期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 54, 期 3, 页码 1041-1053出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2005.862845
关键词
autoregressive (AR) models; autoregressive moving average (ARMA) models; convergence properties; gradient search; least squares filtering; moving average (MA) models; martingale convergence theorem; parameter estimation; recursive identification
The correlation analysis based methods are not suitable for identifying parameters of nonstationary autoregressive (AR), moving average (MA), and ARMA systems. By using estimation residuals in place of immeasurable noise terms in information vector or matrix, we develop a least squares based and gradient based algorithms and establish the consistency of the proposed algorithms without assuming noise stationarity, ergodicity, or existence of higher order moments. Furthermore, we derive the conditions for convergence of the parameter estimation. The simulation results validate the convergence theorems proposed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据