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Measurement time dependency of asymptotic Cramer-Rao bound for an unknown constant in stationary Gaussian noise

机译:平稳高斯噪声中未知常数的渐近Cramer-Rao界的测量时间依赖性

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In metrology, the knowledge of uncertainty principles helps to improve the performance of measurement systems and to understand measurement limits. Especially an uncertainty principle that explains the relation between the measurement uncertainty and the measurement time of a measurand would be beneficial, because repeating and averaging measurements is a common technique for reducing random errors. Although many research covered the analysis of the Cramer-Rao bound (CRB) as minimum achievable measurement uncertainty, its relation to the measurement time of the measurand was only studied for specific measurands yet. For this reason, the dependency of the CRB on the measurement time is studied for an unknown constant in stationary Gaussian noise, while the unknown constant can be any physical quantity as measurand. Here, the correlations of the samples of the Gaussian process are assumed to be independent of the measurand. As a result, the CRB can be asymptotically linked with the measurement time by the noise power spectral density at the frequency zero and the sensitivity. A comparison between the proposed asymptotic and the exact CRB solution shows a good agreement for common types of colored Gaussian noise. Hence, the analytic expression of the asymptotic solution is proven to be applicable for estimating the measurement uncertainty limits of an unknown constant in stationary Gaussian noise for a desired measurement time and vice versa. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在计量学中,不确定性原理的知识有助于改善测量系统的性能并了解测量极限。尤其是,解释重复测量和平均化测量是减少随机误差的常用技术,解释测量不确定度和被测时间之间关系的不确定性原理将是有益的。尽管许多研究都将Cramer-Rao界线(CRB)的分析视为可实现的最小测量不确定度,但仅针对特定被测量体研究了其与被测量体测量时间的关系。因此,对于静态高斯噪声中的一个未知常数,研究了CRB对测量时间的依赖性,而该未知常数可以是任何物理量,例如被测者。这里,假定高斯过程的样本的相关性独立于被测量者。结果,CRB可以通过零频率处的噪声功率谱密度和灵敏度渐近地与测量时间联系起来。拟议的渐近与精确CRB解决方案之间的比较表明,对于有色高斯噪声的常见类型,它具有良好的一致性。因此,渐近解的解析表达式被证明可用于估计平稳高斯噪声中未知常数在所需测量时间内的测量不确定性极限,反之亦然。 (C)2015 Elsevier Ltd.保留所有权利。

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