Joel Harley Defense

Joel Harley Defense

Starts at: April 29, 2014 12:00 PM

Ends at: 3:00 PM

Location: PH B34


This dissertation develops a robust, data-driven localization methodology based
on the integration of matched field processing with compressed sensing ℓ1 recovery
techniques and scale transform signal processing. The localization methodology
is applied to an ultrasonic guided wave structural health monitoring system for detecting,
locating, and imaging damage in civil infrastructures. In these systems,
the channels are characterized by complex, multi-modal, and frequency dispersive
wave propagation, which severely distort propagating signals. Acquiring the characteristics
of these propagation mediums from data represents a difficult inverse problem
for which, in general, no readily available solution exists. In this dissertation,
we build data-drivenmodels of these complexmediums by integrating experimental
guided wave measurements with theoretical wave propagation models and ℓ1 sparse
recovery methods from compressed sensing. The data-driven models are combined
with matched field processing, a localization framework extensively studied for underwater
acoustics, to localize targets in complex, guided wave environments. The
data-driven matched field processing methodology is then refined, through the use
of the scale transform, to achieve robustness to environmental variations that distort
guided waves. Data-driven matched field processing is experimentally applied to
an ultrasound structural health monitoring system to detect and locate damage in
aluminum plate structures.