'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Deconvolution is an important preprocessing
procedure often needed in the spectral analysis of
transient exponentially decaying signals. Three
deconvolution techniques are studied and applied to the
problem of estimating the parameters of
multiexponential signals observed in noise. Both the
conventional and optimal compensated inverse filtering
approaches produce data which are further analyzed by
SVD-based autoregressive moving average (ARMA)
modeling techniques. The third procedure is based on
homomorphic filtering and it is implemented by fast
Fourier transform (FFT) technique. A comparative
study of the performance of the above deconvolution
techniques in analyzing multicomponent exponential
signals with varied signal-to-noise ratio (SNR) is
examined in this paper. The results of simulation studies
show that the homomorphic deconvolution technique is
most computationally efficient, however, it produces
inaccurate estimates of signal parameters even at high
SNR, especially with closely related exponents.
Simulation results show that the optimal compensation
deconvolution technique is indeed a generalized form of
the conventional inverse filtering and has the potential
of producing accurate estimates of signal parameters
from a substantial wide range of SNR data
