Dynamic reconstruction of sea clutter using regularized REP networks

We demonstrate the dynamic reconstruction of sea clutter time series using a regularized radial basis function (RBF) network. The dynamic invariants, namely, correlation dimension, Lyapunov exponents and the Kaplan-Yorke dimension of the actual and the reconstructed time series are compared to confirm the goodness of fit of the RBF model to the actual clutter data. A detailed statistical analysis of the horizon of predictability is presented to show the model's ability to capture the underlying dynamics of sea clutter. These convincing results show that the RBF model is capable of approximating the dynamics of sea clutter process in a convincing manner.