Neural network modeling of radar backscatter from an ocean surface using chaos theory

摘要

Abstract: Radar backscatter from an ocean surface, commonly referred to as sea clutter, has a long history of being modeled as a stochastic process. In this paper, we take a fundamentally different viewpoint in describing sea clutter. In particular, we demonstrate that the random nature of sea clutter is indeed the result of chaotic phenomenon. Using different real-life sea clutter data, we use correlation dimension analysis to show that sea clutter can be embedded as a chaotic attractor in a finite dimensional space. This observation provides a reliable indication for the existence of a chaotic behavior. The result of correlation dimension analysis is used to construct a neural network model for sea clutter to reconstruct the dynamics of sea clutter. The model is in the form of a radial basis function (RBF) network. The deterministic model for sea clutter so obtained is shown to be capable of predicting the evolution of sea clutter as a function of time. !12