一类非均匀采样数据系统的状态估计

State Estimation for a Class of Non-uniformly Sampled-data Systems

首先利用提升技术推导出非均匀周期刷新和周期采样多率系统的提升状态空间模型;并基于卡尔曼滤波原理,通过极小代估计误差协方差矩阵,提出这类系统提升状态空间模型的状态估计算法。仿真试验说明,提出的算法可以有效地估计系统状态。

The lifting technique to derive the lifted state-space models for nonuniformly periodically updated is usesd and periodically sampled multirate systems. Based on the Kalman estimation principle, the state estimation algorithm of the lifted state-space models is derived, by minimizing the estimation error covariance matrix. Finally,an example is given to validate the algorithm proposed.