干扰背景下u滤波器的高斯化函数

Wang Pingbo, Liu Feng, C. Zhiming, Tang Suofu
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引用次数: 2

摘要

高斯化通过大的减弱,小的增强,增强了样本的高斯性,提高了后续相关检验的性能。首先给出了高斯化滤波器的明确定义和评价滤波性能的实用方法。其次,基于概率密度函数及其衍生物,提出并研究了一种典型的高斯化滤波器——u型滤波器。用湖泊试验数据举例说明。最后,讨论了该方法在频谱估计和Rao检验中的两种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
U-filter's gaussianization function for interference background
By weakening the bigger and strengthening the smaller, gaussianization can enhance the gaussianity of samples and improve performance of subsequent correlation test. Firstly, an explicit definition on gaussianizing filter and a practical method to evaluate the filtering performance are given. Secondly, based on the probability density function and its derivate, one typical gaussianizing filters, so-called U-filter, are proposed and studied. Instances with lake trial data are illustrated. Finally, two applications, one in spectrum estimation and the other in Rao test, are discussed.
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