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

Abstract

The authors present a unique viewpoint in describing sea clutter. They demonstrate that the random nature of sea clutter is the result of chaotic phenomena. Using real-life sea clutter data, the authors 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 chaotic behavior. A neural network model incorporating the result of correlation-dimension analysis is used in the reconstruction of the dynamics of sea clutter. The model is in the form of a radial basis function network. The deterministic model for sea clutter is shown to be capable of predicting the evolution of sea clutter as a function of time.<>