Probabilistic focalization for shallow water localization

Abstract

Localizing and tracking an underwater acoustic source is a key task for maritime situational awareness. This paper presents a sequential Bayesian estimation method for passive acoustic source localization in shallow water. The proposed probabilistic focalization approach associates detected directions of arrival (DOAs) to modeled DOAs and jointly estimates the time-varying source location. Embedded ray tracing makes it possible to incorporate environmental parameters that characterize the acoustic waveguide. Due to its statistical model, the proposed method can provide robustness in scenarios with severe environmental uncertainty. We demonstrate performance advantages compared to matched field processing using data collected during the SWellEx-96 experiment.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 01, 2021
Source ID
10.1121/10.0005814

Entities

People

  • Florian Meyer
  • Kay L Gemba

Organizations

  • Office of Naval Research
  • United States Naval Research Laboratory
  • University of California, San Diego

Tags

Readers

  • Acoustical Oceanography.
  • Distributed Systems and Data Platform Development
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference