Innovation-trigger-based sequential fusion Kalman filter for CPSs with multiplicative noises against deception attacks

For the CPSs with multiplicative noises against deception attacks, the sequential fusion filters under the innovation-triggered mechanism are presented. The random deception attacks occur in the transmitted measurements, the innovation-triggered mechanism is introduced to ease off the network transmission pressure, the fusion center makes two-sensor fusion estimation under three different fusion algorithms according to the arriving order of the local estimators. The results indicate that three sequential fusion algorithms can effectively ease off random attack effection and computational pressure, and the accuracy of sequential state fusion filter is higher than those of SCI fusion filter and SICI fusion filter. A simulation example verifies the effectiveness of above sequential fusion algorithms.