摘要
用Kalman滤波方法,利用典范型分解对线性离散时不变广义随机系统提出了降阶Wiener状态平滑器,可明显减小计算负担,便于实时应用。一个仿真的例子说明了其有效性。
Using the Kalman filtering method, applying a decomposition in canonical form, a reduced-order Wiener state smoother is presented for linear discrete time-invariant descriptor stochastic systems, which can obviously reduces the computational burden and is suitable for real time applications. A simulation example shows its effectiveness.
出处
《科学技术与工程》
2003年第5期405-407,共3页
Science Technology and Engineering
基金
国家自然科学基金(69774019)
黑龙江省自然科学基金(F01-15)
