Summary: For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on the on-line identification of the Moving Average (MA) innovation models, and based on the solution of the matrix equations for the correlation function, the on-line estimators of noise statistics are obtained. Furthermore, under the linear minimum variance optimal information fusion criterion weighted by matrices, a self-tuning information fusion Kalman smoother is presented. A new concept of the convergence in a realization is presented, and it is proved that the self-tuning Kalman fuser converges to the optimal Kalman fuser in a realization, so that it is asymptotically optimal. Compared with the single-sensor self-tuning Kalman smoother, its accuracy is improved. A simulation example for a target tracking system shows its effectiveness.