Gaussian Filter for Nonlinear Networked Systems With Synchronously Correlated Noises and One- Step Randomly Delayed Measurements and Multiple Packet Dropouts

In this paper, a Gaussian recursive filter is proposed for nonlinear networked systems with synchronously correlated noises and one-step randomly delayed measurements and multiple packet dropouts. Since each packet at the sensor side is sent only once and the system has one-step delay and multiple packet dropouts, the data processing center may receive zero/one/two measurements. By augmenting the system state at last epoch to the current one, employing two Bernoulli distributed random variables to describe the phenomena of delay and packet dropouts and using the prediction compensation mechanism, a novel model is derived. At the same time, to deal with the problem of correlated noise, Gaussian approximation recursive filter (GASF) is used. Based on the above processing, a nonlinear Gaussian recursive filter and corresponding numerical implementation based on cubature Kalman filter (CKF) are given. Compared with the existing results in linear systems, the proposed algorithm is more general and has higher estimation accuracy in nonlinear systems. The simulation results show the effectiveness of the proposed algorithm.