poses increasing security and economic risks to civil aviation.
  • Synthetic Aperture Radars (SARs) show potential for FOD detection but struggle with distinguishing targets from strong clutter.
  • Existing methods lack effective clutter suppression in inhomogeneous clutter backgrounds.

Purpose of the Study:

  • To propose a clutter-analysis-based Space-time Adaptive Processing (STAP) method for effective FOD detection.
  • To achieve robust clutter suppression and moving FOD indication in challenging environments.
  • To enhance the reliability of radar systems for runway surveillance.

Main Methods:

  • Dividing radar coverage into equal scattering cells in a rectangular coordinate system.
  • Measuring normalized Radar Cross Sections (RCSs) in the X-band to modify traditional models.
  • Deriving theoretical clutter covariance by describing clutter expressions in space and time domains.

Main Results:

  • Effective detection of FODs with reflection higher than -30 dBsm using a Linear Constraint Minimum Variance (LCMV) filter.
  • Successful indication of a -40 dBsm target in Doppler.
  • Achieved 60dB deeper clutter suppression compared to training-sample-coupled STAP under identical conditions.

Conclusions:

  • The proposed clutter-analysis-based STAP method offers superior performance in FOD detection.
  • This approach significantly enhances clutter suppression capabilities for radar systems.
  • The method provides a viable solution for improving aviation safety by detecting runway debris.