Maximum likelihood estimation of structural wave components from noisy data.
Maximum likelihood estimation of structural wave components from noisy data.
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根据噪声数据进行结构波分量的最大似然估计。
DOI：
10.1121/1.1456518
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
K. Grosh
中科院分区：
文献类型：
--
作者：
P. J. Halliday;K. Grosh
本文通过对具有分离变量的函数应用极大似然估计理论，建立了确定一般信号模型的基本参数的一般框架。该方法扩展了以前在正弦和指数估计方面的工作，以包括具有其他函数基的模型，例如具有非恒定振幅的指数函数和贝塞尔函数。该技术还可以实现非均匀空间采样。将极大似然法应用于沿一维结构单元的波分量识别。结果表明，利用噪声仿真数据估计指数和贝塞尔函数模型参数的方法是可行和准确的。
In this paper, a general framework is developed for determining the underlying parameters of general signal models through the application of maximum likelihood estimation theory for functions whose variables separate. This method extends previous work in sinusoidal and exponential estimation to include models with other functional bases, such as exponential functions with nonconstant amplitudes and Bessel functions. Nonuniform spatial sampling is also possible with this technique. The maximum likelihood method is applied to the identification of wave components along one-dimensional structural elements. Results are given which demonstrate the viability and accuracy of the technique estimating exponential and Bessel function model parameters from noisy simulation data.