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Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

High Technology Letters 2015年第1期21卷 39-45页

作者：胡振涛 Fu Chunling Cao Zhiwei Li Congcong Instituteof Image Processing and Pattern Recognition Henan University School of Physics and Electronics Henan University

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering *** with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is ***,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation ***,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering *** the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

关键词： maneuvering target tracking nonlinear filtering cubature Kalman filter(CKF) interacting multiple model(IMM)

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Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy

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High Technology Letters 2017年第2期23卷 149-155页

作者：胡振涛 Zhang Jin Fu Chunling Li Xian Instituteof Image Processing and Pattern Recognition Henan University Kaifeng 475004 P. R. China School of Physics and Electronics Henan University Kaiteng 475004 P. R. Ghina

The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking *** at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is *** 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 *** 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 *** that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion *** 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 *** theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： maneuvering target tracking interacting multiple model(IMM) central difference Kalman filter(CDKF) bootstrapping observation

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