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Non-stationary spectral estimation based on robust time varying AR model excited by a t-distribution process

机译:基于T分布过程激发的鲁棒时间变化AR模型的非静止光谱估计

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A new robust time variant spectral estimation method is proposed. We use the parametric autoregressive (AR) model to obtain the desired spectra. For robust estimation, we assumed that the residual signal is identically and independently distributed. The probability density function (PDF) of the residual signal is a t-distribution with small /spl alpha/ degrees of freedom. We put a certain base function to the parameter of the AR model, so that the obtained spectra is time variant within the considered window. Simulation results show that by using a small /spl alpha/, the obtained running spectra is closer to the ideal spectra than that by using a large /spl alpha/. The mean square error (MSE) between the estimation result and the ideal spectra derived by using a small /spl alpha/ is smaller than that by utilizing a large /spl alpha/.
机译:提出了一种新的鲁棒时间变体光谱估计方法。我们使用参数自回归(AR)模型来获得所需的光谱。对于稳健的估计,我们假设残余信号相同且独立地分布。残留信号的概率密度函数(PDF)是具有小/ SPLα/自由度的T分布。我们向AR模型的参数提供了一定的基础功能,使得所获得的光谱是所考虑的窗口中的时间变量。仿真结果表明,通过使用小/ SPLα/,所获得的运行光谱比使用大/ SPL alpha /的更接近理想光谱。估计结果和通过使用小/ SPLα/衍生的理想光谱之间的平均方误差(MSE)小于通过利用大/ SPL alpha /来衍生的理想光谱。

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