Open AccessStrong tracking SCKF based on adaptive CS model for manoeuvring aircraft tracking
Author(s) -
Zhang Haowei,
Xie Junwei,
Ge Jiaang,
Lu Wenlong,
Liu Bingzhen
Publication year - 2018
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2017.0467
Subject(s) - kalman filter , acceleration , tracking (education) , fading , control theory (sociology) , orthogonality , computer science , recursion (computer science) , algorithm , filter (signal processing) , function (biology) , position (finance) , mathematics , artificial intelligence , computer vision , physics , geometry , psychology , pedagogy , decoding methods , control (management) , finance , classical mechanics , evolutionary biology , economics , biology
A novel tracking algorithm is proposed by the integration of the adaptive current statistical (CS) model and the modified strong tracking (ST) square‐root cubature Kalman filter (SCKF) for the manoeuvring aircraft tracking problem. Firstly, the acceleration recursion equation and the acceleration mean input estimation are combined in order to realise the adaptive adjustment of the CS model. Then, the introduced position of the fading factor is relocated from the orthogonality principle and a new formula is put forward. Additionally, the strong manoeuver detection function is established to adjust the manoeuvring frequency of the CS model. The simulation results show that the proposed algorithm possesses better tracking accuracy than the multiple‐fading‐factor SCKF based on the CS model, the SCKF‐ST filter based on the modified CS model and the interacting‐multiple‐model (IMM)‐SCKF. Moreover, the proposed algorithm decreases the runtime by 40% compared with the IMM‐SCKF.