A practical well established signal processing technique for
suppression of ultrasonic clutter known as split spectrum
processing (SSP) is studied in an attempt to obtain better
theoretical understanding and optimal parameter tuning.
The SSP technique relies on
nonlinear processing of the outputs from a filter bank which
splits the received signal into different frequency bands.
A general approach to parameter tuning of SSP is considered
where an adaptive artificial neural network (ANN) replaces
the nonlinear part of the SSP.
Extensions to an
adaptive filter bank is also considered in the context of
both a multilayer perceptron ANN
operating on a delay line and the Wiener model of
nonlinear dynamical systems. Both the
ANN and the Wiener model are shown to have the
same structure as the SSP and the
potential of supervised learning of the ANN and
system identification of the Wiener model
Originating from the ANN approach, many theoretical
aspects of how SSP works
are presented. New insights about how SSP exploits
phase and amplitude information is
elaborated and conceptual links between the SSP filter
bank and the short-time Fourier
transform and other time-frequency methods such as
wavelets are touched upon.
Employing a statistical pattern recognition perspective,
the optimal detector for a known
transient in additive Gaussian noise (the matched filter)
is formulated as a time-frequency
method and used for nonlinear clutter suppression.
The new formulation is used to
compare SSP with conventional detection theory and
to obtain a unifying link with recent
work on maximum likelihood amplitude estimation
in the context of ultrasonics. It is also
employed to show that the polarity thresholding SSP
algorithm relies on a test statistic
which is a nonlinear function of the observed samples.
A simple clutter model suitable for digital signal
processing is developed based on
physical principles. It is used mainly to motivate the
theoretical studies of the optimal
detectors in additive Gaussian noise and for
evaluation of different algorithms.
As a final contribution, the underlying principle of
SSP to produce and compound
uncorrelated filter signals is reconsidered,
resulting in a statistically based formula for the
number of optimal filters to use and insights about
the role of the filter bank in stationary
as well as nonstationary noise. Other results include
a new SSP algorithm based on a
noncoherent detector for additive
Gaussian noise which demonstrates that the original
SSP filter bank can produce optimal statistics
for in-phase noncoherent subband detection.