Chaotic radar signal processing over the sea

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Abstract
Radar backscatter from an ocean surface, commonly referred as sea clutter, has a long history of being modeled as a stochastic process. In this paper, we take a different viewpoint in describing sea clutter. In particular, we demonstrate that the random nature of sea clutter may be explained as a chaotic phenomenon. Using different real sea clutter data, we use a correlation dimension analysis to show that sea clutter can be embedded in a finite dimensional space. The result of correlation dimension analysis is used to construct a neural network predictor to reconstruct the dynamics of sea clutter. The deterministic model so obtained is shown to be capable of predicting the evolution of sea clutter. In addition, the predictive analysis is also applied to analyze the dimension of sea clutter. Using neural network as an approximation of the underlying dynamics of sea clutter, a dynamic-based detection technique is introduced and applied to the problem of detection of growlers (small fragments of icebergs) in sea clutter. The performance of this new detection method is shown to be superior to that of a conventional detector for the real data sets used in this paper.

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