抖动样本中非随机信号系数的估计
On the Estimation of Nonrandom Signal Coefficients from Jittered Samples
摘要：
本文研究了由随机抖动（定时噪声）和加性iid高斯噪声破坏的样本估计带限信号的参数的问题，其中信号位于有限基的跨度中。针对经典估计问题，计算了Cramer-Rao下界，并提出了一种近似于极大似然估计的期望最大化算法.仿真研究了期望最大化算法的收敛特性，并与CRB和基本线性估计器的性能进行了比较。这些仿真表明，通过使用所提出的期望最大化算法对抖动样本进行后处理，可以容忍更大的抖动，从而有可能大幅降低片内ADC功耗。
英文摘要：
This paper examines the problem of estimating the parameters of a bandlimited signal from samples corrupted by random jitter (timing noise) and additive iid Gaussian noise, where the signal lies in the span of a finite basis. For the presented classical estimation problem, the Cramer-Rao lower bound (CRB) is computed, and an Expectation-Maximization (EM) algorithm approximating the maximum likelihood (ML) estimator is developed. Simulations are performed to study the convergence properties of the EM algorithm and compare the performance both against the CRB and a basic linear estimator. These simulations demonstrate that by post-processing the jittered samples with the proposed EM algorithm, greater jitter can be tolerated, potentially reducing on-chip ADC power consumption substantially.
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