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Proceedings Paper

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

Paper Details

Date Published: 1 December 1991
PDF: 8 pages
Proc. SPIE 1565, Adaptive Signal Processing, (1 December 1991); doi: 10.1117/12.49784
Show Author Affiliations
Henry Leung, McMaster Univ. (Canada)
Simon Haykin, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 1565:
Adaptive Signal Processing
Simon Haykin, Editor(s)

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