Parameter estimation of superimposed signals by dynamic programming

Sze Fong Yau, Yoram Bresler

Research output: Contribution to journalConference article

Abstract

The problem of fitting a model composed of a number of superimposed signals to noisy data using the maximum-likelihood (ML) criterion is considered. A local interaction model is established through the study of Cramer-Rao bound. For such models, the global extremum of the criterion is found efficiently by dynamic programming. An approximate version of the algorithm is developed to further reduce the computation. Using the minimum description length principle, it is shown that the dynamic programming method can be easily adapted to determine the number of signals as well.

Original languageEnglish (US)
Pages (from-to)2499-2502
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
StatePublished - Dec 1 1990
Event1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA
Duration: Apr 3 1990Apr 6 1990

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dynamic programming
Dynamic programming
Parameter estimation
Cramer-Rao bounds
range (extremes)
Maximum likelihood
interactions

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Parameter estimation of superimposed signals by dynamic programming. / Yau, Sze Fong; Bresler, Yoram.

In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 5, 01.12.1990, p. 2499-2502.

Research output: Contribution to journalConference article

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