标题: 广义静止讯号积分之最小变异估测器
Minimum Variance Estimator of the Integration of WSS Random Signals
作者: 刘佳荣
Jia-Rong Liu
李福进
Fu-Ching Lee
电控工程研究所
关键字: 随机积分;广义静止讯号;卡们滤波器;最小变异估测器;stochastic integration;WSS;Kalman Filter;Minimum Variance Estimator
公开日期: 2002
摘要: 本篇论文提出一个以卡门滤波器为基础的递回演算法来有效率地实现最小变异估测器。藉由数位取样所得到的点,来估计一个广义静止讯号的积分值。从均方的观点来看,当取样的点数趋近到无限多点的时候,估测的最小变异误差值也会跟者收敛到零。而随机积分对于陈述线性运算子方面,像摺合积分(出现在当一个随机讯号通过一个线性系统时候) ,是很重要的应用。因此我们可以把我们的演算法延伸套用到一个输入广义静止讯号的线性非时变系统的结果输出。
In this thesis, we propose a recursive algorithm based on Kalman Filter to implement efficiently the minimum variance estimator of the integration of WSS random signal by the discrete-time samples. As the number of samples trends to infinity, the minimum mean square errors will converge to zero in mean square sense. Stochastic integrals are important in applications for representing linear operator such as convolution, which arises when random processes are passed through linear systems. Thus, we can extend our results to the output of a linear time-invariant system with WSS random signal input.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT910591063
http://hdl.handle.net/11536/71041
显示于类别:Thesis