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

## TL;DR

This paper introduces a new framework to predict sea clutter radar data under various ocean conditions, using wind speed and polarization to improve radar performance.

## Contribution

The framework is the first to predict sea clutter attributes using empirical spectra, Monte Carlo simulations, and statistical regression under different operational conditions.

## Key findings

- Empirical spectra and Monte Carlo methods generate realistic sea-surface profiles for different wind speeds.
- K and Weibull distributions accurately model NRCS probability density functions with derived statistical parameters.
- Power-law distributions effectively model outlier regions of weak and strong radar signals.

## Abstract

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.