DocumentCode
1650602
Title
Parameter estimation of autoregressive processes with periodic coefficients
Author
McLernon, D.C.
Author_Institution
Dept. of Electr. & Electron. Eng., South Bank Polytech., London, UK
fYear
1989
Firstpage
1350
Abstract
Consideration is given to the identification of the parameters of a nonstationary random process {x (n )} generated by an autoregressive (AR) model with periodically changing coefficients. A set of Yule-Walker equations is derived. A time-varying linear predictor is fitted to {x (n )}, and an equivalence is established between the coefficient of the predictor and the AR model. An adaptive method is developed to identify (and track, if necessary) the periodic coefficients of the AR model
Keywords
filtering and prediction theory; parameter estimation; random processes; signal processing; time-varying systems; AR model; Yule-Walker equations; adaptive method; autoregressive processes; coefficient tracking; identification; nonstationary random process; periodic coefficients; predictor coefficients; time-varying linear predictor; Autoregressive processes; Equations; Finite impulse response filter; Frequency domain analysis; Parameter estimation; Predictive models; Random processes; System identification; White noise; Wiener filter;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location
Portland, OR
Type
conf
DOI
10.1109/ISCAS.1989.100606
Filename
100606
Link To Document