Partial-Nodes-Based Estimation for Complex Networks With Random Inner Coupling
研究了通信容量受限下复杂网络的分布式状态估计问题，采用随机传输策略并利用部分可用测量值设计估计器，推导了状态预测误差协方差的上界并分析了稳定性。
This article investigates distributed state estimation of complex networks (CNs) with limited communication capacity. A random transmission strategy is used to overcome the communication capacity constraint between two nodes. A distributed state estimator that makes use of the partially available measurements is designed. To handle the cross-term, Young’s inequality is employed, and an upper bound (UB) for the state prediction error covariance (PEC) is derived. An optimal estimation gain is then devised based on the derived state PEC. The stability of the UB is analyzed using a vectorization approach, and then a sufficient condition for stability is obtained. Finally, a numerical simulation is carried out to validate the effectiveness of the proposed distributed estimator and confirm the accuracy of the derived UB.