Abstract:
All real-time signals observed from various
measurement systems require signal-processing techniques like
the deconvolution, to compensate for the effect of transfer
function of the systems. Wiener deconvolution is a widely
used signal-processing technique for signal restoration. Often,
if the power spectral density of signal and noise is unknown,
the accuracy of the restored signal depends on an unknown
filter parameter. In the literature, time-consuming, iterative,
computational methods were reported to estimate the parameter.
However, many of these methods give a range of optimum
values instead of an unique value and often lead to either oversmoothing
or under-smoothing. In this paper, novel analytical
expressions are presented through which the unknown parameter
can be estimated explicitly. The analytical results of this study
are compared with the numerical methods, and they are found
to be accurate and robust against numerical evaluation. Further,
the results are demonstrated for signals obtained experimentally
from the pulsed electroacoustic system and a network, in authors’
laboratory.
