广义离散随机线性系统降阶固定区间最优Kalman平滑器

Reduced -order fixed -interval optimal Kalman smoother for generalized discrete stochastic linear systems

利用广义系统典范型,将广义系统状态估计问题转化为一个降阶常规系统的状态估计问题。应用Kalman滤波方法和白噪声估计理论,提出了广义离散随机线性系统降阶固定区间最优Kalman平滑器,可减少计算负担,便于实时应用。一个仿真例子说明其有效性。

By a canonical form of descriptor systems, the state estimation problem of generalized systems is transformed into the state estimation problem for reduced - order conventional systems. Reduced-order fixed - interval optimal Kalman smoother for generalized discrete stochastic linear systems is proposed by applying Kalman filtering approach and white noise estimation theory. It can reduce the computational load and is suitable for real time applications. A simulation example shows its effectiveness.