期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：12
页码：37-46
出版社：SERSC
摘要：In this paper, an adaptive Kalman Filtering method is presented for the state prediction of random systems. It is shown that the adaptive Kalman Filtering method in conjunction with equilibrium optimization solution can estimate the initial accelerations of targets effectively since the equilibrium optimization solution tunes the state prediction vector to diminish the error between measured value and prediction estimation value. We evaluate our model on special and random trajectories. Experimental evidence shows that the proposed method can robustly estimate an initial acceleration from a dynamic model and stably track a trajectory which is moving randomly.
