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This article is concerned with the moving horizon estimation (MHE) problem for networked linear systems (NLSs) with unknown inputs under dynamic quantization effects. For the NLSs with unknown input signals, the conventional MHE strategy is incapable of guaranteeing the satisfactory performance as the estimation error is dependent on the external disturbances. In this work, a novel MHE strategy is developed to cope with the underlying NLS with unknown inputs by dedicatedly introducing certain temporary estimates of unknown inputs, where the desired estimator parameters are designed to decouple the estimation error dynamics from the unknown inputs. A two-step design strategy (namely, decoupling step and convergence step) is proposed to obtain the estimator parameters. In the decoupling step, the decoupling parameter of the moving horizon estimator is designed based on certain assumptions on system parameters and quantization parameters. In the convergence step, by employing a special observability decomposition scheme, the convergence parameters of the moving horizon estimator are achieved such that the estimation error dynamics is ultimately bounded. Moreover, the developed MHE strategy is extended to the scenario with direct feedthrough of unknown inputs. Two simulation examples are given to demonstrate the correctness and effectiveness of the proposed MHE strategies. [ABSTRACT FROM AUTHOR]

This paper is concerned with the moving horizon estimation problem for a class of discrete time-delay systems under the Round-Robin (RR) protocol. The communication between the sensor nodes and the remote state estimator is implemented via a shared network, where only one sensor node is permitted to transmit data at each time instant for the purpose of preventing data collisions. The RR protocol is utilized to orchestrate the transmission order of sensor nodes, under which the selected node obtaining access to the network could be modeled by a periodic function. A lifting technology is introduced to reformulate the system model into a linear system without delays. The aim of the addressed problem is to develop a moving horizon estimator such that the estimation error is ultimately bounded. A sufficient condition is established to ensure the ultimate boundedness in terms of a matrix inequality. Within the established theoretical framework, two optimization problems are proposed to calculate the corresponding estimator parameters according to two different performance requirements (e.g., the smallest ultimate bound and the fastest decay rate). Finally, simulation examples are given to illustrate the effectiveness of the estimator design scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed filtering problem for continuous-time stochastic systems over sensor networks subject to Markovian switching topologies. Due to limited communication energy and bandwidth, an event-based communication scheme is proposed with the aim to decrease the transmission frequency. An individual triggering condition is put forward to regulate the communication rates for each component of the system state in order to better reflect the engineering requirements. The aim of this paper is to design a distributed filter over sensor networks with Markovian switching topologies such that the dynamics of the estimation error is exponentially mean-square bounded. It is shown that, with the proposed event-based distributed filtering algorithm, the exponential mean-square boundedness of the estimation errors is guaranteed if the sensor network is distributively detectable and the combined communication topology is strongly connected. A numerical example is presented to illustrate the usefulness of the developed algorithm. [ABSTRACT FROM AUTHOR]