DSP-based AR spectral estimation with zoom effect for NMR spectroscopy

Author

Caspary, O. ; Tomczak, M. ; Nus, P. ; Staiquly, P.

Author_Institution

Centre de Recherche en Autom. de Nancy, Nancy Univ., Vandoeuvre, France

fYear

1993

fDate

18-20 May 1993

Firstpage

128

Lastpage

131

Abstract

Autoregressive (AR) spectral estimation is a high-resolution method based on the modeling of time series by an AR model. AR parameters are estimated by a fast recursive least squares (FRLS) algorithm with a transversal structure, the fast Kalman (FK) algorithm. To implement it on a DSP96002 processor the algorithm is stabilized by using the method of Benallal that avoids the accumulation of round-off errors. For particular applications, only a part of the spectrum is of interest. For that case, a zoom function is described that can be used to yield local spectra with better resolution. For this purpose, the complex form of the stabilized FK (SFK) is used. An application of this AR spectral analysis to nuclear magnetic resonance (NMR) spectroscopy is shown

Keywords

Kalman filters; NMR spectroscopy; biomedical NMR; digital signal processing chips; least squares approximations; recursive estimation; spectral analysis; spectroscopy computing; time series; Benallal; DSP96002 processor; NMR spectroscopy; autoregressive spectral estimation; fast recursive least squares algorithm; high-resolution method; local spectra; modeling; nuclear magnetic resonance; round-off errors; time series; zoom effect; zoom function; Costs; Frequency estimation; Kalman filters; Nuclear magnetic resonance; Parameter estimation; Partitioning algorithms; Roundoff errors; Signal processing algorithms; Signal resolution; Spectroscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 1993. IMTC/93. Conference Record., IEEE

Conference_Location

Irvine, CA

Print_ISBN

0-7803-1229-5

Type

conf

DOI

10.1109/IMTC.1993.382666

Filename

382666