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Robust Automatic Target Recognition via HRRP Sequence Based on Scatterer Matching.

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Summary
This summary is machine-generated.

This study introduces a new method using singular value decomposition (SVD) for high-resolution range profile (HRRP) sequence matching to improve radar automatic target recognition (ATR). The approach enhances robustness against clutter and aspect variations.

Keywords:
atomic norm minimization (ANM)automatic target recognition (ATR)feature extractionhigh resolution range profile (HRRP)singular value decomposition (SVD)

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Machine Learning for ATR

Background:

  • High-resolution range profiles (HRRP) are crucial for wideband radar automatic target recognition (ATR).
  • Sensitivity to clutter and target aspect variations challenges traditional HRRP-based ATR.
  • Using sequences of HRRPs offers a promising strategy to improve ATR performance.

Purpose of the Study:

  • To propose a novel HRRP sequence-matching method for enhanced ATR.
  • To develop a robust similarity measure for HRRP sequence recognition.
  • To improve the performance of ATR systems by addressing clutter and aspect sensitivity.

Main Methods:

  • Decoupling HRRP sequences into angle and range spaces using Singular Value Decomposition (SVD).
  • Estimating dominant scatterers in the range space via Atomic Norm Minimization (ANM).
  • Measuring scatter similarity using Hausdorff distance and evaluating angle space similarity via left singular vector correlations.
  • Fusing range and angle space similarities weighted by singular values.

Main Results:

  • The proposed SVD-based method effectively decouples HRRP sequences into distinct spaces.
  • Atomic Norm Minimization and Hausdorff distance provide robust scatterer estimation and similarity measurement.
  • Fusion of range and angle space information yields a powerful similarity metric.
  • The method demonstrates robustness in both simulation and outfield experiments.

Conclusions:

  • The novel SVD-based HRRP sequence-matching method offers a robust similarity measure for ATR.
  • This approach effectively alleviates sensitivity to clutter and target aspect variations.
  • The proposed technique enhances the overall performance of radar automatic target recognition systems.