Description

In a multi-agent data fusion scenario, agents may iteratively exchange their states to arrive at a consensus state which signifies 'general agreement' among the agents. Agent states that are being exchanged may have been generated from hard (i.e., physics based) or soft (i.e., human based evidence, such as opinions or beliefs regarding an event) sensors. Convergence analysis becomes an extremely challenging problem in such complex hard+soft fusion environments, which may also involve complex evidence fusion/updating strategies, communication delays and dynamic links. In this talk, we model agent evidence within the Dempster-Shafer (DS) theoretic framework and analyze agent consensus by viewing the process of agents exchanging and revising their states as an iteration of a pool of paracontracting operators. Due to its DS theoretic basis, this consensus protocol can deal with a wider variety of data imperfections characteristic of hard+soft data fusion environments. It also easily adapts itself to networks where agent states are captured with probability mass functions because they can be considered a special case of DS theoretic models.