Title :
AR, ARMA, and AR-in-noise modeling by fitting windowed correlation data
Author :
Jackson, Leland B. ; Huang, Jianguo ; Richards, Kevin P. ; Chen, Haiguang
Author_Institution :
Dept. of Electr. Eng., Rhode Island Univ., Kingston, RI, USA
fDate :
10/1/1989 12:00:00 AM
Abstract :
A method for autoregressive (AR) modeling of stationary stochastic signals is extended to AR moving-average (ARMA) models, including the special case of AR signals in white noise. Both AR and ARMA examples are presented. The method differs from the well-known method of overdetermined normal equations in that fitting error, not equation error, is minimized, and significantly improved performance is obtained. Iterative algorithms patterned after the Steiglitz-McBride (1965) deterministic method are derived to solve the resulting nonlinear equations
Keywords :
correlation theory; iterative methods; signal processing; white noise; AR modelling; AR moving-average; AR signals; AR-in-noise modeling; ARMA; deterministic method; iterative algorithms; nonlinear equations; stationary stochastic signals; white noise; windowed correlation data; Acoustic signal processing; Autocorrelation; Equations; Filtering; Filters; Iterative algorithms; Predictive models; Stochastic processes; White noise; Writing;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
