A new optimal batch asynchronous data fusion algorithm is proposed in this paper. Firstly, the continuous-time stochastic linear system is discretized. Secondly, based on the measurements from multiple sensors, a pseudo measurement equation is constructed at the fusion center. As a result, the process noise and the pseudo measurement noise are correlated. Finally, the Kalman filter towards one-step correlated process and measurement noise is utilized to achieve the optimal state estimate at the fusion center. Simulation instance is provided to compare the new algorithm with the existing least-square approach and sequential processing approach, the results show the optimality of the new algorithm developed in this paper.
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