Kalman Filter With Innovation-Based Triggering

Due to the constraints of the bandwidth and energy, the communication rate of sensors-to-estimator may be required to be reduced to save communication resources and energy in network control systems (NCSs). This paper studies the remote state estimation triggered by the innovation to meet expected estimation performance in order to improve the performance of the whole system under reducing communication rate. We propose an event-trigger based on measurement innovation, which decide on how information could be sent to remote estimator for estimation. Then under the Gaussian assumption of the predicted conditional probability density, a minimum mean squared error (MMSE) Kalman filter with innovation-based triggering is derived based on the Bayes Rule which realizes the tradeoff between communication rate and estimation quality. Furthermore, it provides the solution to the average communication rate under a given threshold and the optimal threshold value in the case of known communication rate. A numerical example is simulated to verify the effectiveness and correctness of the designed filter.