Adaptive pole estimation

AN adaptive algorithm is developed for online estimation of the poles of autoregressive (AR) processes. The method estimates the poles directly from the data without intermediate estimation of the AR coefficients or polynomial factorization. It converges rapidly, is computationally efficient, and attains the Cramer-Rao bound (CRB) asymptotically. A closed-form expression for the asymptotic CRB is provided. Convergence to the true solution is proved, and methods are discussed for extending the algorithm for use with more general (e.g. autoregressive moving-average) models. Numerical examples are presented to demonstrate the performance of the algorithm.<
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