Event-triggered-based sequential fusion filters for CPSs with deception attacks and correlated noises

In order to reduce the computational burden of fusion center and ease off the communication pressure, for the CPSs with deception attacks and correlated noises under the SoD event-triggered mechanism, a sequential covariance intersection fusion Kalman filter and a sequential inverse covariance intersection fusion Kalman filter are presented, where the SoD event-triggered mechanism is considered in the data transmission process of the CPSs, and random deception attacks occures in the triggered observations, which are sent to the estimator and then to the fusion center. Both algorithms have good tracking results and good accuracies, a little lower than the centralized fusion algorithm. Simulation example of the tracking system verifies the effectiveness of two above sequential fusion algorithms.