两传感器信息融合超前k步稳态最优Kalman预报器

Two-sensor Information Fusion k-step-ahead Steady-state Optimal Kalman Predictor

应用Kalman滤波方法 ,基于Riccati方程 ,对于带相关噪声的系统 ,在线性最小方差融合准则下 ,提出了两传感器按矩阵加权信息融合超前k步稳态最优Kalman预报器 ,给出了最优加权阵和最小融合预报误差方差阵的具体计算公式。同单传感器情形相比 ,可提高预报器的精度。

Using Kalman filtering method, based on the Riccati equation, under the linear minimum variance information fusion criterion, the two-sensor information k \|step-ahead steady-state optimal Kalman predictor weighted by matrices is presented for systems with correlated noises, where the optimal weighting matrices ad minimum fused error variance matrix are given. Compared with the single sensor case, the accuracy of the predictor can be improved. A simulation example for a tracking system shows their effectiveness.