Distributed Estimation on Sensor Networks With Measurement Uncertainties

This article brings distributed estimation for large-scale systems. The plant is considered affected by process disturbance, and measurements are corrupted by measurement noise. The proposed approach fuses measurements of differing reliability so that all nodes reach consensus on the plant's state estimate. This architecture is flexible to the addition of new nodes and, to a certain extent, robust to node or communication link failures. In spite of limited observability by each of the nodes, data fusion over the network allows each node to obtain the full estimate of the plant's state. Structured Lyapunov functions are used to prove the convergence of the estimator. Estimation error covariances are analyzed in detail. The proposed distributed observer design is validated by numerical simulations.