Abstract:
The thesis develops and demonstrates methods of classifying ocean processes using
an underwater moving platform such as an Autonomous Underwater Vehicle (AUV).
The "mingled spectrum principle" is established which concisely relates observations
from a moving platform to the frequency-wavenumber spectrum of the ocean process.
It clearly reveals the role of the AUV speed in mingling temporal and spatial
information. For classifying different processes, an AUV is not only able to jointly
utilize the time-space information, but also at a tunable proportion by adjusting
its cruise speed. In this respect, AUVs are advantageous compared with traditional
oceanographic platforms.
Based on the mingled spectrum principle, a parametric tool for designing an AUVbased
spectral classifier is developed. An AUV's controllable speed tunes the separability
between the mingled spectra of different processes. This property is the key to
optimizing the classifier's performance.
As a case study, AUV-based classification is applied to distinguish ocean convection
from internal waves. The mingled spectrum templates are derived from the MIT
Ocean Convection Model and the Garrett-Munk internal wave spectrum model. To
allow for mismatch between modeled templates and real measurements, the AUVbased
classifier is designed to be robust to parameter uncertainties. By simulation
tests on the classifier, it is demonstrated that at a higher AUV speed, convection's
distinct spatial feature is highlighted to the advantage of classification.
Experimental data are used to test the AUV-based classifier. An AUV-borne flow
measurement system is designed and built, using an Acoustic Doppler Velocimeter
(ADV). The system is calibrated in a high-precision tow tank. In February 1998, the
AUV acquired field data of flow velocity in the Labrador Sea Convection Experiment.
The Earth-referenced vertical flow velocity is extracted from the raw measurements.
The classification test result detects convection's occurrence, a finding supported by
more traditional oceanographic analyses and observations. The thesis work provides
an important foundation for future work in autonomous detection and sampling of
oceanographic processes.
