Active-passive dynamic consensus filters consist of agents subject to local observations of a process of interest (active agents) and agents without any observations (passive agents). In this paper, we introduce a new class of active-passive dynamic consensus filters using results from graph theory and systems science. Specifically, the proposed filters only require agents to exchange their current state information with neighbors in a simple and isotropic manner to reduce the overall information exchange cost of the network. In addition, we allow the roles of active and passive agents to be time-varying for making these filters suitable for a wide range of multiagent systems applications. We show that the proposed active-passive dynamic consensus filters enable the states of all agents to converge to an adjustable neighborhood of the average of the observations sensed by a time-varying set of active agents.
['J. Daniel Peterson', 'Tansel Yucelen', 'Eduardo L. Pasiliao']
Generalizations on active-passive dynamic consensus filters