Dynamic Decoupled Event-Triggered Nonlinear State Estimation for Sensor Networks with Incomplete Measurements

How to reduce communication consumptions without notable compromise in estimation performance becomes an important topic of sensor networks. Event-triggered (ET) mechanisms can solve the above problem by avoiding unnecessary data transmissions. However, existing ET mechanisms can't efficiently schedule data transmissions in sensor networks with incomplete measurements. In order to solve this problem, a measurement equation is proposed for typical optic-electric detectors with considering incomplete measurements. Based on this, a dynamic decoupled event-triggered (DDET) mechanism is designed to schedule measurement transmissions from sensors to the fusion center, and reduce interferences caused by incomplete measurements. Then based on the proposed DDET mechanism and the fifth-degree cubature Kalman filter, a dynamic decoupled event-triggered nonlinear state estimation algorithm is developed. Finally, simulation results verify the effectiveness and advantage of the proposed method.