Chaotic radar signal processing over the sea

It is demonstrated that the random nature of sea clutter may be explained as a chaotic phenomenon. For different sets of real sea clutter data, a correlation dimension analysis is used 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 for reconstructing the dynamics of sea clutter. The deterministic model so obtained is shown to be capable of predicting the evolution of sea clutter. The predictive analysis is also used to analyze the dimension of sea clutter. Using the 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 detecting growlers (small fragments of icebergs) in sea clutter. The performance of this method is shown to be superior to that of a conventional detector for the real data sets used here