Exploiting correlation in passive sonar Bayesian localization techniques.

Abstract

Low frequency acoustic signals in shallow water are strongly affected by interference between multiple paths resulting from boundary interactions. As the acoustic source moves through this interference pattern, the spatial variation in transmission loss can result in strong temporal modulation of the received signal, which can be used to localize the source. Acoustic propagation models can produce accurate transmission loss field predictions, but are sensitive to ocean environmental parameters such as bottom composition, bathymetry, and sound speed profile. If the uncertainty in the undersea environment can be described by probability density functions of these parameters, Monte Carlo forward models can be used to produce an ensemble of possible transmission loss realizations. A probabilistic model representing this ensemble must include a density function of transmission loss at each location, as well as correlation of transmission loss between locations. In addition, the choice of probabilistic model has a large impact on the form of the resulting Bayesian localization algorithm. Previous results have shown that including spatial correlation of transmission loss can result in improved localization. This talk will introduce a non‐Gaussian probabilistic model for representing uncertainty in transmission loss predictions that includes correlation, and discuss the resulting recursive Bayesian localization algorithm.