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Updated: Jul 9, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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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.

Sensors (Basel, Switzerland)

|June 27, 2024

Summary

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 spectrum JONSWAP spectrum K distribution Monte Carlo method Weibull distribution grazing angle particle swarm optimization physical-optics method polarization power-law distribution probability density function radar cross-section sea clutter sea-surface profile statistical parameters wind direction wind 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.