A stopping rule for multi-agent consensus with bounded noise in measurements

A multi-agent based averaging consensus algorithm is investigated over noisy undirected connected graphs. It is assumed that each agent can know measurements of the neighbors' state with bounded errors. A rigorous stopping rule which reveals an explicit relation between the number of iterations and the closeness of the agreement with a probabilistic guarantee is established based on a matrix Bernstein inequality. The result is illustrated through a numerical example.