ABSTRACT

Sea clutter models play a critical role in cognitive radar waveform design and target detection. In this paper, a new sea clutter modelling using Stochastic Differential Equations (SDEs) is proposed to improve clutter amplitude dynamic prediction. The Extended Vasicek model (EV), a nonstationary time-varying diffusion model, is leveraged as the framework. Model parameters are estimated using the observations in a short time interval in temporal dimension before the prediction time, providing an accurate and real-time prediction which is critical in cognitive radars deployed in marine environments. The method is evaluated in terms of mean-squared Error (MSE), mean absolute error (MAE), mean relative error (MRE), relative standard error (RSE), the complementary cumulative distribution function (CCDF) and the Kolmogorov-Smirnov statistical distance $D_{K-S}$. The performance of the method is compared through experimental radar data with the state-of-the-art models such as generalized autoRegressive conditional heteroskedasticity (GARCH) model, Weibull, log-normal, K and log-logistic distributions. It is demonstrated that the proposed model outperforms other state-of-the-art methods in terms of all the above-mentioned metrics.

摘要

海杂波模型在认知雷达波形设计和目标检测中发挥着关键作用。在本文中，提出了一种使用随机微分方程 (SDE) 的新海杂波建模，以改进杂波幅度动态预测。Extended Vasicek 模型 (EV)，一种非平稳的时变扩散模型，被用作框架。在预测时间之前，使用时间维度的短时间间隔内的观测来估计模型参数，提供准确和实时的预测，这对于部署在海洋环境中的认知雷达至关重要。该方法根据均方误差 (MSE)、平均绝对误差 (MAE)、平均相对误差 (MRE)、相对标准误差 (RSE)、互补累积分布函数 (CCDF) 和 Kolmogorov-Smirnov 统计距离$D_{钾-秒}$. 该方法的性能通过实验雷达数据与最先进的模型进行比较，例如广义自回归条件异方差 (GARCH) 模型、Weibull、对数正态分布、K 和对数逻辑分布。事实证明，就所有上述指标而言，所提出的模型优于其他最先进的方法。