应该选择哪些累积量来进行转向矢量估计?

T. Kaiser, J. Mendel
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引用次数: 1

摘要

累积量已成功地应用于窄带阵列信号处理领域。这促使我们进行性能分析,找出每种新算法的优缺点。迄今为止,大多数已知的性能分析都是基于样本累积量的渐近协方差,因此被称为渐近性能分析。最近,对于任何类型的信号,任何类型的噪声,任何阵列形状和任意传感器,已经导出了二阶,三阶和四阶样本累积量的有限样本协方差的显式公式。这些公式使有限样本性能分析成为可能。在单源情况下,转向矢量与由二阶累积量或四阶累积量的乘积构成的矢量成正比。这意味着可以通过使用二阶和四阶样本累积量的有限样本协方差公式来研究转向矢量的有限样本(co)方差。因此,本文提出了一个开放的问题“应该选择哪些累积量来进行转向矢量估计?”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Which cumulants should be selected for steering vector estimation?
Cumulants have been successfully applied in the area of narrowband array signal processing. This motivates a performance analysis to find out the strengths and the weaknesses of each new algorithm. Hitherto, most of the known performance analyses are based on the asymptotic covariance of sample cumulants and are therefore called asymptotic performance analyses. Recently, explicit formulas for the finite-sample covariances of second-, third-, and fourth-order sample cumulants for any kind of signal, any kind of noise, any array shape and arbitrary sensors have been derived. These formulas enable a finite-sample performance analysis. In the single source case the steering vector is proportional to a vector built up by a product of second-order cumulants or by fourth-order cumulants. This means that the finite-sample (co)variance of the steering vector can be investigated by using the formulas for the finite-sample covariance of the second- and fourth-order sample cumulant. Hence, the open question "Which cumulants should be selected for steering vector estimation ?"-is addressed in this paper.
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