Bayesian passive sonar tracking in the context of active-passive data fusion
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This thesis investigates the improvements that can be made to Bayesian passive sonar tracking in the context of active-passive sonar data fusion. Performance improvements are achieved by exploiting the prior information available within a typical Bayesian data fusion framework. The algorithms developed are tested against both simulated data and data measured during the SEABAR 07 sea trial. Results show that the proposed approaches achieve improved detection, decreased estimation error, and the ability to track quiet targets in the presence of loud interferers.