Title :
Parameter estimation of autoregressive processes with periodic coefficients
Author_Institution :
Dept. of Electr. & Electron. Eng., South Bank Polytech., London, UK
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;
Conference_Titel :
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location :
Portland, OR
DOI :
10.1109/ISCAS.1989.100606
