PDF(Pubmed)
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.
摘要：
一个完整的预测不同作战条件下海杂波属性的框架,由风速指定，风向,放牧角度,和两极分化，这是第一次提出。该框架由经验光谱组成，以表征不同风速下的海面剖面，蒙特卡罗方法生成海面剖面的实现，从单个海面实现计算归一化雷达横截面(NRCS)的物理光学方法，以及NRCS数据（海杂波）的回归，其经验概率密度函数（PDF）以一些统计参数为特征。采用JONSWAP和Hwang海浪谱来实现低风速和高风速下的海面剖面，分别。NRCS的概率密度函数用K和Weibull分布进行回归，每个都有两个参数。弱信号和强信号的异常区域中的概率密度函数用幂律分布进行回归，每个都以索引为特征。在不同的运行条件下，首次得出了K和Weibull分布的统计参数和幂律指数。该研究揭示了海杂波的简洁信息，可用于改善各种复杂海洋环境中的雷达性能。提出的框架可以用作设计未来测量任务的参考或指南，以增强现有的海浪谱经验模型，归一化雷达截面，等等。
