Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals
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Date
2007-12
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Computer Science
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.
Description
Keywords
Eigenvector, Minimum norm, Multiexponential, Subspace
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.