Decoupling Strong Tracking Cubature Kalman Filter for Nonlinear Systems with One-step Randomly Delayed Measurements and Correlated Noises

The existing strong tracking filter (STF) cannot deal with the filtering estimation problem of nonlinear systems with one-step randomly delayed measurements and correlated noises. In order to solve this problem, this paper proposes a novel decoupling strong tracking cubature kalman filter (DSTCKF). First of all, based on the equivalent model transformation method and the extended orthogonality criterion, a decoupling strong tracking filter (DSTF) with one-step randomly delayed measurements and correlated noises is derived. Then, the three-order spherical-radial cubature sampling technique is used to replace the calculations of posterior mean and covariance of the augmented state and available measurement in DSTF, and then the corresponding filtering recursive formula of the DSTCKF is obtained. Finally, simulation experiments are implemented based on the universal non-stationary growth model. The simulation results indicate the effectiveness and advantages of the proposed filter.