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库尔贝克-莱布勒散度与帕累托指数近似

Kullback-Leibler divergence and the Pareto-Exponential approximation.

作者信息

Weinberg G V

机构信息

Defence Science and Technology Group, Edinburgh, Australia.

出版信息

Springerplus. 2016 May 12;5:604. doi: 10.1186/s40064-016-2253-y. eCollection 2016.

Abstract

Recent radar research interests in the Pareto distribution as a model for X-band maritime surveillance radar clutter returns have resulted in analysis of the asymptotic behaviour of this clutter model. In particular, it is of interest to understand when the Pareto distribution is well approximated by an Exponential distribution. The justification for this is that under the latter clutter model assumption, simpler radar detection schemes can be applied. An information theory approach is introduced to investigate the Pareto-Exponential approximation. By analysing the Kullback-Leibler divergence between the two distributions it is possible to not only assess when the approximation is valid, but to determine, for a given Pareto model, the optimal Exponential approximation.

摘要

最近,将帕累托分布作为X波段海上监视雷达杂波回波模型的雷达研究兴趣,引发了对该杂波模型渐近行为的分析。特别值得关注的是,要了解帕累托分布何时能被指数分布很好地近似。这样做的理由是,在后者的杂波模型假设下,可以应用更简单的雷达检测方案。引入了一种信息论方法来研究帕累托-指数近似。通过分析两种分布之间的库尔贝克-莱布勒散度,不仅可以评估近似何时有效,而且对于给定的帕累托模型,还可以确定最优的指数近似。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9d3/4864786/0e48deea4c77/40064_2016_2253_Fig1_HTML.jpg

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