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This paper describes a Bayesian approach to detecting and tracking multiple moving targets using acoustic data from multiple passive arrays. We describe a surveillance application, where a collection of fixed-location passive acoustic arrays is charged with monitoring a predefined spatial region. Our approach combines a unique hybrid discrete-grid/particle approximation to the posterior with a dynamic density factorization. This results in a novel 2-D (X/Y) multisensor multitarget tracker that uses bearing measurements only. The efficacy of the algorithm is illustrated both in simulation and on collected at-sea data.