带不同局部动态模型的时变系统信息融合Kalman估值器

对于带不同局部动态模型和多传感器的的线性离散时变随机控制系统，应用Kalman滤波方法，基于Riccati方程，根据按矩阵加权、按对角阵加权和按标量加权三种最优融合规则．提出了系统公共状态的三种最优加权融合Kalman估值器，可统一处理融合滤波、预报和平滑问题。为计算最优加权，提出计算局部估计误差互协方差公式。它们可用于信号融合滤波。用增广状态方法．将待估信号看成子系统公共状态，提出了信号多传感器信息融合滤波的一种设计方法。

Information Fusion Kalman Estimators for Time-varying Systems with Different Local Dynamic Models

For linear discrete time-varying stochastic control systems with different local dynamic models, using the Kalman filtering method, based on the Riccati equations, according to three optimal fusion rules weighted by matrices, diagonal matrices, and scalars, the three optimal weighted fusion Kalman estimators are presented for the common state. They can handle the fused filtering, prediction, and smoothing problems in a unified framework. In order to compute the optimal weights, the formulas of computing the local estimation error cross-covariances are proposed. They can be applied to signal fused filtering. By the augmented state approach, the signal to be estimated can be viewed as a ammon state of the subsystemes, so that a design approach is presented of the muhisensor information fusion filtering for the time-varying signals.