Distributed estimation based on weighted data aggregation over delayed sensor networks

In this paper, data aggregation laws and distributed observers over delayed sensor networks with any topology are proposed. In the proposed method, each node compensates communication delays of received data. For the delay compensation, each node predicts the future output based on state space models. To stabilize the aggregation data in any networks, the received data are multiplied by weight coefficients before the aggregation. The stability condition of the weighted aggregation laws is expressed by a weighted adjacency matrix. The aggregated value of the measurements at each node is expressed by a linear time-varying function of the current state. To estimate the state, we utilize the Kalman filters as the distributed observers. The effectiveness of the proposed method is confirmed by a numerical simulation. (c) 2020 Elsevier Ltd. All rights reserved.