Relevant Sampling Applied to Event-Based State-Estimation

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.