A Suboptimum Maximum Likelihood Approach to Parametric Signal Analysis

[+] Author and Article Information
S. D. Fassois

Department of Mechanical Engineering and Applied Mechanics, The University of Michigan, Ann Arbor, MI 48109-2125

K. F. Eman

Mechanical and Nuclear Engineering Department, Technological Institute, Northwestern University, Evanston, IL 60201

S. M. Wu

Department of Mechanical Engineering and Applied Mechanics, The University of Michigan Ann Arbor, MI 48109-2125

J. Dyn. Sys., Meas., Control 111(2), 153-159 (Jun 01, 1989) (7 pages) doi:10.1115/1.3153031 History: Received September 01, 1987; Revised April 28, 1988; Online July 21, 2009


A computationally efficient approach to stochastic ARMA modeling of wide-sense stationary signals is proposed. The discrete estimator minimizes a modified version of the likelihood function by using exclusively linear techniques, and thus circumventing the high computational complexity of the Maximum Likelihood (ML) method. The proposed approach is thus easy to implement, requires no explicit second order statistical information, and is shown to produce high quality estimates at a very modest computational cost. A recursive version of the algorithm, suitable for on-line implementation, is also developed, and, modeling strategy issues discussed. The effectiveness of the proposed approach is finally established through numerical simulations and comparisons with other suboptimum schemes.

Copyright © 1989 by ASME
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