Sample-Efficient Kalman Filter with Intermittent Measurement for Dynamical System

This paper investigates the issue of sample-efficient Kalman filter with intermittent measurement for dynamical system over wireless sensor networks, which only transmits measured values to the estimator when the certain conditions occurs, thereby reducing communication costs. Under the linear unbiased minimum variance norm, the event-triggered Kalman filter with intermittent measurement for dynamical system is presented, where the optimal filtering gain could be gained through minimizing the estimated error covariance matrices. In addition, the stability of the system filtering error could be analyzed via making use of the Lyapunov-based method. Eventually, the filter’s effectiveness is verified by numerical examples.