Performance evaluation of music and minimum norm Eigenvector Algorithms in resolving noisy multiexponential signals
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
World Academy of Science, Engineering and Technology (W A S E T)
2007
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/6955/ http://irep.iium.edu.my/6955/1/Performance_Evaluation_of_Music_and_Minimum.pdf |
| Summary: | 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 |
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