Multiple model ballistic missile tracking with state-dependent transitions and Gaussian particle filtering Miao Yu Liyun Gong Hyondong Oh Wen-Hua Chen Jonathon Chambers 2134/27191 https://repository.lboro.ac.uk/articles/journal_contribution/Multiple_model_ballistic_missile_tracking_with_state-dependent_transitions_and_Gaussian_particle_filtering/9228434 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 reentry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilising domain knowledge. Based on this modelling 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. 2017-10-30 12:37:09 Ballistic missile tracking Multiple state models State-dependent transition probabilities Bayesian inference Gaussian particle filter Aerospace Engineering Engineering not elsewhere classified