论文标题
基于内核的新方法用于光谱估计
A new kernel-based approach for spectral estimation
论文作者
论文摘要
本文解决了该问题，以估计ARMA零平均高斯过程的功率谱密度。我们提出了一个基于内核的最大熵光谱估计器。后者在一类高阶自回归模型上搜索了最佳光谱，而内核矩阵引起的惩罚项将规则性和指数衰减促进了相应的一步前预测器的脉冲响应的零。此外，提出的方法还提供了过程的最小相光谱因子。数值实验显示了该方法的有效性。
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian process. We propose a kernel based maximum entropy spectral estimator. The latter searches the optimal spectrum over a class of high order autoregressive models while the penalty term induced by the kernel matrix promotes regularity and exponential decay to zero of the impulse response of the corresponding one-step ahead predictor. Moreover, the proposed method also provides a minimum phase spectral factor of the process. Numerical experiments showed the effectiveness of the proposed method.
