Bayesian localization, tracking, and environmental inference

Abstract

This paper presents an overview of a general Bayesian inference approach to source localization, tracking and/or environmental estimation. Source location and spectral parameters together with environmental parameters are all considered unknown random variables to be estimated from prior information and observed acoustic data. The relative level of prior information for various parameters differentiates applications of interest. For instance, controlled-source geoacoustic inversion typically involves large prior uncertainties for seabed parameters but small uncertainties for source locations, although some applications, such as inverting noise from ships-of-opportunity, may involve larger location uncertainties. Alternatively, source localization in an uncertain environment typically involves non-informative location priors and environmental priors that reflect available knowledge. Tracking applications include additional prior constraints on source speed. In all cases, the goal is to compute marginals of the posterior probability density for source and environmental parameters, quantifying the information content of the data and prior. This is typically carried out with Markov-chain Monte Carlo methods including Metropolis-Hastings sampling and/or Gibbs sampling, with various approaches applied to improve efficiency (e.g., principal-component sampling, parallel tempering) and generality (trans-dimensional inversion). Multiple-source localization minimizes the Bayesian information criterion to estimate the number of sources.