Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1195-1209.doi: 10.23919/JSEE.2022.000115

• CONTROL THEORY AND APPLICATION • Previous Articles     Next Articles

Maneuvering target state estimation based on separate modeling of target trajectory shape and dynamic characteristics

Zhuanhua ZHANG(), Gongjian ZHOU*()   

  1. 1 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
  • Received:2021-04-22 Online:2022-10-27 Published:2022-10-27
  • Contact: Gongjian ZHOU;
  • About author:|ZHANG Zhuanhua was born in 1989. She received her M.E. degree from Northwest Normal University. She is currently pursuing her Ph.D. degree in Harbin Institute of Technology. Her research interests include estimation and tracking. E-mail:||ZHOU Gongjian was born in 1979. He is a Ph.D. and a professor with the Department of Electronic Engineering, Harbin Institute of Technology. He is also a Longjiang Young Scholar. His research interests include estimation, tracking, detection, information fusion and signal processing. E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61671181).


The state estimation of a maneuvering target, of which the trajectory shape is independent on dynamic characteristics, is studied. The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics. However, this is not true in the applications of road-target, sea-route-target or flight route-target tracking, where target trajectory shape is uncoupled with target velocity properties. In this paper, a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed. The trajectory of a target over a sliding window is described by a linear function of the arc length. To determine the unknown target trajectory, an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates. At every estimation cycle except the first one, the interaction (mixing) stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector, which is determined by the least squares (LS). Numerical experiments are conducted to assess the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.

Key words: maneuvering target tracking, separate modeling, natural parametric function, interacting multiple model (IMM) filter, data fitting, state augmentation