Distributed Estimation in Multi-Agent Networks

Abstract

A problem of distributed state estimation at multiple agents that are physically connected and have competitive interests is mapped to a distributed source coding problem with additional privacy constraints. The agents interact to estimate their own states to a desired fidelity from their (sensor) measurements which are functions of both the local state and the states at the other agents. For a Gaussian state and measurement model, it is shown that the sum-rate achieved by a distributed protocol in which the agents broadcast to one another is a lower bound on that of a centralized protocol in which the agents broadcast as if to a virtual CEO converging only in the limit of a large number of agents. The sufficiency of encoding using local measurements is also proved for both protocols.