Distributed Dissipative Estimation With Partial Nodes Measurements: A Two-Dimensional System Approach

This paper addresses the distributed dissipative estimation problem subject to partial nodes measurements over sensor networks. A novel partial-nodes-based distributed estimation algorithm is proposed to accurately estimate the states across the entire network, even when measurements are only available from a subset of nodes. Moreover, the consensus protocol is employed as the information fusion strategy, enabling local communication among neighboring nodes to drive their estimates toward consensus. Sufficient conditions are derived to guarantee that the estimation error system is asymptotically stable and satisfies the predefined dissipativity performance by applying dynamic stability theory and linear matrix inequality (LMI) technique. Within the established two-dimensional system framework, the consensus update is interpreted into an additional dimension, accompanied by the fundamental estimator update dimension. Therefore, the estimator gain and the consensus gain are decoupled, which can be further designed effectively by making use of the LMI technique. In addition, an adaptive consensus weight scheme is developed to enhance the overall estimation accuracy of the proposed partial-nodes-based estimation algorithm. Finally, simulation examples are provided to demonstrate the validity and applicability of the proposed distributed estimation strategy.