  Some challenging problems in tracking multiple objects include the
time-dependent cardinality, unordered measurements and object parameter
labeling. In this paper, we employ Bayesian Bayesian nonparametric methods to
address these challenges. In particular, we propose modeling the multiple
object parameter state prior using the dependent Dirichlet and Pitman-Yor
processes. These nonparametric models have been shown to be more flexible and
robust, when compared to existing methods, for estimating the time-varying
number of objects, providing object labeling and identifying measurement to
object associations. Monte Carlo sampling methods are then proposed to
efficiently learn the trajectory of objects from noisy measurements. Using
simulations, we demonstrate the estimation performance advantage of the new
methods when compared to existing algorithms such as the generalized labeled
multi-Bernoulli filter.
