DATA SCHEDULING AND STATE ESTIMATION FOR LARGE-SCALE EVENT-BASED SENSOR ARRAYS

In this paper, we study the problem of sensor scheduling and state estimator design for large-scale array-based event sensors, where each sensor incorporates an inherent event-based output transmission mechanism. To effectively extract the information from sensor arrays, an online sensor scheduling strategy based on similarity of measurement data is introduced. The observability of systems equipped with dynamic spatial-temporal data selection mechanisms (consisting of event-based and sensor scheduling protocols) is analyzed, where a criterion for \epsilon-observability is derived. Furthermore, an event-based state estimator aimed at obtaining ellipsoidal regions containing system states is designed. The convergence property of the proposed state estimation algorithm is proved through analyzing the asymptotic boundness of sizes of the estimated state ellipsoids. A comparative analysis of estimation performances with and without sensor scheduling is conducted. The computational complexity of the designed state estimation algorithm is also discussed. Finally, the effectiveness of the proposed event-based estimator is demonstrated by numerical simulations.