Abstract:
Matched-field methods concern estimation of source location and/or ocean environmental
parameters by exploiting full wave modeling of acoustic waveguide propagation.
Typical estimation performance demonstrates two fundamental limitations.
First, sidelobe ambiguities dominate the estimation at low signal-to-noise ratio (SNR),
leading to a threshold performance behavior. Second, most matched-field algorithms
show a strong sensitivity to environmental/system mismatch, introducing some biased
estimates at high SNR.
In this thesis, a quantitative approach for ambiguity analysis is developed so that
different mainlobe and sidelobe error contributions can be compared at different SNR
levels. Two large-error performance bounds, the Weiss-Weinstein bound (WWB)
and Ziv-Zakai bound (ZZB), are derived for the attainable accuracy of matched-field
methods. To include mismatch effects, a modified version of the ZZB is proposed.
Performance analyses are implemented for source localization under a typical shallow
water environment chosen from the Shallow Water Evaluation Cell Experiments
(SWellEX). The performance predictions describe the simulations of the maximum
likelihood estimator (MLE) well, including the mean square error in all SNR regions
as well as the bias at high SNR. The threshold SNR and bias predictions are also
verified by the SWellEX experimental data processing. These developments provide
tools to better understand some fundamental behaviors in matched-field performance
and provide benchmarks to which various ad hoc algorithms can be compared.
