Self-tuning information fusion Kalmansmoother
自校正信息融合Kalman平滑器

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 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 has the asymptotic optimality.Compared with the single-sensor self-tuning Kalman smoother,its accuracy is improved.A simulation example for a target tracking system shows its effectiveness.