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

Henry Leung; Simon Haykin

doi:10.1117/12.49784

1 December 1991 Neural network modeling of radar backscatter from an ocean surface using chaos theory

Henry Leung, Simon Haykin

Proceedings Volume 1565, Adaptive Signal Processing; (1991) https://doi.org/10.1117/12.49784
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Henry Leung and Simon Haykin "Neural network modeling of radar backscatter from an ocean surface using chaos theory", Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); https://doi.org/10.1117/12.49784

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