Tracking a ballistic re-entry vehicle with a sequential Monte-Carlo filter

This paper deals with a missile defense challenge, i.e. tracking a ballistic vehicle in re-entry phase. While derived Kalman filters were mostly used to solve this nonlinear filtering problem, a Sequential Monte-Carlo (SMC) filter, called Bootstrap filter, is herein presented. It operates by generating a large set of random samples, which approximates the probability density function of a state vector, in order to implement the recursive Bayesian estimation. It is compared, in various conditions of drag dynamics and observations, with a Cartesian Coordinate Extended Kalman filter (EKF), specifically designed for that tracking purpose. The results are contrasting: the EKF filter often appears to be as efficient as the SMC filter with much less computation time, while the SMC filter seems to be more suited to taking unusual measurements into account. Generally, SMC filtering gives a larger modeling framework for integration of imprecise information about dynamics, environment and measurement.