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In this paper, we present a way to track multiple maneuvering targets with varying time-frequency signatures. A particle filter is used to track targets that have constant speeds with changing heading directions. The target motion dynamics help the particle filter achieve an angular resolution otherwise not possible by the conventional beamforming techniques. Moreover, the particle filter has a built-in target association that eliminates the need for heuristic techniques commonly used in the multiple target tracking problems. Reference priors are used to derive the probability distribution function of the acoustic array outputs given the state of the multiple target states (MTS's). Local linearization is used to approximate the importance function used in the particle filter by a Gaussian pdf. Finally, computer simulations are used to demonstrate the performance of the algorithm with synthetic data.