Abstract
This paper proposes a new method for tracking the entire trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are used to represent the different ballistic missile dynamics in three flight phases: boost, coast, and re-entry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilizing domain knowledge. Based on this modeling system and radar measurements, a state-dependent interacting multiple model approach based on Gaussian particle filtering is developed to accurately estimate information describing the ballistic missile such as the phase of flight, position, velocity, and relevant missile parameters. Comprehensive numerical simulation studies show that the proposed method outperforms the traditional multiple model approaches for ballistic missile tracking.
Highlights
A ballistic missile (BM) is one of the major threats from the air in modern warfare, so it is important to intercept before it hits the target on the ground
Multiple state models corresponding to different flight phases have been applied in the development of interacting multiple model (IMM) algorithms where the state estimation is given by three steps: interaction, filtering and combination [18]
Based on different models defined in the previous section, a state-dependent interacting multiple model Gaussian particle filtering (SD-IMMGPF) algorithm is developed for ballistic missile tracking
Summary
A ballistic missile (BM) is one of the major threats from the air in modern warfare, so it is important to intercept before it hits the target on the ground. In order to accurately track the whole trajectory of the BM, multiple state models need to be used as the BM experiences different flight phases from the launch to impact To this end, Benavoli et al [13] proposed an optimisation-based method to estimate the BM states and model parameters by adopting multiple models. Different BM dynamic models (as detailed in [13]) have been designed to construct the cost function before and after the estimated burnout time and optimised for the state and parameter estimation The limitation of this method is that it is always assumed that the tracking of a BM starts from the boost phase. Multiple state models corresponding to different flight phases have been applied in the development of IMM algorithms where the state estimation is given by three steps: interaction, filtering and combination [18].
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