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中文摘要：
对于带多传感器的广义线性离散随机系统，应用奇异值分解，将其变换为等价的两个降阶多传感器子系统，提出了基于变换后的状态融合器构造原始状态融合器的新的融合方法。应用Kalman滤波方法，在线性最小方差按矩阵加权、按对角阵加权和按标量加权融合准则下，分别提出了三种最优加权融合降阶广义Kalman滤波器。可统一处理融合滤波、平滑和预报问题。可减少计算负担和改善局部滤波精度。证明了三种融合器和局部估值器之间的精度关系。为了计算最优加权。提出了局部滤波误差协方差阵的计算公式。一个Monte Carlo仿真例子说明了其有效性。
英文摘要：
For the linear discrete stochastic descriptor systems with multisensor, using me smgu, ar value decomposition, it is transformed into two reduced order subsystems, and a new fusion method of constructing the original state fuser based on the transformed state fuser is presented. Using Kalman filtering method, under the linear minimum variance optimal weighted criterion by matrices, diagonal matrices, and scalars, three optimal weighted fusion reduced order descriptor Kalman estimators are presented respectively. They can handle the fused filtering, smoothing, and prediction problems in a unified framework. They can reduce the computational burden, and can improve the local filtering accuracy. The accuracy relations among three fusers and local estimators are proved. In order to compute the optimal weights, the formula of computing the covariance matrices among local filtering errors is presented. A Monte Carlo simulation example shows its effectiveness.
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