Communication strategies to ensure generic networked observability in multi-agent systems

In this paper, we consider the state estimation in linear dynamical systems when their observations are distributed over a network of agents. We provide a Networked Kalman Filtering (NKF) approach exploring both state and observation fusion. Assuming global observability, we study the structure of the agent communication network in order to stabilize the networked estimation error. In particular, we use structured systems theoretic methods to show that the underlying network may recover observability of locally unobservable agents when the system matrices have full structured rank. In this context, we provide strategies to design communication among the agents and study the effectiveness of these links towards networked observability.