An improved CI EKF data fusion algorithm for multi-sensor time-delay system

Abstract

In order to solve the problem of time-delay in non-linear multi-sensor information fusion, a simulation model of moving target tracking in sensor networks with the time-delay state and observation system is established. Using the augmented matrix to transform the time-delay system into the non-time-delay system, an improved Covariance Intersection (CI) Extended Kalman filter (EKF) data fusion algorithm for multi-sensor systems with time-delay is presented. This method avoids calculating any two local filter error cross-covariance matrices and greatly reduces the computational complexity and time. Analysing the precision of this method, comparing the precision of improved CI EKF data fusion algorithm and the locally optimal fusion EKF algorithm. The results show that the precision of improved CI EKF data fusion algorithm is higher than that of the local EKF and close to the precision of the optimal EKF fusion.