EXACT MAXIMUM LIKELIHOOD ESTIMATION OF SUPERIMPOSED EXPONENTIAL SIGNALS IN NOISE.

Yoram Bresler, Albert Macovski

Research output: Contribution to journalConference articlepeer-review

Abstract

A unified framework for the exact maximum likelihood estimation of the parameters of superimposed exponential signals in noise, encompassing both the single- and multiexperiment cases (respectively, the time series and the array problems), is presented. An exact expression for the ML criterion is derived in terms of the prediction polynomial of the noiseless signal, and an iterative algorithm for the maximization of this criterion is presented. A simulation example shows the estimator to be capable of providing more accurate frequency estimates than existing techniques.

Original languageEnglish (US)
Pages (from-to)1824-1827
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Dec 1 1985
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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