Title :
Tracking analysis of an ARMA parameter estimation algorithm
Author :
Rao, Bhaskar ; Peng, Rong
Author_Institution :
Dept. of AMES, California Univ., San Diego, La Jolla, CA, USA
fDate :
2/1/1990 12:00:00 AM
Abstract :
The problem of adaptively estimating parameters of a time-varying autoregressive moving-average (ARMA) process using a constant-step-size Gauss-Newton algorithm is studied. Using weak convergence theory and the concept of prescaling, it is shown that an ordinary differential equation can be used to describe the mean behavior of the adaptive filter coefficients. Computer simulations are provided to substantiate the analysis
Keywords :
adaptive filters; convergence; differential equations; parameter estimation; time series; ARMA parameter estimation; adaptive filter coefficients; constant-step-size Gauss-Newton algorithm; ordinary differential equation; prescaling; time series; time-varying autoregressive moving-average process; weak convergence theory; Adaptive algorithm; Algorithm design and analysis; Autoregressive processes; Convergence; Differential equations; Least squares methods; Newton method; Parameter estimation; Recursive estimation; Time varying systems;
Journal_Title :
Automatic Control, IEEE Transactions on
