Using ambient noise and sources of opportunity to estimate environment parameters and improve matched field source detection and localization

This dissertation describes matched field source localization methods in a shallow water ocean waveguide which overcome lack of knowledge of the waveguide properties, whose measurement would otherwise be essential for conventional matched field methods to succeed. Such measurements are typically obtained only at great cost by dedicated measurement platforms, separate and distinct from the sensors used to localize sources. We demonstrate MFP using modes derived from data, the sound speed profile, but no a priori bottom information. We show how mode shapes can be estimated directly from vertical line array data, without a priori knowledge of the environment and without using numerical wavefield models. However, it is difficult to make much headway with modes derived from data, without wave numbers, since only a few modes at a few frequencies may be captured, and only at depths sampled by the array. Using a measured sound speed profile, we derive self-consistent, complete sets of modes, wave numbers and bottom parameters from incomplete modes derived from data. Bottom parameters enable us to calculate modes at all frequencies, not just those at which we derived modes from data. This process is applied to SWellEx-96 experiment data. Modes, wave numbers and bottom parameters are derived from source tow data along one track and MFP based on this information is performed on source tow data along another track.