A Data-Driven Approach to Modeling Sea Clutter Stochastic Differential Equations

The development of a family of data-driven methods, called dynamic mode decomposition, for modeling the behavior of dynamical systems through the approximation of the associated Koopman operator, has led to a rapid increase in the related research. Separately, the modeling and algorithm development for target detection in the presence of sea clutter often involves probability density function descriptions of the amplitude process, which ignores time dependency in data. In this paper, we combine these data-driven methods with a stochastic differential equation model of radar scattering from the sea surface for the purpose of sea state change detection. This approach relies on building a dynamic model of sea clutter directly from the radar measurements, without the need to estimate parameters of underlying equations. Using this model, an anomaly detection scheme is demonstrated using a Kalman filtering approach constructed from the Koopman model that is able to identify changes in the sea state.