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|Title:||Performance evaluation of music and minimum norm eigenvector algorithms in resolving noisy multiexponential signals|
|Authors:||Jibia, Abdussamad U.|
Salami, Momoh-Jimoh E.
|Publisher:||International Journal of Computer Science|
|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.|
|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.|
|Appears in Collections:||Research Articles|
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|An appraisal of Gardner transform-based methods of transient multiexponential signal analysis.pdf||Article full-text||808.51 kB||Adobe PDF|
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