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Related Experiment Video

Updated: Dec 15, 2025

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A Single-Dataset-Based Pre-Processing Joint Domain Localized Algorithm for Clutter-Suppression in Shipborne

Liang Guo1, Xin Zhang1,2, Di Yao2,3

  • 1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|July 9, 2020
PubMed
Summary
This summary is machine-generated.

Shipborne high-frequency surface-wave radar (HFSWR) faces challenges detecting low-velocity targets due to sea clutter. A novel training sample acquisition method improves space-time adaptive processing (STAP) performance for enhanced target detection.

Keywords:
clutter-suppressionsignal processingspace time adaptive processing

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Oceanographic Remote Sensing

Background:

  • Shipborne high-frequency surface-wave radar (HFSWR) systems are crucial for maritime surveillance.
  • Platform motion broadens sea clutter spectra, masking low-velocity targets like ships.
  • Traditional space-time adaptive processing (STAP) methods suffer performance degradation in HFSWR due to limited training samples.

Purpose of the Study:

  • To propose an innovative training sample acquisition method for joint domain localized (JDL) reduced-rank STAP.
  • To enhance the performance of clutter suppression in shipborne HFSWR systems.
  • To improve the detection of low-velocity targets masked by sea clutter.

Main Methods:

  • Introduced the unscented transformation as a preprocessing step using single range cell data.
  • Acquired adequate homogeneous secondary data and a roughly estimated clutter covariance matrix (CCM).
  • Calculated an accurate CCM by integrating approximate CCMs from different range cells.

Main Results:

  • The proposed method effectively generates sufficient homogeneous secondary data.
  • Accurate clutter covariance matrices (CCMs) were obtained by integrating estimates.
  • Demonstrated superior signal-to-clutter-plus-noise ratio (SCNR) improvement compared to existing algorithms.

Conclusions:

  • The novel training sample acquisition method significantly enhances STAP performance in shipborne HFSWR.
  • The approach provides a robust solution for detecting low-velocity targets in challenging sea clutter environments.
  • Real-world data validation confirms the effectiveness and superiority of the proposed clutter-suppression technique.