Self-tuning Information Fusion Kalman Predictor Weighted by Scalars

For the multisensor systems with unknown noise statistics, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the estimators of noise statistics are obtained, and under the linear minimum variance optimal information fusion criterion weighted by scalars, a self-tuning information fusion Kalman predictor weighted by scalars is presented. Its asymptotic optimality is proved, i.e. it converges to the optimal fused Kalman predictor in a realization. Its accuracy is higher than each local self-tuning Kalman predictor. Its algorithm is simple, and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness