  The state space representation of active resident space objects can be posed
in the form of a stochastic hybrid system. Satellite maneuvers may be accounted
for according to control cost or heuristical considerations, yet it is possible
to jointly consider a combination of both. In this work, Sequential Monte Carlo
filtering techniques are applied to the maneuvering target tracking problem in
an optical survey scenario, where the maneuver control inputs are characterized
in a Bayesian inference process. Due to the scarcity of data inherent to space
surveillance and tracking, model switching probabilities are not estimated but
derived from the ability of the state representation to fit incoming
measurements. A Markov Chain Monte Carlo sampling scheme is used to explore the
region assumed accessible to the object in terms of the hypothesized
post-maneuver observation and a novel and efficient control distance metric.
Results are obtained for a simulated optical survey scenario, and comparisons
are drawn with respect to a moving horizon least-squares estimator. The
proposed framework is proved to allow for a capable implementation of an
automated online maneuver detection algorithm, thus contributing to the
reduction of uncertainty in the state of active space objects.
