A new kernel-based approach for spectral estimation
Optimization and Control
2020-04-30 v1
Abstract
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.
Cite
@article{arxiv.2004.14184,
title = {A new kernel-based approach for spectral estimation},
author = {Mattia Zorzi},
journal= {arXiv preprint arXiv:2004.14184},
year = {2020}
}