Time-dependent diffusion model for sea clutter dynamics

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 . 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.