ARMA parameter estimation using a novel recursive estimation algorithm with selective updating

Ahstract-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 leastsquares 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: I ) 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 a re 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 a re shown to he asymptotically bounded. Simulation results show that the parameter estimation error for the EOBE algorithm is comparable to that for the ELS algorithm.