A robust spectral estimation by modeling an estimated autocovariance with an ARMA model

1989 ◽  
Vol 37 (2) ◽  
pp. 181-191 ◽  
Author(s):  
Sungkwon Park ◽  
L.A. Gerhardt
Keyword(s):  
Spectral Estimation ◽  
Arma Model

scholarly journals Sea Spectral Estimation Using ARMA Models

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4280
Author(s):  
Marta Berardengo ◽  
Giovanni Battista Rossi ◽  
Francesco Crenna
Keyword(s):  
Spectral Estimation ◽  
Arma Model ◽  
Arma Models ◽  
Reconstruction Procedure ◽  
Spectral Estimates ◽  
Wave Elevation ◽  
Accuracy Of Results ◽  
Auto Covariance ◽  
Signal Time ◽  
Sea Wave

This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented. Afterwards, an analysis on how the parameters involved in the ARMA reconstruction procedure—for example, the signal time length, the number of poles and data used—affect the spectral estimates is carried out, providing evidence on their effect on the accuracy of results. This allowed us to provide guidelines on how to set these parameters in order to make the ARMA model as accurate as possible. The paper focuses on mono-modal sea states. Nevertheless, examples also related to bi-modal sea states are discussed.

scholarly journals ARMA model maximum entropy power spectral estimation

1984 ◽  
Vol 32 (5) ◽  
pp. 984-990 ◽  
Author(s):  
M. Lagunas-Hernandez ◽  
M. Santamaria-Perez ◽  
A. Figueiras-Vidal
Keyword(s):  
Maximum Entropy ◽  
Spectral Estimation ◽  
Arma Model ◽  
Power Spectral

Causal Probabilistic Network and Power Spectral Estimation Used in Sleep Stage Classification

1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Spectral Estimation ◽  
Interrater Agreement ◽  
Probabilistic Networks ◽  
Probabilistic Network ◽  
Spectral Bands ◽  
Power Spectral ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
The Brain

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

The Theory of Spectral Estimation of Signals and Generalized Kravchenko-Kotel'nikov-Levitan Theorems

PIERS Online ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 373-376
Author(s):  
Victor Filippovich Kravchenko ◽  
Dmitry V. Churikov
Keyword(s):  
Spectral Estimation
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