On modeling sea clutter by noisy chaotic dynamics

Abstract

Modeling sea clutter by chaotic dynamics has been an exciting yet heatedly debated topic. To resolve controversies associated with this approach, we use the scale-dependent Lyapunov exponent (SDLE) to study sea clutter. The SDLE has been shown to be able to unambiguously distinguish chaos from noise. Our analyses of almost 400 sea clutter datasets measured by Professor Simon Haykin suggest that on very short time scales, sea clutter may be classified as noisy chaos, characterized by a parameter γ, which characterizes the speed of information loss. It is shown that γ can be used to very effectively detect low observable targets within sea clutter.