基于先验知识的Cramer-Rao界分析

R. Boyer
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引用次数: 0

摘要

在多极正弦模型的参数估计中引入一些阻尼/无阻尼极点的先验知识是一个重要的问题,例如在轴承估计或生物医学信号分析中。其原理是将数据正交投影到与已知极点相关的噪声空间上。由于Cramer-Rao下界(CRB)提供了一个可以比较算法性能的基准,因此可以推导与该模型相关的CRB,称为Prior-CRB (P-CRB)。特别地,我们在紧密子空间上下文中分析了这个界。,当已知极点接近未知极点时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the Cramer-Rao Bound Integrating a Prior-Knowledge
Introducing prior-knowledge of some damped/undamped poles in the estimation of the parameters of a mutlipoles sinusoidal model is an important problem as for instance in bearing estimation or in biomedical signal analysis. The principle is to orthogonally project the data onto the noise space associated with the known poles. As the Cramer-Rao Lower Bound (CRB) gives a benchmark against which algorithms performance can be compared, it is useful to derive the CRB associated with this model, named Prior-CRB (P-CRB). In particular, we analyze this bound in the context of close subspaces context, ie., when the known poles are close to the unknown ones.
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