数字采样测量噪声误差的估计和分析

Estimation and analysis of noise error on digital sampling

针对高频数字采样中的附加噪声和时间抖动误差估计问题,给出了高频数字采样测量噪声误差的最大似然估计方法。在获得了相应的数学模型后,对最小二乘估计和最大似然估计方法进行了比较。仿真结果表明,对于此类误差最大似然估计比最小二乘估计有效性更好。另外最大似然估计的一个优点是用于不确定度计算的克拉美-罗下限值更易于获得。

This paper presented a ML estimator for the estimation of high-frequency sampling noise model to solve the problem that high-frequency sampling suffer from both additive noise and time jitter error estimation. After deriving the mathematical equations, a comparison between the LS and the ML estimators was performed. Simulation results show that the ML estimator produces more efficient than the LS estimator. In addition, it has the advantage that the Cramer-Rao lower bound can be used as an uncertainty bound on the estimation.