Motion Modelling and State Estimation for Ballistic Targets in Reentry Phase Based on Destination Information

Abstract

In some ballistic target tracking applications, the target travels to the destination with a constant horizontal heading in the reentry phase, whose states are subjected to a destination constraint. If the prior information on the destination can be acquired and effectively utilised, a significant enhancement of performance can be expected. In this paper, a three-dimensional (3D) constrained motion model is established to describe the target motion in the reentry phase. For different cases where the prior destination information is accurately known or contaminated by noise, the horizontal heading angle or the destination position is augmented into the state vector to formulate the accurate constraint relationships in the horizontal plane. Based on the augmented state vectors and the existing 2D model for reentry targets in the vertical plane, accurate state equations are derived to describe the ballistic target motion in the 3D space. Corresponding filtering methods, which employ the unscented Kalman filter to deal with the strong nonlinearity in the augmented state equation, are proposed. Simulation results of Monte Carlo experiments verify the effectiveness of the proposed constrained estimation methods. It is demonstrated that the incorporation of extra destination constraint information leads to superior tracking performance compared with the unconstrained method.