Distributed Input and State Estimation Using Local Information in Heterogeneous Sensor Networks - Tran, Dzung; Yucelen, Tansel; Sarsilmaz, Selahattin Burak; Jagannathan, Sarangapani (Frontiers in Robotics and AI, 2017)
A new distributed input and state estimation architecture is introduced and analyzed for heterogeneous sensor networks. Specifically, nodes of a given sensor network are allowed to have heterogeneous information roles in the sense that a subset of nodes can be active (that is, subject to observations of a process of interest) and the rest can be passive (that is, subject to no observations). Both fixed and varying active and passive roles of sensor nodes in the network are investigated. In addition, these nodes are allowed to have nonidentical sensor modalities under the common underlying assumption that they have complimentary properties distributed over the sensor network to achieve collective observability. The key feature of our framework is that it utilizes local information not only during the execution of the proposed distributed input and state estimation architecture but also in its design in that global stability is guaranteed once each node satisfies given local stability conditions independent from the graph topology and neighboring information of these nodes. As a special case (e.g., when all nodes are active and a positive real condition is satisfied), the asymptotic stability can be achieved with our algorithm. Several illustrative numerical examples are further provided to demonstrate the efficacy of the proposed architecture.