ECE Seminar - State Estimation with Event Sensors: Observability Analysis and Multi-Sensor Fusion
Abstract: This work investigates a state estimation problem for linear time-invariant systems based on polarized measurement information from event sensors. To enable estimator design, a new notion of observability, namely, ε-observability is defined with the precision parameter ε which relates to the worst-case performance of inferring the initial state, based on which a criterion is developed to test the ε-observability of discrete-time linear systems. Utilizing multi-sensor polarity data from event sensors and the implicit information hidden in event-triggering conditions at no-event instants, an iterative event-triggered state estimator is designed to evaluate a set containing all possible values of the state. Distributed implementation of the estimation algorithm utilizing a two-layer processor network of hierarchy architecture is discussed, and the temporal computational complexity of the algorithm implemented in centralized and distributed ways is analyzed. The efficiency of the proposed event-triggered state estimator is verified by numerical experiments.
Dawei Shi is presently a Professor in the School of Automaton at the Beijing Institute of Technology, Beijing, China. He received the B.Eng. degree in Electrical Engineering and Automaton from the Beijing Institute of Technology in 2008, and the Ph.D. degree in control systems from the University of Alberta in 2014. In 2017–2018, he was a Postdoctoral Fellow in Bioengineering at the Harvard University. His research focuses on the analysis and design of advanced sampled-data control systems, with applications to biomedical engineering, robotics, and motion systems. He serves as an Associate Editor/Technical Editor for several international journals, including IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Electronics and IEEE Control Systems Letters.