Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals

dc.contributor.author	Jibia, Abdussamad U.
dc.contributor.author	Salami, Momoh-Jimoh E.
dc.date.accessioned	2019-08-14T14:59:31Z
dc.date.available	2019-08-14T14:59:31Z
dc.date.issued	2007-12
dc.description.abstract	Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks.	en_US
dc.identifier.citation	Jibia, A. U., & Salami, M. J. E. (2007). Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals. International Journal of Computer Science, 2(4), 235-239.	en_US
dc.identifier.uri	http://repository.elizadeuniversity.edu.ng/handle/20.500.12398/483
dc.language.iso	en	en_US
dc.publisher	International Journal of Computer Science	en_US
dc.subject	Eigenvector	en_US
dc.subject	Minimum norm	en_US
dc.subject	Multiexponential	en_US
dc.subject	Subspace	en_US
dc.title	Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals	en_US
dc.type	Article	en_US

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