Information fusion k-step-ahead steady-state optimal Kalman predictor and Wiener predictor
Zili Deng · Journal of Natural Science of Heilongjiang University · 2005
By the Kalman filtering method, based on Riccati equation, under the linear minimum variance information fusion criterion, the two-sensor information fusion k-step-ahead steady-state optimal Kalman predictor and Wiener predictor are presented, where the optimal weighting matrices and minimum fused error variance matrix are given. Compared to the single sensor case, the accuracy of predictors is improved. A simulation example for a radar tracking system shows its effectiveness.