Information-theoretic analysis of iterated Bayesian acoustic source localization in a static ocean waveguide.

Area of Science:

• Acoustic signal processing
• Information theory
• Statistical inference

Background:

• Accurate source localization in acoustic waveguides is crucial for underwater applications.
• Bayesian inference provides a probabilistic framework for sequential estimation problems.
• Information theory offers tools to quantify uncertainty and information gain.

Purpose of the Study:

• To apply information theory to quantify the performance of iterated Bayesian localization.
• To analyze the impact of acoustic channel and noise model mismatch on localization accuracy.
• To develop methods for quantifying source location uncertainty in acoustic waveguides.

Main Methods:

• Utilizing segmented Fourier transforms of received pressure time series.
• Modeling the acoustic propagation as a nonlinear communication channel.
• Quantifying uncertainty via posterior probability density, entropy, and information gain.

Main Results:

• Demonstrated performance quantification of Bayesian localization in a shallow-water waveguide.
• Quantified performance degradation due to noise-model mismatch.
• Established methods for assessing source location uncertainty.

Conclusions:

• Information theory provides a robust framework for evaluating Bayesian localization in acoustic environments.
• Model mismatch significantly degrades localization performance.
• The developed methods are extendable to uncertain and stochastic acoustic environments.