Multimodel filtering of partially observable space object trajectories

Abstract

In this paper we present methods for multimodel filtering of space object states based on the theory of finite state time nonhomogeneous cadlag Markov processes and the filtering of partially observable space object trajectories. The state and observation equations of space objects are nonlinear and therefore it is hard to estimate the conditional probability density of the space object trajectory states given EO/IR, radar or other nonlinear observations. Moreover, space object trajectories can suddenly change due to abrupt changes in the parameters affecting a perturbing force or due to unaccounted forces. Such trajectory changes can lead to the loss of existing tracks and may cause collisions with vital operating space objects such as weather or communication satellites. The presented estimation methods will aid in preventing the occurrence of such collisions and provide warnings for collision avoidance. © 2011 SPIE.