  We propose a Bayesian method for distributed sequential localization of
mobile networks composed of both cooperative agents and noncooperative objects.
Our method provides a consistent combination of cooperative self-localization
(CS) and distributed tracking (DT). Multiple mobile agents and objects are
localized and tracked using measurements between agents and objects and between
agents. For a distributed operation and low complexity, we combine
particle-based belief propagation with a consensus or gossip scheme. High
localization accuracy is achieved through a probabilistic information transfer
between the CS and DT parts of the underlying factor graph. Simulation results
demonstrate significant improvements in both agent self-localization and object
localization performance compared to separate CS and DT, and very good scaling
properties with respect to the numbers of agents and objects.
