Performance analysis of estimation algorithms of nonstationary ARMA processes

Authors:
Feng Ding;Yang Shi;Tongwen Chen
Affiliations:
Control Sci. & Eng. Res. Center, Southern Yangtze Univ., Jiangsu, China;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2006

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Abstract

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 unmeasurable 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.