Analysis of multiexponential transient signals using interpolation-based deconvolution and parametric modeling techniques
Previous work has shown that the deconvolution technique is one of the most effective procedures for analyzing transient exponentially decaying signals. Direct deconvolution approach often leads to poor resolution of ihe estimated decay rates since the fast Fourier transform (FFT) algorithm is...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/6945/ http://irep.iium.edu.my/6945/1/Analysis_of_Multiexponential_Transient_signals_Using_Interpolation-Based.pdf |
| Summary: | Previous work has shown that the deconvolution
technique is one of the most effective procedures for
analyzing transient exponentially decaying signals.
Direct deconvolution approach often leads to poor
resolution of ihe estimated decay rates since the fast
Fourier transform (FFT) algorithm is used to analyze
the resulting deconvolved data. One of the most
promising approaches is based on optimal inverse
Xltering followed by fitting an autoregressive moving
average ( A M ) model to the deconvolved data so that
its AR parameters are determined by solving high order
Yule- Walker equations (HOYWE) via the singular value
decomposition (SVD) algorithm. Many desirable results
have been obtained by using this technique for both
clean and noisy signals. However, the real-time
implementation of this algorithm poses some diflculties
since nonlinear transformation is involved in such
analysis. One method of overcoming this d1ficulty is by
incorporating the spline interpolation algorithm into the
nonlinear preprocessing procedure. The performance of
the proposed algorithm in accurately estimating the
number of exponential signals and their corresponding
exponential consfanis for both simulated and real data is
investigated in this paper. Results of analysis have
shown that high-resolution estimates of decay constants
are obtained when the proposed algorithm is used to
analyze multiexponential signals with varied signal-tonoise
(SNR) ratio. |
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