Relevant Sampling Applied to Event-Based State-Estimation

Abstract

To reduce the amount of data transfer in net- worked control systems and wireless sensor networks, measurements are usually sampled only when an event occurs, rather than synchronous in time. Today's event sampling methodologies are triggered by the current value of the sensor. State-estimators are designed to cope with such methods. In this paper we propose a sampling method in which an event is triggered depending on the reduction of the estimator's uncertainty and estimation-error. As such, communication requirements are minimized while attaining a certain error- covariance matrix and estimation error at the state-estimator. Furthermore, it is proven that the error-covariance matrix is asymptotically bounded in case the designed sampling protocol is combined with an event-based state-estimator. An illustrative example shows that the developed protocol provides an improved state estimation, while minimizing communication between sensor and state-estimator.