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In this paper, a novel method, process noise identification based particle filter is proposed for tracking highly maneuvering target. In the proposed method, the equivalent-noise approach is adopted, which converts the problem of maneuvering target tracking to that of state estimation in the presence of non-stationary process noise with unknown statistics. A novel method for identifying the non-stationary process noise is proposed in the particle filter framework. Compared with the multiple model approaches for maneuvering target tracking, the proposed method needs to know neither the possible multiple models nor the transition probability matrices. One simple dynamic model is adopted during the whole tracking process. The proposed method is especially suitable for tracking highly maneuvering target due to its capability of dealing with sample impoverishment, which is a common problem in particle filter and becomes serious when tracking large uncertain dynamics.