Multi-sensor Information Fusion Cubature Kalman Filter for Nonlinear System

In this paper, a multi-sensor information fusion Cubature Kalman Filter for nonlinear systems is presented. Based on the Gaussian Approximation filter, Cubature Kalman Filter is introduced in this paper. In order to improve the estimation accuracy, a multi-sensor information fusion method is adopted. The fusion method includes centralized fusion and covariance intersection fusion. The centralized fusion algorithm is optimal, but the computational burden is too large. To avoid the calculation of cross-covariance matrices, a distributed fission filter is presented by using the covariance intersection fusion algorithm, which can reduce the computational burden. And the relationship between the accuracy and the computation complexities among the two fusion algorithm are analyzed. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness.