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Considering the specific driving environment as well as complex and changeable motion patterns of agricultural vehicles, when a single motion model is used to track and estimate the driving state, there will be low filtering accuracy or even loss of the target during vehicle maneuvering. In this paper, interactive multiple model (IMM) algorithm is combined with extended Kalman filter to effectively track changes of the target's motion mode, thereby avoiding low filtering accuracy or serious state estimation inaccuracy. Finally, through t
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