Title :
High resolution spectral estimation through localized polynomial approximation
Author :
Liang, Zhi-Pei ; Haacke, E. Mark ; Thomas, Cecil W.
Author_Institution :
Case Western Reserve Univ., Cleveland, OH, USA
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 give, 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
Keywords :
computerised tomography; eigenvalues and eigenfunctions; least squares approximations; matrix algebra; parameter estimation; polynomials; spectral analysis; Gibbs free recovery; high-resolution capability; least-squares fitting; linear prediction theory; localized polynomial approximation; matrix eigendecomposition; model parameters; numerical simulation; piecewise continuous spectra; spectral estimation; spectral estimation problems; tomographic imaging reconstruction problems; Data analysis; Direction of arrival estimation; High-resolution imaging; Image resolution; Least squares approximation; Least squares methods; Magnetic resonance imaging; Polynomials; Radar; Spectroscopy;
Conference_Titel :
Spectrum Estimation and Modeling, 1988., Fourth Annual ASSP Workshop on
Conference_Location :
Minneapolis, MN
DOI :
10.1109/SPECT.1988.206230