Author:
Gu Peng,Jing Zhongliang,Wu Liangbin
Abstract
AbstractOne purpose of target tracking is to estimate the states of targets, and unscented Kalman filter is one of the effective algorithms for estimating in the nonlinear tracking problem. Considering the characteristics of complex maneuverability, it is easy to reduce the tracking accuracy and cause divergence due to the mismatch between the system model and the practical target motion model. Adaptive fading factor is an effective counter to this problem, having been instrumental in solving accuracy and divergence problems. Fading factor can adaptively adjust covariance matrix online to compensate model mismatch error. Moreover, fading factor not only improves the filtering accuracy, but also automatically adjusts the error covariance in response to the different situation. The simulation results show that the adaptive fading factor unscented Kalman filter has more advantages in target tracking and it can be better applied to nonlinear target tracking.
Funder
National Natural Science Foundation of China
Shanghai key Project of Basic Research
Publisher
Springer Science and Business Media LLC
Subject
Space and Planetary Science,Computers in Earth Sciences,Mechanical Engineering,Social Sciences (miscellaneous),Aerospace Engineering,Control and Systems Engineering
Cited by
2 articles.
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