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.

Date Published: 17 April 2008
PDF: 10 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69681G (17 April 2008); doi: 10.1117/12.777204

Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)