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Accuracy analysis of the Frisch estimates for identifying errors-in-variables systems

机译:Frisch估计值的准确性分析,以识别变量误差系统

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Several estimation methods have been proposed for identifying errors-in-variables systems, where both input and output measurements are corrupted by noise. One of the promising approaches is the so called Frisch scheme. This paper provides an accuracy analysis of the Frisch scheme applied to system identification. The estimates of the system parameters and the noise variances are shown to be asymptotically Gaussian distributed. An explicit expression for the covariance matrix of the asymptotic distribution is given as well. Numerical simulations support the theoretical results. A comparison with the Cramer-Rao lower bound is also given in examples, and it is shown that the Frisch scheme gives a performance close to the Cramer-Rao bound for large signal-to-noise ratios.
机译:已经提出了几种估计方法来识别变量误差系统,在该系统中输入和输出测量值都被噪声破坏。一种有前途的方法是所谓的弗里施方案。本文提供了用于系统识别的Frisch方案的准确性分析。系统参数和噪声方差的估计值显示为渐近高斯分布。还给出了渐近分布的协方差矩阵的显式表达式。数值模拟支持理论结果。在示例中还给出了与Cramer-Rao下限的比较,并且表明对于大的信噪比,Frisch方案的性能接近于Cramer-Rao界。

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