Augmented sensing-based state estimation for cooperative Multi-Agent Systems

Distributed estimation and control schemes play an important role in cooperative Multi-Agent Systems (MASs), addressing various challenges of the insufficient capabilities of individual agents, such as limited sensing ranges. In this paper, we propose an augmented estimation algorithm that enables state estimation of agents which are out of sensing range from a local monitoring agent. The algorithm utilizes the probability of out-of-range agents affecting the behavior of in-range agents; this allows the monitoring agent to indirectly obtain information about unobserved agents. Then, based on a Bayesian approach, the proposed estimation algorithm recursively computes the state estimate by tracking the observed behaviors and their interactions with out-of-range agents. The performance of the proposed algorithm is demonstrated with numerical simulations of formation flight and cooperative surveillance.