Abstract：In order to solve the limitation of the static model set of the traditional interactive multiple model(IMM)algorithm, a interactive multi-model set adaptation collaborative filtering(SAC-IMM)algorithm is proposed. By calculating the model matching probability between the target and different models in the current model set, the best model and the worst model in the current model match are automatically determined. The adaptive process of the model set is achieved by changing the structure of the static model set by using activation, retention and elimination strategies. Compared with the traditional IMM algorithm, the SAC-IMM algorithm proposed has a certain degree of improvement in positioning accuracy. By comparing with other IMM algorithms that have been proposed, the experimented results show that the SAC-IMM algorithm proposed has been optimized for state estimation of speed, acceleration, and turning rate. The proposed method can improve the accuracy of target tracking and positioning to a certain extent.
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2021, Vol. 42 
