  This paper presents a scalable Bayesian technique for decentralized state
estimation from multiple platforms in dynamic environments. As has long been
recognized, centralized architectures impose severe scaling limitations for
distributed systems due to the enormous communication overheads. We propose a
strictly decentralized approach in which only nearby platforms exchange
information. They do so through an interactive communication protocol aimed at
maximizing information flow. Our approach is evaluated in the context of a
distributed surveillance scenario that arises in a robotic system for playing
the game of laser tag. Our results, both from simulation and using physical
robots, illustrate an unprecedented scaling capability to large teams of
vehicles.
