Source detection and localization in the ocean with a Bayesian approach

Source localization and detection of sound sources in the ocean are investigated from a Bayesian Matched Field Processing perspective. Unknown parameters comprise the source spectrum along with source location; the spectrum is estimated within the localization and detection process. We develop incoherent and coherent processors, integrating the source spectrum estimation employing a Gibbs sampler. Using synthetic data we find that the coherent processor is superior to the incoherent processor and the standard Matched Field approach both in terms of source location estimates and uncertainty. The coherent and incoherent techniques are also applied to real data from the Hudson Canyon experiment with the same conclusion. Employing Receiver Operating Characteristic (ROC) curves, the coherent and incoherent processors are evaluated and compared in the task of joint detection and localization as well. The coherent detector/localization processor is superior to the incoherent one. Joint detection and localization performance is evaluated with Localization-ROC curves. [Work supported by ONR.]