Open AccessAFAKF for manoeuvring target tracking based on current statistical model
Author(s) -
Yang Yongjian,
Fan Xiaoguang,
Zhuo Zhenfu,
Wang Shengda,
Nan Jianguo,
Huang Jinke
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0030
Subject(s) - kalman filter , fading , robustness (evolution) , control theory (sociology) , computer science , acceleration , algorithm , artificial intelligence , physics , decoding methods , biochemistry , chemistry , control (management) , classical mechanics , gene
The fixed maximum acceleration of current statistical model (CSM) will lead to the deterioration of Kalman filter. To improve the performance of CSM in target tracking, a new modified CSM (MCSM) and a new Kalman filter (KF) are proposed. The new model, which employs innovation dominated subjection function to adaptively adjust maximum acceleration, has a better performance in target tracking, but it is very sensitive to innovation and will lead to a fluctuant phenomenon when target manoeuvres occur. The new adaptive fading Kalman filter which is formed by amendatory KF (AKF) and adaptive fading KF can weaken the fluctuant phenomenon caused by MCSM. The principle and deducing of AKF are specifically elaborated based on probability theory. Three simulations results indicate the high performance and robustness of MCSM and MCSM‐adaptive fading amendatory Kalman filter in target tracking.