Suboptimum maximum likelihood approach to parametric signal analysis.

S. D. Fassois*, K. F. Eman, S. M. Wu

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

A computationally efficient approach to stochastic ARMA (autoregressive moving-average) 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 circumventing the high computational complexity of the maximum-likelihood (ML) method. The proposed approach is thus easy to implement, requires no 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 online implementation, is also developed, and, certain modeling strategy issues discussed. The effectiveness of the proposed approach is established by numerical simulations and comparisons with other suboptimum schemes.

Original languageEnglish (US)
Pages (from-to)406-413
Number of pages8
JournalProceedings of the American Control Conference
Volume88 pt 1-3
StatePublished - 1988

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Suboptimum maximum likelihood approach to parametric signal analysis.'. Together they form a unique fingerprint.

Cite this