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Updated: Jun 22, 2025

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Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions.

Yung-Cheng Pai1, Jean-Fu Kiang1

  • 1Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan.

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|June 27, 2024
PubMed
Summary
This summary is machine-generated.

A novel framework predicts sea clutter attributes based on wind speed, angle, and polarization. This research provides crucial data for enhancing radar performance in diverse ocean environments.

Keywords:
Hwang spectrumJONSWAP spectrumK distributionMonte Carlo methodWeibull distributiongrazing angleparticle swarm optimizationphysical-optics methodpolarizationpower-law distributionprobability density functionradar cross-sectionsea cluttersea-surface profilestatistical parameterswind directionwind speed

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Area of Science:

  • Oceanography
  • Radar Engineering
  • Signal Processing

Background:

  • Sea clutter significantly impacts radar performance in maritime surveillance.
  • Existing models lack comprehensive prediction capabilities across varied operational conditions.
  • Understanding sea clutter dynamics is crucial for effective radar system design.

Purpose of the Study:

  • To propose a complete framework for predicting sea clutter attributes.
  • To analyze sea clutter under varying wind speeds, directions, grazing angles, and polarizations.
  • To provide data for improving radar performance in complex ocean environments.

Main Methods:

  • Utilized empirical spectra (JONSWAP, Hwang) for sea-surface profile generation.
  • Employed the Monte Carlo method for sea-surface realization generation.
  • Applied the physical-optics method to compute normalized radar cross-sections (NRCSs).
  • Regressed NRCS data using K, Weibull, and power-law distributions.

Main Results:

  • Derived statistical parameters and power-law indices for K and Weibull distributions under different conditions.
  • Quantified sea clutter behavior across a range of operational parameters.
  • Established a predictive model for sea clutter characteristics.

Conclusions:

  • The proposed framework offers a comprehensive approach to sea clutter prediction.
  • Derived parameters provide valuable insights for radar system optimization.
  • This study serves as a guideline for future radar measurement tasks and model enhancements.