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