Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters

Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters

Abstract Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics of the signal under consideration. In this work, time-frequency analysis is achieve...

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Bibliographic Details
Main Authors: Kyriaki Kostoglou, Michael Lunglmayr
Format: Article
Language:English
Published: SpringerOpen 2020-02-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
ARMA models
Time-varying
Cascade structure
Sigma-point Kalman filter
Root tracking
Genetic algorithm
Online Access:http://link.springer.com/article/10.1186/s13634-020-00666-7

Internet

http://link.springer.com/article/10.1186/s13634-020-00666-7

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