Optimal Detectors for Transient Signal Families
and Nonlinear Sensors
Derivations and Applications.
PhD Thesis, Uppsala University,
The Comprehensive Summary is available
In Pdf (685K)
Paper copies of the thesis can be obtained from
Signals and Systems Group, Uppsala University,
Box 534, SE-75121 Uppsala, Sweden.
This thesis is concerned with detection of
transient signal families and
detectors in nonlinear static sensor systems.
The detection problems are
treated within the framework of likelihood
ratio based binary hypothesis testing.
An analytical solution to the noncoherent
detection problem is derived, which
in contrast to the classical noncoherent detector,
is optimal for wideband signals.
An optimal detector for multiple transient signals
with unknown arrival times is also
derived and shown to yield higher detection
performance compared to the classical
approach based on the generalized likelihood ratio test.
An application that is treated in some detail is
that of ultrasonic nondestructive
testing, particularly pulse-echo detection of
defects in elastic solids. The defect
detection problem is cast as a composite hypothesis
test and a methodology, based
on physical models, for designing statistically
optimal detectors for cracks in elastic
solids is presented. Detectors for defects with low
computational complexity are also
formulated based on a simple phenomenological
model of the defect echoes. The
performance of these detectors are compared with
the physical model-based optimal
detector and is shown to yield moderate performance degradation.
Various aspects of optimal detection in static
nonlinear sensor systems are also
treated, in particular the stochastic resonance (SR)
phenomenon which, in this
context, implies noise enhanced detectability.
Traditionally, SR has been quantified
by means of the signal-to-noise ratio (SNR)
and interpreted as an increase of a
system's information processing capability.
Instead of the SNR, rigorous information
theoretic distance measures, which truly can
support the claim of noise enhanced
information processing capability, are
proposed as quantifiers for SR. Optimal
are formulated for two static nonlinear sensor
systems and shown to exhibit noise
optimal detection, transient signals, noncoherent detection,
unknown arrival time,
ultrasonic nondestructive testing, nonlinear sensor, stochastic resonance.