IEEE Trans Cybern. 2018 Feb;48(2):818-824. doi: 10.1109/TCYB.2017.2653242. Epub 2017 Jan 24.
This paper studies the state estimation problem for nonlinearly coupled complex networks. A variance-constrained state estimator is developed by using the structure of the extended Kalman filter, where the gain matrix is determined by optimizing an upper bound matrix for the estimation error covariance despite the linearization errors and coupling terms. Compared with the existing estimators for linearly coupled complex networks, a distinct feature of the proposed estimator is that the gain matrix can be derived separately for each node by solving two Riccati-like difference equations. By using the stochastic analysis techniques, sufficient conditions are established which guarantees the state estimation error is bounded in mean square. A numerical example is provided to show the effectiveness and applicability of the proposed estimator.
本文研究了非线性耦合复杂网络的状态估计问题。通过利用扩展卡尔曼滤波器的结构，开发了一种方差约束状态估计器，其中增益矩阵通过优化估计误差协方差的上界矩阵来确定，尽管存在线性化误差和耦合项。与用于线性耦合复杂网络的现有估计器相比，所提出的估计器的一个显著特点是，通过求解两个类似 Riccati 的差分方程，可以为每个节点分别推导出增益矩阵。通过使用随机分析技术，建立了充分条件，保证了状态估计误差的均方有界。提供了一个数值示例来说明所提出的估计器的有效性和适用性。
