Anti-eavesdropping distributed fusion filtering for multi-rate nonlinear systems with missing measurements*

In this paper, a novel anti-eavesdropping distributed fusion filtering algorithm is proposed for multi-rate nonlinear systems with missing measurements over sensor networks, where the missing measurements are characterized by Bernoulli random variables. During the information exchange of sensor nodes, it is assumed that there exists an eavesdropper. Accordingly, an active contamination strategy is introduced to ensure the confidentiality of information transmission. Meanwhile, a corresponding compensation strategy is adopted in the receiver side in order to reduce the performance loss. The main objective of this paper is to design a distributed filtering algorithm such that an upper bound on the local filtering error covariance is obtained and the filter gain optimizing the upper bound is given accordingly. In addition, the fusion filter is obtained by using the covariance intersection fusion criterion, and the monotonicity is analyzed about the upper bound on the filtering error covariance with respect to the occurrence probability of missing measurements. Finally, the effectiveness of the proposed algorithm is demonstrated through a simulation experiment.