.png){kind=icon}
.png){kind=icon}
Return
A constraint optimization problem for model order estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2023.109092.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we present a new method for estimating the number of terms in a sum of exponentially damped sinusoids embedded in noise. In particular, we propose to combine the shift-invariance property of the Hankel matrix associated with the signal with a constraint over its singular values to penalize small-order estimations. With this new methodology, the algebraic structure of the Hankel matrix and the statistical properties of the noise are considered. The new order estimation technique shows signif-icant improvements over subspace-based methods. In particular, when a good separation between the noise and the signal subspaces is not possible, the new methodology outperforms known techniques. We evaluate the performance of our method using numerical experiments and compare its performance with previous results found in the literature.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Spectral estimation
Subspace-based methods
Optimal threshold
Model order selection