Multiscale Characterization of Sea Clutter by Scale-Dependent Lyapunov Exponent

Abstract

Determining whether sea clutter radar returns are stochastic or deterministic is crucial to the successful modelling of sea clutter as well as to facilitate target detection within sea clutter. Despite extensive studies of sea clutter using distributional analysis, chaos analysis, and fractal analysis, the nature of sea clutter is still not well understood. Realizing that the difficulty in sea clutter modeling is due to the multiscale nature of sea clutter, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to better characterize the nonstationary and multiscale nature of sea clutter. SDLE has been shown to readily characterize major models of complex time series, including deterministic chaos, noisy chaos, stochastic oscillations, random processes, random Levy processes, and complex time series with multiple scaling behaviors. With SDLE, we are able to directly show that sea clutter is not chaotic. More importantly, we find a new scaling law suggesting noisy dynamics for sea clutter. The new scaling law has an interesting interpretation in terms of intrinsic predictability of sea clutter, and provides an excellent new means of detecting targets within sea clutter.