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In this paper, a multi-model Rao-Blackwellised particle filter algorithm is presented for tracking high maneuvering target in distributed acoustic sensor networks. It is more efficient for high-dimension nonlinear and non-Gaussian estimation problems than generic particle filter, and by stratified particles sampling from a set of system models, it can tackle the target's maneuver perfectly. In the simulation comparison, a high maneuvering target moves through an acoustic sensor network field. The target is tracked using both the RBPF and the multi-model RBPF algorithms, and a location-central protocol is applied for energy conservation. The results show that our approach has great performance improvements, especially when the target is making maneuver.