ARMA PARAMETER-ESTIMATION USING A NOVEL RECURSIVE ESTIMATION ALGORITHM WITH SELECTIVE UPDATING

This paper investigates an extension of a recursive estimation algorithm (the so-called OBE algorithm) [9]-[ll], which features a discerning update strategy. In particular, an extension of the algorithm to ARMA parameter estimation is presented here along with convergence analysis. The extension is similar to the extended least-squares algorithm. However, the convergence analysis is complicated due to the discerning update strategy which incorporates an information-dependent updating factor. The virtues of such an update strategy are: 1) more efficient use of the input data in terms of information processing, and 2) a modular adaptive filter structure which would facilitate the development of a parallel-pipelined signal processing architecture. It is shown in this paper that if the input noise is bounded and the moving average parameters satisfy a certain magnitude bound, then the a posteriori prediction errors are uniformly bounded. With an additional persistence of excitation condition, the parameter estimates are shown to converge to a neighborhood of the true parameters, and the a priori prediction errors are shown to be asymptotically bounded. Simulation results show that the parameter estimation error for the EOBE algorithm is comparable to that for the ELS algorithm. © 1990 IEEE