High resolution spectral estimation through localized polynomial approximation

Zhi-Pei Liang, E. Mark Haacke, Cecil W. Thomas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autoregressive-moving-average models are not adequate for most tomographic imaging reconstruction problems. Consequently, the high-resolution capability being sought is lost when these models are used. In this work, a model based on localized polynomial approximation of the spectrum is proposed to solve this class of spectral estimation problems. A method for finding the model parameters is given, which uses linear prediction theory, matrix eigendecomposition and least-squares fitting. Numerical simulation results are presented to demonstrate its high-resolution capability. It is concluded that the proposed model has a clear advantage over existing models for Gibbs free recovery of piecewise continuous spectra when only limited data are available.

Original languageEnglish (US)
Title of host publicationFourth Annu ASSP Workshop Spectrum Estim Model
PublisherPubl by IEEE
Pages402-407
Number of pages6
StatePublished - 1988
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)

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