An event-triggered recursive state estimation approach for time-varying nonlinear complex networks with quantization effects

This paper proposes a novel robust extended Kalman filter (REKF) for a class of discrete time-varying nonlinear complex networks with event-triggered communication and quantization effects. In this approach, it is assumed that each sensor transmits data to its estimator over two redundant communication channels. Before the data from each sensor is sent to its estimator, an event-triggered strategy is employed to reduce the wastage of communication bandwidth and unnecessary executions. Afterwards, a logarithmic quantizer is implemented to quantize data. The estimator parameters for each node are derived separately into two Riccati-like difference equations so that the achieved upper bound is minimized by the proposed estimator parameters. Finally, the simulation results are included to illustrate the performance of the derived filtering method. (C) 2020 Elsevier B.V. All rights reserved.