The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly.Aiming at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed.The framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of IMM.Considering the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise.On that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way.The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm. The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking directly. Aiming at improving performance of model filters, a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is proposed. The framework of interactive multiple model (IMM) is used to realize identification of motion pattern, and a central difference Kalman filter (CDKF) is selected as the model filter of IMM. Consongring the advantage of multi-sensor fusion method in improving the stability and reliability of observation information, the hardware cost of the observation system for multiple sensors is, meanwhile, according to the data assimilation technique in Ensemble Kalman filter (En KF), a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation noise. Of that basis, these bootstrapping observati ons are reasonably used to optimize the filtering performance of CDKF by means of weight fusion way. The object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical sensors. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.