Asynchronous Multi-Sensor Fusion Algorithm Based on the Steady-State Kalman Filter

Abstract

The paper studies an asynchronous multi-sensor fusion problem based a kind of asynchronous multi-sensor dynamic system. Firstly, this paper presents a centralized fusion algorithm based on the Kalman filter without ignoring the correlation between process noise and augmented measurement noise. It is optimal in minimum mean square error. Then using the steady-state Kalman filter to estimate and fuse. Secondly, in the condition that the local sensor estimation error is associated, a distributed fusion algorithm is given by utilizing S.L. Sun optimal information fusion criterion in minimum error covariance matrix trace at fusion center. In distributed algorithm, the value transmitting to the fusion center is determined by the local sensor estimation based on the steady-state Kalman filter and one step predictive value. Since both optimal fusion algorithm standards are different, so the fusion precision will vary. Finally the effectiveness of the algorithm is verified by computer simulation.