This technical note studies the state estimation problem for stochastic complex networks with switching topology. A set of Bernoulli random variables are used to describe the switching behavior of the network. By using the structure of the extended Kalman filter (EKF), a recursive estimator is developed for each node to guarantee an optimized upper bound on the state estimation error covariance despite the switching coupling and linearization errors. Compared with the existing results on state estimation for stochastic complex networks, a distinct feature for the proposed estimator is that the estimation errors for all nodes are not formulated in an augmented vector so that the gain matrix can be determined separately for each node by solving two Riccati-like difference equations. Based on the stochastic analysis theory, it is shown that the estimation error is bounded in mean square under certain conditions. Simulation results are provided to verify the effectiveness of the proposed estimator.

Kalman consensus filter (KCF) has been developed for distributed state estimation over sensor networks where local estimates are exchanged with time‐triggered transmission mechanism. To reduce the amount of data transfer in sensor networks, the authors propose a KCF with an event‐triggered communication protocol. The triggering decision is based on the send‐on‐delta data transmission mechanism: each sensor transmits its local estimates to its neighbours only if the difference between the most recent transmitted estimate and the current estimate exceeds a tolerable threshold. On the basis of the event‐triggered communication protocol, an optimal Kalman gain matrix is derived by minimising the mean squared errors for each sensor and a suboptimal KCF is developed for scalable considerations. By using the Lyapunov‐based approach, a sufficient condition is presented for ensuring the stochastic stability of the suboptimal KCF. A numerical example is provided to verify the effectiveness of the proposed filter.

In this article, the fixed-time consensus control is addressed for general linear multi-agent systems guided by a dynamic leader with control inputs. A new fixed-time consensus protocol is proposed based on the specific coordinate transformation and a distributed observer to estimate the leader's state information, provided that each agent in the group is controllable. It is proved that the consensus can be realized within a finite time, and an upper bound of the convergence time is also given that is independent of initial conditions. Some simulations on multiple F-18 aircrafts are done to illustrate the effectiveness of the developed fixed-time protocol.

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.

This study is concerned with the distributed state estimation problem for non‐linear systems over sensor networks. By using the strategy of consensus on prior estimates, a distributed extended Kalman filter (EKF) is developed for each node to guarantee an optimised upper bound on the state estimation error covariance despite consensus terms and linearisation errors. The Kalman gain matrix is derived for each node by solving two Riccati‐like difference equations. It is shown that the estimation error is bounded in mean square under certain conditions. The effectiveness of the proposed filter is evaluated on an indoor localisation of a mobile robot with visual tracking systems.