Skip-glide target tracking based on improved Jerk model

Abstract

Aiming at the problem of tracking skip-glide targets using radar, this paper proposes an Unscented Kalman Filter (UKF) tracking algorithm based on the improved Jerk model. Aiming at the estimation error introduced by the artificial preset acceleration variance and Jerk frequency of the conventional Jerk algorithm, this method calculates the acceleration variance adaptively through the current position estimation value and the current position one-step prediction value, and correlates the jerk frequency with the acceleration variance, realizing the adaptive adjustment of the model parameters while estimating the target motion state. At the same time, the improved Jerk model is combined with UKF algorithm, and the overall algorithm flow is given, and simulation experiments are carried out. The simulation results show that compared with the conventional Jerk model algorithm, the method proposed in this paper realizes the adaptive adjustment of model parameters, making the tracking process more adaptable to the maneuvering characteristics of the target.

Keywords

• jump glide trajectory|jerk model|ukf algorithm|adaptive adjustment