Maneuvering star-convex extended target tracking using random hypersurface model
The extended target tracking problem with strong maneuverability requires not only estimation of the target's motion,but also accurate identification of changes in the target's shape during maneuvering.Due to the high-dimensional nonlinearities caused by both target maneuvering and model complexities,this paper proposes an extended target maneuvering direction adaptive tracking algorithm based on a random hypersurface model(RHM).This algorithm uses an input estimation(IE)chi-square detector to judge the target's maneuvering,and corrects the prior shape parameters based on the magnitude and direction of the maneuver,the measurement is updated based on prior information,and the unscented Kalman filtering algorithm is combined to achieve tracking of the state and shape recognition of maneuvering extension targets.The root mean square error(RMSE)and quasi-Jaccard distance are used to evaluate the tracking quality of the target centroid position and the tracking performance of the shape,respectively.Simulation results demonstrate the effectiveness of the proposed algorithm.
maneuver targetextended targetstar-convex random hypersurfacedirection adaptiveprior shape parameter
NETL
NSTL
万方数据
