Distributed fusion filtering for multi-rate nonlinear systems with binary measurements under encryption and decryption scheme

This paper discusses the distributed fusion filtering problem for multi-rate nonlinear systems with binary measurements (BMs) based on an encryption and decryption scheme (EDS), in which the measurement outputs are represented by vectors with elements taking the values of 0 or 1. The expectation of the BMs is described by the cumulative distribution function of the standard normal distribution, where a newly defined random variable is utilized for reconstructing the BMs model. In order to ensure information security, the EDS is introduced in the data transmission process among the sensor nodes. Based on the information obtained, the local distributed filtering algorithm is proposed to obtain an upper bound on the local filtering error covariance, and the local filter gain is designed to minimize the resulting upper bound. In addition, the fusion filter is obtained with the parallel covariance intersection fusion criterion and the filtering performance is analyzed in terms of boundedness with theoretical proof. Finally, a target tracking experiment is taken to show the effectiveness and applicability of the proposed fusion filtering scheme.