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High Technology Letters - Abstract Contents
2  Time:  2017-9-18
Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy
Hu Zhentao (ºúÕñÌÎ), Zhang Jin, Fu Chunling, Li Xian
Get Time£º2017-9-13 15:20:17  Publishing Time£º2017-9-13 15:20:17
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 multiª²sensor 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(EnKF), 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 multiª²sensor fusion method without increasing the number of physical sensors. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
Key words: maneuvering target tracking, interacting multiple model (IMM), central difference Kalman filter (CDKF), bootstrapping observation
2017-9-13 15:20:17 [Abstract] [PDF]

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