One purpose of target tracking is to estimate the states of targets, and Unscented Kalman filter(UKF) 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(AFUKF) has more advantages in target tracking and it can be better applied to nonlinear target tracking.
Adaptive Fading Factor Unscented Kalman Filter with Application to Target Tracking
Lect. Notes Electrical Eng.
International Conference on Aerospace System Science and Engineering ; 2020 ; Shanghai, China July 14, 2020 - July 16, 2020
Proceedings of the International Conference on Aerospace System Science and Engineering 2020 ; Kapitel : 42 ; 579-588
02.06.2021
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Adaptive Fading Factor Unscented Kalman Filter with Application to Target Tracking
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