Abstract
Part I of this series considers a probabilistic approach to signal processing using Bayesian inversion. This approach allows incorporation of prior knowledge, fusion over multiple data to improve parameter estimates, and interpretation of data in spite of incomplete knowledge. In Part II this inversion technique is applied to the problem of localizing an acoustic source in a shallow water ocean environment. Simulation results are presented that demonstrate the incorporation of additional data to improve the source localization. Further simulation results demonstrate implicit data fusion over frequency and multiple sensors. A third set of simulations examines the case of two sources and demonstrates isolation of a weak source in the presence of a strong interfering source.
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