Asynchronous Consensus-Based Distributed Target Tracking

This paper addresses the problem of distributed target tracking, performed by a network of agents which update their local estimates asynchronously. The proposed solution extends and improves an existing consensus-based distributed target tracking framework to cope with real-world settings in which each agent is driven by a different clock. In the consensus-based target tracking framework, it is assumed that only a few agents can actually measure the target state at a given time, whereas the remainder is able to perform a model-based prediction. Subsequently, an algorithm based on max-consensus makes all the agents agree, in finite time, on the best available estimate in the network. The limitations imposed by the assumption of synchronous updates of the network nodes are here overcome by the introduction of the concept of asynchronous iteration. Moreover, an event-based approach makes for the lack of a common time scale at the network level. Furthermore, the synchronous scenario can be derived as a special case of the asynchronous setting. Finally, numerical simulations confirm the validity of the approach.