This paper proposes a distributed observer on a sensor network, where communication on the network is randomly performed. This work is a natural extension of Kalman consensus filter approach to the cases involving random communication. In both bidirectional and unidirectional communication cases, gain conditions that guarantee improvement of estimation error convergence compared to the case with no communication are obtained. The obtained conditions are more practical than those of previous studies and give appropriate cooperative gains for a given communication probability. The effectiveness of the proposed method is confirmed by computer simulations.

This paper proposes a distributed delay-compensated observer for a wireless sensor network with delay. Each node of the sensor network aggregates data from the other nodes and sends the aggregated data to the neighbor nodes. In this communication, each node also compensates communication delays among the neighbor nodes. Therefore, all of the nodes can synchronize their sensor measurements using scalable and local communication in real-time. All of the nodes estimate the state variables of a system simultaneously. The observer in each node is similar to the delay-compensated observer with multi-sensor delays proposed by Watanabe et al. Convergence rates for the proposed observer can be arbitrarily designed regardless of the communication delays. The effectiveness of the proposed method is verified by a numerical simulation.

This paper addresses a distributed filter over wireless sensor networks for optimal estimation. A distributed filter over the networks allows all local estimators to calculate optimal estimates with a scalable communication cost. Outputs of the distributed filter for the optimal estimation can be denoted as a solution of a consensus optimization problem. Thus, the distributed filter is designed based on distributed alternating direction method of multipliers (ADMM). The remarkable points of the distributed filter based on the ADMM are that: the distributed filter has a faster convergence rate than distributed subgradient projection algorithm; the weight, which is optimized by a semidefinite programming problem, accelerates the convergence rate of the proposed method.

This paper addresses distributed optimal estimation over wireless sensor networks with scalable communications. For realizing scalable communication, a data-aggregation method is introduced. Since our previously proposed method cannot guarantee the global optimality of each estimator, a modified protocol is proposed. A modification of the proposed method is that weights are introduced in the data aggregation. For selecting the weight values in the data aggregation, a redundant output reduction method with minimum covariance is discussed. Based on the proposed protocol, all estimators can calculate the optimal estimate. Finally, numerical simulations show that the proposed method can realize both the scalability of communication and high accuracy estimation.

In this paper, we consider the design problem of an unknown-input observer for distributed network systems under the existence of communication delays. In the proposed method, each node estimates all states and calculates inputs from its own estimate. It is assumed that the controller of each node is given by an observer-based controller. When calculating each node, the input values of the other nodes cannot be utilized. Therefore, each node calculates alternative inputs instead of the unknown inputs of the other nodes. The alternative inputs are generated by own estimate based on the feedback controller of the other nodes given by the assumption. Each node utilizes these values instead of the unknown inputs when calculating the estimation and delay compensation. The stability of the estimation error of the proposed observer is proven by a Lyapunov-Krasovskii functional. The stability condition is given by a linear matrix inequality (LMI). Finally, the result of a numerical simulation is shown to verify the effectiveness of the proposed method.

This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.

This study proposes a maximum-likelihood-estimation method for a quadrotor UAV given the existence of sensor delays. The state equation of the UAV is nonlinear, and thus, we propose an approximated method that consists of two steps. The first step estimates the past state based on the delayed output through an extended Kalman filter. The second step involves calculating an estimate of the present state by simulating the original system from the past to the present. It is proven that the proposed method provides an approximated maximum-likelihood-estimation. The effectiveness of the estimator is verified by performing experiments.