Canonical correlation analysis (CCA) for ARMA spectral estimation

Sayfe Kiaei, L. Luo

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

1 Scopus citations

Abstract

The canonical correlation analysis (CCA) of rational system identification is investigated for autoregressive moving-average (ARMA) spectral estimation at low SNR. The method is used to compute the parameters of the state-space Markovian model and its spectrum using CCA. It is shown that this approach yields significantly better results and improved resolution for low SNR. An interesting feature of the CCA is that the system parameters are sign symmetric, which reduces the computation cost by half. The performance of this method for spectral estimation of multiple sinusoids in noise is compared with singular value decomposition and the canonical vector method.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Editors Anon
PublisherPubl by IEEE
Pages1319-1322
Number of pages4
Volume2
StatePublished - 1989
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1 - Portland, OR, USA
Duration: May 8 1989May 11 1989

Other

OtherIEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1
CityPortland, OR, USA
Period5/8/895/11/89

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Fingerprint Dive into the research topics of 'Canonical correlation analysis (CCA) for ARMA spectral estimation'. Together they form a unique fingerprint.

Cite this