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Multiscale Characterization of Sea Clutter by Scale-Dependent Lyapunov Exponent

机译：尺度相关的Lyapunov指数对海杂波的多尺度表征

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摘要

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 1/f 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.
机译：确定海杂波雷达返回是随机的还是确定性的，对于成功建立海杂波模型以及促进海杂波内的目标检测至关重要。尽管使用分布分析，混沌分析和分形分析对海杂波进行了广泛的研究，但对海杂波的性质仍知之甚少。意识到海杂波建模的困难是由于海杂波的多尺度性质所致，我们采用了一种新的多尺度复杂性度量，即与尺度有关的李雅普诺夫指数（SDLE），以更好地表征海杂波的非平​​稳和多尺度性质。事实证明，SDLE可以轻松表征复杂时间序列的主要模型，包括确定性混沌，嘈杂混沌，随机振荡，随机1 / f过程，随机征税过程以及具有多种缩放行为的复杂时间序列。借助SDLE，我们可以直接表明海杂波并不混乱。更重要的是，我们发现了一条新的比例定律，暗示了海杂波的噪声动态。新的缩放定律对海杂波的内在可预测性具有有趣的解释，并为检测海杂波内的目标提供了极好的新手段。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|584252.1-584252.9|共9页
  • 作者

    Jing Hu; Jianbo Gao;

  • 作者单位

    School of Mechanical Engineering, Guangxi University, 100 Daxue Road, Nanning, Guangxi 530005, China,PMB Intelligence LLC, Sunnyvale, CA 94087, USA;

    School of Mechanical Engineering, Guangxi University, 100 Daxue Road, Nanning, Guangxi 530005, China,PMB Intelligence LLC, Sunnyvale, CA 94087, USA;

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